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[ { "type": "text", "value": "As some of you know, I try to convert models to either fp32 or bf16 depending on theirs size before doing imatrix and quantization", "raw": "As some of you know, I try to convert models to either fp32 or bf16 depending on theirs size before doing imatrix and quantization", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Today I decided to see if that matters, and the results have me.. for lack of a better word, perplexed", "raw": "Today I decided to see if that matters, and the results have me.. for lack of a better word, perplexed", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "My setup:", "raw": "My setup:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Mistral Nemo Instruct 2407", "raw": "Mistral Nemo Instruct 2407", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- convert to FP32, calculate imatrix, quantize to Q8_0 and Q4_K_M", "raw": "- convert to FP32, calculate imatrix, quantize to Q8_0 and Q4_K_M", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- convert to FP16, calculate imatrix, quantize to Q8_0 and Q4_K_M", "raw": "- convert to FP16, calculate imatrix, quantize to Q8_0 and Q4_K_M", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I calculated the kld base from the FP32 model:", "raw": "I calculated the kld base from the FP32 model:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "inline_code", "value": null, "raw": "`./llama-perplexity -m /models/Mistral-Nemo-Instruct-2407-f32.gguf -f /training_data/wikitext-2-raw/wiki.test.raw --kl-divergence-base /training_data/mistral-nemo-f32.kld -ngl 35 -fa -sm row`", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": "./llama-perplexity -m /models/Mistral-Nemo-Instruct-2407-f32.gguf -f /training_data/wikitext-2-raw/wiki.test.raw --kl-divergence-base /training_data/mistral-nemo-f32.kld -ngl 35 -fa -sm row", "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "then calculated the divergence itself for each like so:", "raw": "then calculated the divergence itself for each like so:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "inline_code", "value": null, "raw": "`./llama-perplexity -m /models/Mistral-Nemo-Instruct-2407-Q8_0.gguf -f /training_data/wikitext-2-raw/wiki.test.raw --kl-divergence-base /training_data/mistral-nemo-f32.kld --kl-divergence -ngl 50 -fa -sm row`", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": "./llama-perplexity -m /models/Mistral-Nemo-Instruct-2407-Q8_0.gguf -f /training_data/wikitext-2-raw/wiki.test.raw --kl-divergence-base /training_data/mistral-nemo-f32.kld --kl-divergence -ngl 50 -fa -sm row", "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Q4_K_M from fp16 and fp32 were similar, trading blows across statistics, odd since i expected fp32 to be strictly better but it's not", "raw": "Q4_K_M from fp16 and fp32 were similar, trading blows across statistics, odd since i expected fp32 to be strictly better but it's not", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Q8_0 is where things get weird. Despite each file being slightly different size, and the sha256sum of course being different, they each get *completely identical* scores, down to 6 decimal places of precision on the statistics.", "raw": "Q8_0 is where things get weird. Despite each file being slightly different size, and the sha256sum of course being different, they each get *completely identical* scores, down to 6 decimal places of precision on the statistics.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "How is this possible? Is there something I don't understand about llama.cpp that makes it always convert to fp16 before it does quantization? Am I wasting time using FP32/BF16??", "raw": "How is this possible? Is there something I don't understand about llama.cpp that makes it always convert to fp16 before it does quantization? Am I wasting time using FP32/BF16??", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
As some of you know, I try to convert models to either fp32 or bf16 depending on theirs size before doing imatrix and quantization Today I decided to see if that matters, and the results have me.. for lack of a better word, perplexed My setup: Mistral Nemo Instruct 2407 - convert to FP32, calculate imatrix, quantize to Q8_0 and Q4_K_M - convert to FP16, calculate imatrix, quantize to Q8_0 and Q4_K_M I calculated the kld base from the FP32 model: `./llama-perplexity -m /models/Mistral-Nemo-Instruct-2407-f32.gguf -f /training_data/wikitext-2-raw/wiki.test.raw --kl-divergence-base /training_data/mistral-nemo-f32.kld -ngl 35 -fa -sm row` then calculated the divergence itself for each like so: `./llama-perplexity -m /models/Mistral-Nemo-Instruct-2407-Q8_0.gguf -f /training_data/wikitext-2-raw/wiki.test.raw --kl-divergence-base /training_data/mistral-nemo-f32.kld --kl-divergence -ngl 50 -fa -sm row` Q4_K_M from fp16 and fp32 were similar, trading blows across statistics, odd since i expected fp32 to be strictly better but it's not Q8_0 is where things get weird. Despite each file being slightly different size, and the sha256sum of course being different, they each get *completely identical* scores, down to 6 decimal places of precision on the statistics. How is this possible? Is there something I don't understand about llama.cpp that makes it always convert to fp16 before it does quantization? Am I wasting time using FP32/BF16??
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2024-08-15T14:21:20.000Z
2024-08-17T07:15:35.631Z
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/posts/bartowski/608656345183499
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574566841162028
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https://huggingface.co/fal/AuraFlow-v0.3 is now here with support for different aspect resolutions (w/h up to 1536px!) and much nicer aesthetics! Make sure to install the latest diffusers to get support for it.
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2024-08-15T14:15:17.000Z
2024-08-15T14:15:17.634Z
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/posts/isidentical/574566841162028
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[ { "type": "text", "value": "๐—ญ๐—ฒ๐—ฟ๐—ผ-๐—บ๐—ฎ๐˜๐—ต ๐—ถ๐—ป๐˜๐—ฟ๐—ผ ๐˜๐—ผ ๐—”๐—œ ๐—ต๐—ถ๐˜€๐˜๐—ผ๐—ฟ๐˜†: ๐—ณ๐—ฟ๐—ผ๐—บ ๐˜๐—ต๐—ฒ ๐Ÿญ๐Ÿต๐Ÿฑ๐Ÿฌ๐˜€ ๐˜๐—ผ ๐˜๐—ผ๐—ฑ๐—ฎ๐˜†'๐˜€ ๐—Ÿ๐—Ÿ๐— ๐˜€ ๐Ÿ“–", "raw": "๐—ญ๐—ฒ๐—ฟ๐—ผ-๐—บ๐—ฎ๐˜๐—ต ๐—ถ๐—ป๐˜๐—ฟ๐—ผ ๐˜๐—ผ ๐—”๐—œ ๐—ต๐—ถ๐˜€๐˜๐—ผ๐—ฟ๐˜†: ๐—ณ๐—ฟ๐—ผ๐—บ ๐˜๐—ต๐—ฒ ๐Ÿญ๐Ÿต๐Ÿฑ๐Ÿฌ๐˜€ ๐˜๐—ผ ๐˜๐—ผ๐—ฑ๐—ฎ๐˜†'๐˜€ ๐—Ÿ๐—Ÿ๐— ๐˜€ ๐Ÿ“–", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I wanted to structure my thinking about LLMs by going through their history since the 50s. This history is captivating, with the opposition between Connexionists (Rosenblatt, LeCun) and Symbolists, the first victories of \"deep\" neural networks, the revolution of Attention...", "raw": "I wanted to structure my thinking about LLMs by going through their history since the 50s. This history is captivating, with the opposition between Connexionists (Rosenblatt, LeCun) and Symbolists, the first victories of \"deep\" neural networks, the revolution of Attention...", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "So I might have gone a bit too far! ๐Ÿ˜…", "raw": "So I might have gone a bit too far! ๐Ÿ˜…", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“ I've made a long post summarizing the main stages of building LLMs: neural networks, optimization, backpropagation, attention layers...", "raw": "๐Ÿ“ I've made a long post summarizing the main stages of building LLMs: neural networks, optimization, backpropagation, attention layers...", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โœ… And I've made sure to keep it 100% horrible-latex-math-free: the technical stuff is conveyed in graphs only, so it should be accessible to really anyone, even your grandfather (I'm sending it to mine right now).", "raw": "โœ… And I've made sure to keep it 100% horrible-latex-math-free: the technical stuff is conveyed in graphs only, so it should be accessible to really anyone, even your grandfather (I'm sending it to mine right now).", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Read it here in english ๐Ÿ‘‰ ", "raw": "Read it here in english ๐Ÿ‘‰ ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://aymeric-roucher.github.io/brief-history-of-ai/", "resource": null, "url": null, "href": "https://aymeric-roucher.github.io/brief-history-of-ai/", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Pour le post en franรงais ๐Ÿ‘‰ ", "raw": "Pour le post en franรงais ๐Ÿ‘‰ ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://aymeric-roucher.github.io/breve-histoire-de-l-ia/", "resource": null, "url": null, "href": "https://aymeric-roucher.github.io/breve-histoire-de-l-ia/", "user": null, "lang": null, "code": null, "label": null } ]
๐—ญ๐—ฒ๐—ฟ๐—ผ-๐—บ๐—ฎ๐˜๐—ต ๐—ถ๐—ป๐˜๐—ฟ๐—ผ ๐˜๐—ผ ๐—”๐—œ ๐—ต๐—ถ๐˜€๐˜๐—ผ๐—ฟ๐˜†: ๐—ณ๐—ฟ๐—ผ๐—บ ๐˜๐—ต๐—ฒ ๐Ÿญ๐Ÿต๐Ÿฑ๐Ÿฌ๐˜€ ๐˜๐—ผ ๐˜๐—ผ๐—ฑ๐—ฎ๐˜†'๐˜€ ๐—Ÿ๐—Ÿ๐— ๐˜€ ๐Ÿ“– I wanted to structure my thinking about LLMs by going through their history since the 50s. This history is captivating, with the opposition between Connexionists (Rosenblatt, LeCun) and Symbolists, the first victories of "deep" neural networks, the revolution of Attention... So I might have gone a bit too far! ๐Ÿ˜… ๐Ÿ“ I've made a long post summarizing the main stages of building LLMs: neural networks, optimization, backpropagation, attention layers... โœ… And I've made sure to keep it 100% horrible-latex-math-free: the technical stuff is conveyed in graphs only, so it should be accessible to really anyone, even your grandfather (I'm sending it to mine right now). Read it here in english ๐Ÿ‘‰ https://aymeric-roucher.github.io/brief-history-of-ai/ Pour le post en franรงais ๐Ÿ‘‰ https://aymeric-roucher.github.io/breve-histoire-de-l-ia/
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2024-08-15T13:59:58.000Z
2024-08-15T13:59:58.470Z
[]
/posts/m-ric/541848107798204
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[ { "type": "text", "value": "Some personal and professional news โœจ", "raw": "Some personal and professional news โœจ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I'm writing a book on ML metrics.", "raw": "I'm writing a book on ML metrics.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Together with Wojtek Kuberski, weโ€™re creating the missing piece of every ML university program and online course: a book solely dedicated to Machine Learning metrics!", "raw": "Together with Wojtek Kuberski, weโ€™re creating the missing piece of every ML university program and online course: a book solely dedicated to Machine Learning metrics!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The book will cover the following types of metrics:", "raw": "The book will cover the following types of metrics:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โ€ข Regression", "raw": "โ€ข Regression", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โ€ข Classification", "raw": "โ€ข Classification", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โ€ข Clustering", "raw": "โ€ข Clustering", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โ€ข Ranking", "raw": "โ€ข Ranking", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โ€ข Vision", "raw": "โ€ข Vision", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โ€ข Text", "raw": "โ€ข Text", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โ€ข GenAI", "raw": "โ€ข GenAI", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โ€ข Bias and Fairness", "raw": "โ€ข Bias and Fairness", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ‘‰ check out the book: ", "raw": "๐Ÿ‘‰ check out the book: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://www.nannyml.com/metrics", "resource": null, "url": null, "href": "https://www.nannyml.com/metrics", "user": null, "lang": null, "code": null, "label": null } ]
Some personal and professional news โœจ I'm writing a book on ML metrics. Together with Wojtek Kuberski, weโ€™re creating the missing piece of every ML university program and online course: a book solely dedicated to Machine Learning metrics! The book will cover the following types of metrics: โ€ข Regression โ€ข Classification โ€ข Clustering โ€ข Ranking โ€ข Vision โ€ข Text โ€ข GenAI โ€ข Bias and Fairness ๐Ÿ‘‰ check out the book: https://www.nannyml.com/metrics
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2024-08-15T13:04:54.000Z
2024-08-15T14:41:01.073Z
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/posts/santiviquez/420890568934318
466
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525100587174350
[ { "type": "text", "value": "Everchanging Quest is out !", "raw": "Everchanging Quest is out !", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "It is an LLM controlled Rogue-Like in which the LLM gets a markdown representation of the map, and should generate a JSON with the objective to fulfill on the map as well as the necessary objects and their placements.", "raw": "It is an LLM controlled Rogue-Like in which the LLM gets a markdown representation of the map, and should generate a JSON with the objective to fulfill on the map as well as the necessary objects and their placements.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Come test it on the space :", "raw": "Come test it on the space :", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Jofthomas/Everchanging-Quest", "resource": { "type": "space", "id": "Jofthomas/Everchanging-Quest", "discussionNum": null }, "url": "https://huggingface.co/spaces/Jofthomas/Everchanging-Quest", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Everchanging Quest is out ! It is an LLM controlled Rogue-Like in which the LLM gets a markdown representation of the map, and should generate a JSON with the objective to fulfill on the map as well as the necessary objects and their placements. Come test it on the space : https://huggingface.co/spaces/Jofthomas/Everchanging-Quest
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2024-08-15T09:32:15.000Z
2024-09-04T20:40:33.377Z
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/posts/Jofthomas/525100587174350
3,206
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240020635038154
[ { "type": "text", "value": "ehm, so today i've finished my new project", "raw": "ehm, so today i've finished my new project", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Blane187/animalese-py", "resource": { "type": "space", "id": "Blane187/animalese-py", "discussionNum": null }, "url": "https://huggingface.co/spaces/Blane187/animalese-py", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "or you can make your voice to animalese with it:", "raw": "or you can make your voice to animalese with it:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Blane187/animalese_RVC", "resource": { "type": "space", "id": "Blane187/animalese_RVC", "discussionNum": null }, "url": "https://huggingface.co/spaces/Blane187/animalese_RVC", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "i'm just bored, so i make the project, lol", "raw": "i'm just bored, so i make the project, lol", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
ehm, so today i've finished my new project https://huggingface.co/spaces/Blane187/animalese-py or you can make your voice to animalese with it: https://huggingface.co/spaces/Blane187/animalese_RVC i'm just bored, so i make the project, lol
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2024-08-15T09:06:34.000Z
2024-08-15T09:06:34.270Z
[]
/posts/Blane187/240020635038154
936
0
575318853216493
[ { "type": "text", "value": "๐Ÿ“ฃ Introducing Dataset Viber: your chill repo for data collection, annotation and vibe checks! ๐ŸŽ‰", "raw": "๐Ÿ“ฃ Introducing Dataset Viber: your chill repo for data collection, annotation and vibe checks! ๐ŸŽ‰", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I've cooked up Dataset Viber, a set of cool tools designed to make data preparation for AI models easier, more approachable and enjoyable for standalone AI engineers and enthusiasts.", "raw": "I've cooked up Dataset Viber, a set of cool tools designed to make data preparation for AI models easier, more approachable and enjoyable for standalone AI engineers and enthusiasts.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ”ง What Dataset Viber offers:", "raw": "๐Ÿ”ง What Dataset Viber offers:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- CollectorInterface: Lazily collect model interaction data without human annotation", "raw": "- CollectorInterface: Lazily collect model interaction data without human annotation", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- AnnotatorInterface: Annotate your data with models in the loop", "raw": "- AnnotatorInterface: Annotate your data with models in the loop", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- BulkInterface: Explore data distribution and annotate in bulk", "raw": "- BulkInterface: Explore data distribution and annotate in bulk", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Embedder: Efficiently embed data with ONNX-optimized speeds", "raw": "- Embedder: Efficiently embed data with ONNX-optimized speeds", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŽฏ Key features:", "raw": "๐ŸŽฏ Key features:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Supports various tasks for text, chat, and image modalities", "raw": "- Supports various tasks for text, chat, and image modalities", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Runs in .ipynb notebooks", "raw": "- Runs in .ipynb notebooks", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Logs data to local CSV or directly to Hugging Face Hub", "raw": "- Logs data to local CSV or directly to Hugging Face Hub", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Easy to install via pip: ", "raw": "- Easy to install via pip: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "inline_code", "value": null, "raw": "`pip install dataset-viber`", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": "pip install dataset-viber", "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "It's not designed for team collaboration or production use, but rather as a fun and efficient toolkit for individual projects.", "raw": "It's not designed for team collaboration or production use, but rather as a fun and efficient toolkit for individual projects.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Want to give it a try? Check out the repository link ", "raw": "Want to give it a try? Check out the repository link ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/davidberenstein1957/dataset-viber/", "resource": null, "url": null, "href": "https://github.com/davidberenstein1957/dataset-viber/", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ".", "raw": ".", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I'm excited to hear your feedback and learn how you vibe with your data. Feel free to open an issue or reach out if you have any questions or suggestions!", "raw": "I'm excited to hear your feedback and learn how you vibe with your data. Feel free to open an issue or reach out if you have any questions or suggestions!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Some shoutouts:", "raw": "Some shoutouts:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Gradio for the amazing backbone", "raw": "- Gradio for the amazing backbone", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Daniel van Strien for some initial presentations I did on vibe checks", "raw": "- Daniel van Strien for some initial presentations I did on vibe checks", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Emily Omier for the workshop on structuring GitHub repo READMEs", "raw": "- Emily Omier for the workshop on structuring GitHub repo READMEs", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Hamel Husain for keeping mentioning that people should look at their data.", "raw": "- Hamel Husain for keeping mentioning that people should look at their data.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Philipp Schmid for his code for ONNX feature-extractors", "raw": "- Philipp Schmid for his code for ONNX feature-extractors", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Ben Burtenshaw for the first PR", "raw": "- Ben Burtenshaw for the first PR", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿ“ฃ Introducing Dataset Viber: your chill repo for data collection, annotation and vibe checks! ๐ŸŽ‰ I've cooked up Dataset Viber, a set of cool tools designed to make data preparation for AI models easier, more approachable and enjoyable for standalone AI engineers and enthusiasts. ๐Ÿ”ง What Dataset Viber offers: - CollectorInterface: Lazily collect model interaction data without human annotation - AnnotatorInterface: Annotate your data with models in the loop - BulkInterface: Explore data distribution and annotate in bulk - Embedder: Efficiently embed data with ONNX-optimized speeds ๐ŸŽฏ Key features: - Supports various tasks for text, chat, and image modalities - Runs in .ipynb notebooks - Logs data to local CSV or directly to Hugging Face Hub - Easy to install via pip: `pip install dataset-viber` It's not designed for team collaboration or production use, but rather as a fun and efficient toolkit for individual projects. Want to give it a try? Check out the repository link https://github.com/davidberenstein1957/dataset-viber/. I'm excited to hear your feedback and learn how you vibe with your data. Feel free to open an issue or reach out if you have any questions or suggestions! Some shoutouts: - Gradio for the amazing backbone - Daniel van Strien for some initial presentations I did on vibe checks - Emily Omier for the workshop on structuring GitHub repo READMEs - Hamel Husain for keeping mentioning that people should look at their data. - Philipp Schmid for his code for ONNX feature-extractors - Ben Burtenshaw for the first PR
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2024-08-15T07:54:24.000Z
2024-08-15T12:28:33.093Z
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/posts/davidberenstein1957/575318853216493
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Here is a hackable and minimal implementation showing how to perform distributed text-to-image generation with Diffusers and Accelerate. Full snippet is here: https://gist.github.com/sayakpaul/cfaebd221820d7b43fae638b4dfa01ba With @JW17
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2024-08-15T06:53:12.000Z
2024-08-15T06:53:12.559Z
[]
/posts/sayakpaul/610534902245547
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[ { "type": "text", "value": "hey everyone, if you have any experience with fine tuning ai models please feel free to reach out to me :)", "raw": "hey everyone, if you have any experience with fine tuning ai models please feel free to reach out to me :)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "We're looking for devs to fine tune an AI model for a snapchat chatting AI project. Open to either part time or full time work.", "raw": "We're looking for devs to fine tune an AI model for a snapchat chatting AI project. Open to either part time or full time work.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Email: [email protected]", "raw": "Email: [email protected]", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Telegram: Kirann46", "raw": "Telegram: Kirann46", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
hey everyone, if you have any experience with fine tuning ai models please feel free to reach out to me :) We're looking for devs to fine tune an AI model for a snapchat chatting AI project. Open to either part time or full time work. Email: [email protected] Telegram: Kirann46
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2024-08-15T02:04:31.000Z
2024-08-15T02:04:31.793Z
[]
/posts/risestud/343123848597732
463
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[ { "type": "text", "value": "๐Ÿš€ Introducing ChemVLM, the first open-source multimodal large language model dedicated to chemistry!", "raw": "๐Ÿš€ Introducing ChemVLM, the first open-source multimodal large language model dedicated to chemistry!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŒŸComparable performances with commercial models or specific OCR model but with dialogue capabilities!", "raw": "๐ŸŒŸComparable performances with commercial models or specific OCR model but with dialogue capabilities!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โœจ2B/26B Models Here! ", "raw": "โœจ2B/26B Models Here! ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/AI4Chem/ChemVLM-26B", "resource": { "type": "model", "id": "AI4Chem/ChemVLM-26B", "discussionNum": null }, "url": "https://huggingface.co/AI4Chem/ChemVLM-26B", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2408.07246", "resource": { "type": "paper", "id": "2408.07246", "discussionNum": null }, "url": "https://huggingface.co/papers/2408.07246", "href": null, "user": null, "lang": null, "code": null, "label": "Seeing and Understanding: Bridging Vision with Chemical Knowledge Via\n ChemVLM (2408.07246)" } ]
๐Ÿš€ Introducing ChemVLM, the first open-source multimodal large language model dedicated to chemistry! ๐ŸŒŸComparable performances with commercial models or specific OCR model but with dialogue capabilities! โœจ2B/26B Models Here! https://huggingface.co/AI4Chem/ChemVLM-26B https://huggingface.co/papers/2408.07246
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2024-08-15T00:44:40.000Z
2024-08-17T00:19:30.635Z
[]
/posts/qq8933/310082664847975
1,533
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[ { "type": "text", "value": "๐Ÿ“ธPhoto LoRA Drop๐Ÿ“ธ", "raw": "๐Ÿ“ธPhoto LoRA Drop๐Ÿ“ธ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I've been working on this one for a few days, but really I've had this dataset for a few years! I collected a bunch of open access photos online back in late 2022, but I was never happy enough with how they played with the base model!", "raw": "I've been working on this one for a few days, but really I've had this dataset for a few years! I collected a bunch of open access photos online back in late 2022, but I was never happy enough with how they played with the base model!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I am so thrilled that they look so nice with Flux!", "raw": "I am so thrilled that they look so nice with Flux!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This for me is a version one of this model - I still see room for improvement and possibly expansion of it's 40 image dataset. For those who are curious:", "raw": "This for me is a version one of this model - I still see room for improvement and possibly expansion of it's 40 image dataset. For those who are curious:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "40 Image", "raw": "40 Image", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "3200 Steps", "raw": "3200 Steps", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Dim 32", "raw": "Dim 32", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "3e-4", "raw": "3e-4", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Enjoy! Create! Big thank you to Glif for sponsoring the model creation! :D", "raw": "Enjoy! Create! Big thank you to Glif for sponsoring the model creation! :D", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/alvdansen/flux_film_foto", "resource": { "type": "model", "id": "alvdansen/flux_film_foto", "discussionNum": null }, "url": "https://huggingface.co/alvdansen/flux_film_foto", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿ“ธPhoto LoRA Drop๐Ÿ“ธ I've been working on this one for a few days, but really I've had this dataset for a few years! I collected a bunch of open access photos online back in late 2022, but I was never happy enough with how they played with the base model! I am so thrilled that they look so nice with Flux! This for me is a version one of this model - I still see room for improvement and possibly expansion of it's 40 image dataset. For those who are curious: 40 Image 3200 Steps Dim 32 3e-4 Enjoy! Create! Big thank you to Glif for sponsoring the model creation! :D https://huggingface.co/alvdansen/flux_film_foto
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2024-08-14T21:51:00.000Z
2024-08-14T21:51:00.882Z
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/posts/alvdansen/960702751544007
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[ { "type": "text", "value": "AuraSR Giga Upscaler V1 by SECourses - Upscales to 4x", "raw": "AuraSR Giga Upscaler V1 by SECourses - Upscales to 4x", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "AuraSR is a 600M parameter upsampler model derived from the GigaGAN paper. It works super fast and uses a very limited VRAM below 5 GB. It is deterministic upscaler. It works perfect in some images but fails in some images so it is worth to give it a shot.", "raw": "AuraSR is a 600M parameter upsampler model derived from the GigaGAN paper. It works super fast and uses a very limited VRAM below 5 GB. It is deterministic upscaler. It works perfect in some images but fails in some images so it is worth to give it a shot.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "GitHub official repo : ", "raw": "GitHub official repo : ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/fal-ai/aura-sr", "resource": null, "url": null, "href": "https://github.com/fal-ai/aura-sr", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I have developed 1-click installers and a batch upscaler App.", "raw": "I have developed 1-click installers and a batch upscaler App.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "You can download installers and advanced batch App from below link:", "raw": "You can download installers and advanced batch App from below link:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://www.patreon.com/posts/110060645", "resource": null, "url": null, "href": "https://www.patreon.com/posts/110060645", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Check the screenshots and examples below", "raw": "Check the screenshots and examples below", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Windows Requirements", "raw": "Windows Requirements", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Python 3.10, FFmpeg, Cuda 11.8, C++ tools and Git", "raw": "Python 3.10, FFmpeg, Cuda 11.8, C++ tools and Git", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "If it doesn't work make sure to below tutorial and install everything exactly as shown in this below tutorial", "raw": "If it doesn't work make sure to below tutorial and install everything exactly as shown in this below tutorial", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://youtu.be/-NjNy7afOQ0", "resource": null, "url": null, "href": "https://youtu.be/-NjNy7afOQ0", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "How to Install and Use on Windows", "raw": "How to Install and Use on Windows", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Extract the attached GigaGAN_Upscaler_v1.zip into a folder like c:/giga_upscale", "raw": "Extract the attached GigaGAN_Upscaler_v1.zip into a folder like c:/giga_upscale", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Then double click and install with Windows_Install.bat file", "raw": "Then double click and install with Windows_Install.bat file", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "It will generate an isolated virtual environment venv folder and install requirements", "raw": "It will generate an isolated virtual environment venv folder and install requirements", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Then double click and start the Gradio App with Windows_Start_App.bat file", "raw": "Then double click and start the Gradio App with Windows_Start_App.bat file", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "When first time running it will download models into your Hugging Face cache folder", "raw": "When first time running it will download models into your Hugging Face cache folder", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Hugging Face cache folder setup explained below", "raw": "Hugging Face cache folder setup explained below", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://www.patreon.com/posts/108419878", "resource": null, "url": null, "href": "https://www.patreon.com/posts/108419878", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "All upscaled images will be saved into outputs folder automatically with same name and plus numbering if necessary", "raw": "All upscaled images will be saved into outputs folder automatically with same name and plus numbering if necessary", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "You can also batch upscale a folder", "raw": "You can also batch upscale a folder", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "How to Install and Use on Cloud", "raw": "How to Install and Use on Cloud", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Follow Massed Compute and RunPod instructions", "raw": "Follow Massed Compute and RunPod instructions", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Usage is same as on Windows", "raw": "Usage is same as on Windows", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "For Kaggle start a Kaggle notebook, import our Kaggle notebook and follow the instructions", "raw": "For Kaggle start a Kaggle notebook, import our Kaggle notebook and follow the instructions", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "App Screenshots and Examples below", "raw": "App Screenshots and Examples below", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": 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AuraSR Giga Upscaler V1 by SECourses - Upscales to 4x AuraSR is a 600M parameter upsampler model derived from the GigaGAN paper. It works super fast and uses a very limited VRAM below 5 GB. It is deterministic upscaler. It works perfect in some images but fails in some images so it is worth to give it a shot. GitHub official repo : https://github.com/fal-ai/aura-sr I have developed 1-click installers and a batch upscaler App. You can download installers and advanced batch App from below link: https://www.patreon.com/posts/110060645 Check the screenshots and examples below Windows Requirements Python 3.10, FFmpeg, Cuda 11.8, C++ tools and Git If it doesn't work make sure to below tutorial and install everything exactly as shown in this below tutorial https://youtu.be/-NjNy7afOQ0 How to Install and Use on Windows Extract the attached GigaGAN_Upscaler_v1.zip into a folder like c:/giga_upscale Then double click and install with Windows_Install.bat file It will generate an isolated virtual environment venv folder and install requirements Then double click and start the Gradio App with Windows_Start_App.bat file When first time running it will download models into your Hugging Face cache folder Hugging Face cache folder setup explained below https://www.patreon.com/posts/108419878 All upscaled images will be saved into outputs folder automatically with same name and plus numbering if necessary You can also batch upscale a folder How to Install and Use on Cloud Follow Massed Compute and RunPod instructions Usage is same as on Windows For Kaggle start a Kaggle notebook, import our Kaggle notebook and follow the instructions App Screenshots and Examples below
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2024-08-14T21:03:57.000Z
2024-08-14T21:03:57.471Z
[]
/posts/MonsterMMORPG/257199579452399
1,584
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[ { "type": "text", "value": "Introducing Fineweb-Edu-Fortified: An enhanced Fineweb-Edu dataset. ๐Ÿ“š", "raw": "Introducing Fineweb-Edu-Fortified: An enhanced Fineweb-Edu dataset. ๐Ÿ“š", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This dataset is tailored for NLP tasks and helps streamline model training by offering a more refined, unique dataset. Perfect for startups and researchers looking for high-quality educational content to train, evaluate, or fine-tune AI models. The dataset is based on the Fineweb-Edu subset of the large Fineweb dataset and includes:", "raw": "This dataset is tailored for NLP tasks and helps streamline model training by offering a more refined, unique dataset. Perfect for startups and researchers looking for high-quality educational content to train, evaluate, or fine-tune AI models. The dataset is based on the Fineweb-Edu subset of the large Fineweb dataset and includes:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Exact-match deduplication across all crawls", "raw": "- Exact-match deduplication across all crawls", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Embeddings for each row using the TaylorAI/bge-micro model", "raw": "- Embeddings for each row using the TaylorAI/bge-micro model", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Count column indicating duplication frequency", "raw": "- Count column indicating duplication frequency", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Includes data from 95 Common Crawl crawls (2013-2024)", "raw": "- Includes data from 95 Common Crawl crawls (2013-2024)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Rows have been reduced from 1.279B to 0.324B after deduplication", "raw": "- Rows have been reduced from 1.279B to 0.324B after deduplication", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- It is comprised of ~375B tokens (down from 1,320B in Fineweb-Edu)", "raw": "- It is comprised of ~375B tokens (down from 1,320B in Fineweb-Edu)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Access the entire Fineweb-Edu-Fortified dataset on Hugging Face โ†’ ", "raw": "Access the entire Fineweb-Edu-Fortified dataset on Hugging Face โ†’ ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/airtrain-ai/fineweb-edu-fortified", "resource": { "type": "dataset", "id": "airtrain-ai/fineweb-edu-fortified", "discussionNum": null }, "url": "https://huggingface.co/datasets/airtrain-ai/fineweb-edu-fortified", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ", "raw": " ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Try a semantic search demo via this Hugging Face Space โ†’ ", "raw": "Try a semantic search demo via this Hugging Face Space โ†’ ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/airtrain-ai/fineweb-edu-fortified-search-demo", "resource": { "type": "space", "id": "airtrain-ai/fineweb-edu-fortified-search-demo", "discussionNum": null }, "url": "https://huggingface.co/spaces/airtrain-ai/fineweb-edu-fortified-search-demo", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Many thanks to the amazing ", "raw": "Many thanks to the amazing ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@josh-sematic", "resource": null, "url": null, "href": null, "user": "josh-sematic", "lang": null, "code": null, "label": null }, { "type": "text", "value": " for his work on this project, the Fineweb/Fineweb-Edu team at Hugging Face for producing the original datasets and for their support during our work on Fineweb-Edu-Fortified, and also thanks toย ", "raw": " for his work on this project, the Fineweb/Fineweb-Edu team at Hugging Face for producing the original datasets and for their support during our work on Fineweb-Edu-Fortified, and also thanks toย ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@underspirit", "resource": null, "url": null, "href": null, "user": "underspirit", "lang": null, "code": null, "label": null }, { "type": "text", "value": " forย pointing outย the reduction in dataset size that could be achieved via deduplication. ๐Ÿค—", "raw": " forย pointing outย the reduction in dataset size that could be achieved via deduplication. ๐Ÿค—", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Introducing Fineweb-Edu-Fortified: An enhanced Fineweb-Edu dataset. ๐Ÿ“š This dataset is tailored for NLP tasks and helps streamline model training by offering a more refined, unique dataset. Perfect for startups and researchers looking for high-quality educational content to train, evaluate, or fine-tune AI models. The dataset is based on the Fineweb-Edu subset of the large Fineweb dataset and includes: - Exact-match deduplication across all crawls - Embeddings for each row using the TaylorAI/bge-micro model - Count column indicating duplication frequency - Includes data from 95 Common Crawl crawls (2013-2024) - Rows have been reduced from 1.279B to 0.324B after deduplication - It is comprised of ~375B tokens (down from 1,320B in Fineweb-Edu) Access the entire Fineweb-Edu-Fortified dataset on Hugging Face โ†’ https://huggingface.co/datasets/airtrain-ai/fineweb-edu-fortified Try a semantic search demo via this Hugging Face Space โ†’ https://huggingface.co/spaces/airtrain-ai/fineweb-edu-fortified-search-demo Many thanks to the amazing @josh-sematic for his work on this project, the Fineweb/Fineweb-Edu team at Hugging Face for producing the original datasets and for their support during our work on Fineweb-Edu-Fortified, and also thanks toย @underspirit forย pointing outย the reduction in dataset size that could be achieved via deduplication. ๐Ÿค—
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2024-08-14T18:45:05.000Z
2024-08-14T18:45:05.366Z
[]
/posts/joylarkin/266261281839963
3,003
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745512209067729
[ { "type": "text", "value": "Letโ€™s see JEPA in action๐Ÿค–", "raw": "Letโ€™s see JEPA in action๐Ÿค–", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Simplified image-based implementation training on a CPU with live preview support - very satisfying to watch:)", "raw": "Simplified image-based implementation training on a CPU with live preview support - very satisfying to watch:)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I-JEPA is the image-based version of JEPA (Joint-Embedding Predictive Architecture - an alternative to autoregressive LLM architectures ) pioneered by professor Yann Lecun.", "raw": "I-JEPA is the image-based version of JEPA (Joint-Embedding Predictive Architecture - an alternative to autoregressive LLM architectures ) pioneered by professor Yann Lecun.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "At a higher level, I-JEPA predicts image segment representations (Target) based on representations of other segments within the same image (Context). It consists of three key components: a context encoder, target encoder and a predictor.", "raw": "At a higher level, I-JEPA predicts image segment representations (Target) based on representations of other segments within the same image (Context). It consists of three key components: a context encoder, target encoder and a predictor.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Code: ", "raw": "Code: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/Jaykef/ai-algorithms/blob/main/mnist_ijepa.ipynb", "resource": null, "url": null, "href": "https://github.com/Jaykef/ai-algorithms/blob/main/mnist_ijepa.ipynb", "user": null, "lang": null, "code": null, "label": null } ]
Letโ€™s see JEPA in action๐Ÿค– Simplified image-based implementation training on a CPU with live preview support - very satisfying to watch:) I-JEPA is the image-based version of JEPA (Joint-Embedding Predictive Architecture - an alternative to autoregressive LLM architectures ) pioneered by professor Yann Lecun. At a higher level, I-JEPA predicts image segment representations (Target) based on representations of other segments within the same image (Context). It consists of three key components: a context encoder, target encoder and a predictor. Code: https://github.com/Jaykef/ai-algorithms/blob/main/mnist_ijepa.ipynb
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2024-08-14T13:36:56.000Z
2024-08-15T00:35:12.980Z
[]
/posts/Jaward/745512209067729
1,489
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179021331327055
[ { "type": "text", "value": "If you are interested in deep reinforcement learning, find my recent survey below:", "raw": "If you are interested in deep reinforcement learning, find my recent survey below:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "A Survey Analyzing Generalization in Deep Reinforcement Learning", "raw": "A Survey Analyzing Generalization in Deep Reinforcement Learning", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Paper: ", "raw": "Paper: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/pdf/2401.02349", "resource": null, "url": null, "href": "https://arxiv.org/pdf/2401.02349", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "GitHub: ", "raw": "GitHub: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/EzgiKorkmaz/generalization-reinforcement-learning", "resource": null, "url": null, "href": "https://github.com/EzgiKorkmaz/generalization-reinforcement-learning", "user": null, "lang": null, "code": null, "label": null } ]
If you are interested in deep reinforcement learning, find my recent survey below: A Survey Analyzing Generalization in Deep Reinforcement Learning Paper: https://arxiv.org/pdf/2401.02349 GitHub: https://github.com/EzgiKorkmaz/generalization-reinforcement-learning
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2024-08-14T09:27:06.000Z
2024-08-14T09:27:06.576Z
[]
/posts/ezgikorkmaz/179021331327055
1,269
0
593632232120320
[ { "type": "text", "value": "It took Googleโ€™s Transformer model from 2017 a whopping $900 to train. ๐Ÿ’ธ", "raw": "It took Googleโ€™s Transformer model from 2017 a whopping $900 to train. ๐Ÿ’ธ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This in contrast to the $191 million Google spent on Gemini Ultra sounds like a bargain! ๐Ÿ’ฐ", "raw": "This in contrast to the $191 million Google spent on Gemini Ultra sounds like a bargain! ๐Ÿ’ฐ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Gemini Ultra required 50 billion petaFLOPS (one petaFLOP equals one quadrillion FLOPs). ๐Ÿค– ", "raw": "Gemini Ultra required 50 billion petaFLOPS (one petaFLOP equals one quadrillion FLOPs). ๐Ÿค– ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Compared to OpenAIโ€™s GPT-4, which required 21 billion petaFLOPS, at a cost of $78 million. ๐Ÿ’ก", "raw": "Compared to OpenAIโ€™s GPT-4, which required 21 billion petaFLOPS, at a cost of $78 million. ๐Ÿ’ก", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "2017: Original Transformer Model: $930 [@Google ] ๐Ÿ’ป ", "raw": "2017: Original Transformer Model: $930 [@Google ] ๐Ÿ’ป ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "2018: BERT-Large: $3,288 [@Google] ๐Ÿ“š ", "raw": "2018: BERT-Large: $3,288 [@Google] ๐Ÿ“š ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "2019: RoBERTa Large: 160k [@Meta] ๐ŸŒ ", "raw": "2019: RoBERTa Large: 160k [@Meta] ๐ŸŒ ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "2020: GPT-3(175B): $4.32M [@OpenAI] ๐Ÿง  ", "raw": "2020: GPT-3(175B): $4.32M [@OpenAI] ๐Ÿง  ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "2023: Llama 2 70B: $3.93M [@Meta] ๐Ÿ‘ ", "raw": "2023: Llama 2 70B: $3.93M [@Meta] ๐Ÿ‘ ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "2023: GPT-4: $78.35M [@OpenAI] ๐ŸŒŸ ", "raw": "2023: GPT-4: $78.35M [@OpenAI] ๐ŸŒŸ ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Now, Gemini Ultra: $191.4M [@Google] ๐Ÿš€ ", "raw": "Now, Gemini Ultra: $191.4M [@Google] ๐Ÿš€ ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This forms an exponential curve! ๐Ÿคฏ", "raw": "This forms an exponential curve! ๐Ÿคฏ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "But, why? ๐Ÿค” ", "raw": "But, why? ๐Ÿค” ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Compute, data, and expertise. All three come at a great cost! โš™๏ธ๐Ÿ“Š๐Ÿ’ก", "raw": "Compute, data, and expertise. All three come at a great cost! โš™๏ธ๐Ÿ“Š๐Ÿ’ก", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Google recently made Gemini-1.5-Flash fine-tuning free, as it's almost impossible for regular businesses to justify an in-house trained foundational model! ๐Ÿ†“", "raw": "Google recently made Gemini-1.5-Flash fine-tuning free, as it's almost impossible for regular businesses to justify an in-house trained foundational model! ๐Ÿ†“", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This barrier of cost is going to result in fewer new foundational ", "raw": "This barrier of cost is going to result in fewer new foundational ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "models/less", "raw": "models/less", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " competition and more fine-tunes! ๐Ÿ“‰๐Ÿ”„", "raw": " competition and more fine-tunes! ๐Ÿ“‰๐Ÿ”„", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Data [Stanford Universityโ€™s 2024 AI Index Report]: ", "raw": "Data [Stanford Universityโ€™s 2024 AI Index Report]: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://aiindex.stanford.edu/report/", "resource": null, "url": null, "href": "https://aiindex.stanford.edu/report/", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ", "raw": " ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Graphic: ", "raw": "Graphic: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://voronoiapp.com/technology/Googles-Gemini-Ultra-Cost-191M-to-Develop--1088", "resource": null, "url": null, "href": "https://voronoiapp.com/technology/Googles-Gemini-Ultra-Cost-191M-to-Develop--1088", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Many thanks to everyone spending tons of resources and open-sourcing the models! ๐Ÿค—", "raw": "Many thanks to everyone spending tons of resources and open-sourcing the models! ๐Ÿค—", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
It took Googleโ€™s Transformer model from 2017 a whopping $900 to train. ๐Ÿ’ธ This in contrast to the $191 million Google spent on Gemini Ultra sounds like a bargain! ๐Ÿ’ฐ Gemini Ultra required 50 billion petaFLOPS (one petaFLOP equals one quadrillion FLOPs). ๐Ÿค– Compared to OpenAIโ€™s GPT-4, which required 21 billion petaFLOPS, at a cost of $78 million. ๐Ÿ’ก 2017: Original Transformer Model: $930 [@Google ] ๐Ÿ’ป 2018: BERT-Large: $3,288 [@Google] ๐Ÿ“š 2019: RoBERTa Large: 160k [@Meta] ๐ŸŒ 2020: GPT-3(175B): $4.32M [@OpenAI] ๐Ÿง  2023: Llama 2 70B: $3.93M [@Meta] ๐Ÿ‘ 2023: GPT-4: $78.35M [@OpenAI] ๐ŸŒŸ Now, Gemini Ultra: $191.4M [@Google] ๐Ÿš€ This forms an exponential curve! ๐Ÿคฏ But, why? ๐Ÿค” Compute, data, and expertise. All three come at a great cost! โš™๏ธ๐Ÿ“Š๐Ÿ’ก Google recently made Gemini-1.5-Flash fine-tuning free, as it's almost impossible for regular businesses to justify an in-house trained foundational model! ๐Ÿ†“ This barrier of cost is going to result in fewer new foundational models/less competition and more fine-tunes! ๐Ÿ“‰๐Ÿ”„ Data [Stanford Universityโ€™s 2024 AI Index Report]: https://aiindex.stanford.edu/report/ Graphic: https://voronoiapp.com/technology/Googles-Gemini-Ultra-Cost-191M-to-Develop--1088 Many thanks to everyone spending tons of resources and open-sourcing the models! ๐Ÿค—
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2024-08-14T05:30:34.000Z
2024-08-22T23:12:30.025Z
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@papepipopu hey would you like to join my team me and my partner created a trading bot now we are diving into the machine learning world if you are intrested email me [email protected]
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2024-08-14T02:38:05.000Z
2024-08-15T14:50:52.119Z
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/posts/nebazi12/760983212136091
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[ { "type": "text", "value": "๐Ÿšจ Code now available for \"Using Large Language Models for Hyperparameter Optimization\" at ", "raw": "๐Ÿšจ Code now available for \"Using Large Language Models for Hyperparameter Optimization\" at ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/michaelrzhang/LLM-HyperOpt", "resource": null, "url": null, "href": "https://github.com/michaelrzhang/LLM-HyperOpt", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ๐Ÿšจ", "raw": " ๐Ÿšจ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "TLDR: You can just ask LLMs which hyperparameters to use, and it works pretty well! You can even directly optimize your modelโ€™s code as a hyperparameter with this.", "raw": "TLDR: You can just ask LLMs which hyperparameters to use, and it works pretty well! You can even directly optimize your modelโ€™s code as a hyperparameter with this.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Check out the paper at ", "raw": "Check out the paper at ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/abs/2312.04528", "resource": null, "url": null, "href": "https://arxiv.org/abs/2312.04528", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " - with Michael Zhang, Nishkrit Desai, Juhan Bae, and Jimmy Ba", "raw": " - with Michael Zhang, Nishkrit Desai, Juhan Bae, and Jimmy Ba", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿšจ Code now available for "Using Large Language Models for Hyperparameter Optimization" at https://github.com/michaelrzhang/LLM-HyperOpt ๐Ÿšจ TLDR: You can just ask LLMs which hyperparameters to use, and it works pretty well! You can even directly optimize your modelโ€™s code as a hyperparameter with this. Check out the paper at https://arxiv.org/abs/2312.04528 - with Michael Zhang, Nishkrit Desai, Juhan Bae, and Jimmy Ba
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2024-08-14T02:27:31.000Z
2024-08-14T02:27:31.666Z
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/posts/lorraine2/835007687119871
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booooring
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2024-08-14T01:05:08.000Z
2024-08-14T20:56:34.535Z
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/posts/nroggendorff/609629654995124
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[ { "type": "text", "value": "Most LLMs are not reproducible because the underlying deep neural networks are not. Because that's something LLM creators don't care about. We do, and ours are reproducible, including our GenAI that uses GAN. ", "raw": "Most LLMs are not reproducible because the underlying deep neural networks are not. Because that's something LLM creators don't care about. We do, and ours are reproducible, including our GenAI that uses GAN. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "All you have to do is allow the user to specify the seeds of the random number generators involved. First, you need a good random generator you have full control over. Better than numpy.random. See ours, with infinite period and one line of code, faster and better than what's in Python and elsewhere. Here is the link: ", "raw": "All you have to do is allow the user to specify the seeds of the random number generators involved. First, you need a good random generator you have full control over. Better than numpy.random. See ours, with infinite period and one line of code, faster and better than what's in Python and elsewhere. Here is the link: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://mltblog.com/4fGDLu0", "resource": null, "url": null, "href": "https://mltblog.com/4fGDLu0", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Most LLMs are not reproducible because the underlying deep neural networks are not. Because that's something LLM creators don't care about. We do, and ours are reproducible, including our GenAI that uses GAN. All you have to do is allow the user to specify the seeds of the random number generators involved. First, you need a good random generator you have full control over. Better than numpy.random. See ours, with infinite period and one line of code, faster and better than what's in Python and elsewhere. Here is the link: https://mltblog.com/4fGDLu0
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2024-08-14T00:18:43.000Z
2024-08-14T22:56:04.737Z
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/posts/vincentg64/258980980246376
446
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237039228274962
[ { "type": "text", "value": "Chomsky predicting LLMs in 1956, curated by Ryan Rhodes (Rutgers) ", "raw": "Chomsky predicting LLMs in 1956, curated by Ryan Rhodes (Rutgers) ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Chomsky predicting LLMs in 1956, curated by Ryan Rhodes (Rutgers)
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2024-08-13T17:06:42.000Z
2024-08-13T17:06:42.666Z
[]
/posts/Smooke/237039228274962
598
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925847991625087
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mobiuslabsgmbh/Llama-3.1-70b-instruct_4bitgs64_hqq 99% of the performance across various benchmarks! https://huggingface.co/mobiuslabsgmbh/Llama-3.1-70b-instruct_4bitgs64_hqq
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2024-08-12T18:56:29.000Z
2024-08-12T18:56:29.759Z
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/posts/KnutJaegersberg/925847991625087
2,309
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[ { "type": "text", "value": "I'm excited to announce that Transformers.js V3 is finally available on NPM! ๐Ÿ”ฅ State-of-the-art Machine Learning for the web, now with WebGPU support! ๐Ÿคฏโšก๏ธ", "raw": "I'm excited to announce that Transformers.js V3 is finally available on NPM! ๐Ÿ”ฅ State-of-the-art Machine Learning for the web, now with WebGPU support! ๐Ÿคฏโšก๏ธ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Install it from NPM with:", "raw": "Install it from NPM with:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐š—๐š™๐š– ๐š’ @๐š‘๐šž๐š๐š๐š’๐š—๐š๐š๐šŠ๐šŒ๐šŽ/๐š๐š›๐šŠ๐š—๐šœ๐š๐š˜๐š›๐š–๐šŽ๐š›๐šœ", "raw": "๐š—๐š™๐š– ๐š’ @๐š‘๐šž๐š๐š๐š’๐š—๐š๐š๐šŠ๐šŒ๐šŽ/๐š๐š›๐šŠ๐š—๐šœ๐š๐š˜๐š›๐š–๐šŽ๐š›๐šœ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "or via CDN, for example: ", "raw": "or via CDN, for example: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://v2.scrimba.com/s0lmm0qh1q", "resource": null, "url": null, "href": "https://v2.scrimba.com/s0lmm0qh1q", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Segment Anything demo: ", "raw": "Segment Anything demo: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/webml-community/segment-anything-webgpu", "resource": { "type": "space", "id": "webml-community/segment-anything-webgpu", "discussionNum": null }, "url": "https://huggingface.co/spaces/webml-community/segment-anything-webgpu", "href": null, "user": null, "lang": null, "code": null, "label": null } ]
I'm excited to announce that Transformers.js V3 is finally available on NPM! ๐Ÿ”ฅ State-of-the-art Machine Learning for the web, now with WebGPU support! ๐Ÿคฏโšก๏ธ Install it from NPM with: ๐š—๐š™๐š– ๐š’ @๐š‘๐šž๐š๐š๐š’๐š—๐š๐š๐šŠ๐šŒ๐šŽ/๐š๐š›๐šŠ๐š—๐šœ๐š๐š˜๐š›๐š–๐šŽ๐š›๐šœ or via CDN, for example: https://v2.scrimba.com/s0lmm0qh1q Segment Anything demo: https://huggingface.co/spaces/webml-community/segment-anything-webgpu
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2024-08-12T16:36:51.000Z
2024-10-24T19:08:59.184Z
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/posts/Xenova/681836693682285
14,782
5
159368967552315
[ { "type": "text", "value": "Hey HF. I just released a new reward modelling dataset: ", "raw": "Hey HF. I just released a new reward modelling dataset: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/Avelina/UltraSteer-v0", "resource": { "type": "dataset", "id": "Avelina/UltraSteer-v0", "discussionNum": null }, "url": "https://huggingface.co/datasets/Avelina/UltraSteer-v0", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "UltraSteer-V0 is a massive collection of single- and multi-turn dialogue with fine-grained reward labels produced by Nvidia's ", "raw": "UltraSteer-V0 is a massive collection of single- and multi-turn dialogue with fine-grained reward labels produced by Nvidia's ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/nvidia/Llama2-13B-SteerLM-RM", "resource": { "type": "model", "id": "nvidia/Llama2-13B-SteerLM-RM", "discussionNum": null }, "url": "https://huggingface.co/nvidia/Llama2-13B-SteerLM-RM", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " reward model. We have a total of 2.3M labelled sequences taken from high quality datasets with a total of 2.8M labelled turns each containing 9 attributes produced as is from the reward model.", "raw": " reward model. We have a total of 2.3M labelled sequences taken from high quality datasets with a total of 2.8M labelled turns each containing 9 attributes produced as is from the reward model.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This is still very much an early version of the dataset (but it's fully usable!) and an updated version will be on the way with a full paper.", "raw": "This is still very much an early version of the dataset (but it's fully usable!) and an updated version will be on the way with a full paper.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I would really appreciate if people could take a look at the dataset and suggest any improvements (e.g. more data sources, different cleaning approaches, different label schema, etc) in the community section.", "raw": "I would really appreciate if people could take a look at the dataset and suggest any improvements (e.g. more data sources, different cleaning approaches, different label schema, etc) in the community section.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Hey HF. I just released a new reward modelling dataset: https://huggingface.co/datasets/Avelina/UltraSteer-v0 UltraSteer-V0 is a massive collection of single- and multi-turn dialogue with fine-grained reward labels produced by Nvidia's https://huggingface.co/nvidia/Llama2-13B-SteerLM-RM reward model. We have a total of 2.3M labelled sequences taken from high quality datasets with a total of 2.8M labelled turns each containing 9 attributes produced as is from the reward model. This is still very much an early version of the dataset (but it's fully usable!) and an updated version will be on the way with a full paper. I would really appreciate if people could take a look at the dataset and suggest any improvements (e.g. more data sources, different cleaning approaches, different label schema, etc) in the community section.
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2024-08-12T15:29:21.000Z
2024-08-17T13:23:13.617Z
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/posts/Avelina/159368967552315
2,162
2
285509068969829
[ { "type": "text", "value": "Releasing HQQ Llama-3.1-70b 4-bit quantized version! Check it out at ", "raw": "Releasing HQQ Llama-3.1-70b 4-bit quantized version! Check it out at ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/mobiuslabsgmbh/Llama-3.1-70b-instruct_4bitgs64_hqq", "resource": { "type": "model", "id": "mobiuslabsgmbh/Llama-3.1-70b-instruct_4bitgs64_hqq", "discussionNum": null }, "url": "https://huggingface.co/mobiuslabsgmbh/Llama-3.1-70b-instruct_4bitgs64_hqq", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ". ", "raw": ". ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Achieves 99% of the base model performance across various benchmarks! Details in the model card. ", "raw": "Achieves 99% of the base model performance across various benchmarks! Details in the model card. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Releasing HQQ Llama-3.1-70b 4-bit quantized version! Check it out at https://huggingface.co/mobiuslabsgmbh/Llama-3.1-70b-instruct_4bitgs64_hqq. Achieves 99% of the base model performance across various benchmarks! Details in the model card.
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2024-08-12T14:48:59.000Z
2024-08-12T14:48:59.725Z
[]
/posts/appoose/285509068969829
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FalconMamba 7B - a new model from TII (Technology Innovation Institute) is out ! - Blogpost: https://huggingface.co/blog/falconmamba - Link to collection: https://huggingface.co/collections/tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a - Link to playground: https://huggingface.co/spaces/tiiuae/falcon-mamba-playground
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2024-08-12T14:26:48.000Z
2024-08-12T14:26:48.320Z
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[ { "type": "text", "value": "If you are interested in Knowledge Graphs, I invented all of this a year ago. It is a Encoder/Decoder that works with Knowledge Graphs. I am glad the world finally realizes this is useful a year later. I tried to tell you. I have not licensed any of the math. I own all of it. I do not have any plans to ever enforce the licensing but I like holding onto it. ", "raw": "If you are interested in Knowledge Graphs, I invented all of this a year ago. It is a Encoder/Decoder that works with Knowledge Graphs. I am glad the world finally realizes this is useful a year later. I tried to tell you. I have not licensed any of the math. I own all of it. I do not have any plans to ever enforce the licensing but I like holding onto it. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/TuringsSolutions/pfafresearch", "resource": null, "url": null, "href": "https://huggingface.co/blog/TuringsSolutions/pfafresearch", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ", "raw": " ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
If you are interested in Knowledge Graphs, I invented all of this a year ago. It is a Encoder/Decoder that works with Knowledge Graphs. I am glad the world finally realizes this is useful a year later. I tried to tell you. I have not licensed any of the math. I own all of it. I do not have any plans to ever enforce the licensing but I like holding onto it. https://huggingface.co/blog/TuringsSolutions/pfafresearch
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2024-08-12T12:21:13.000Z
2024-08-16T15:00:22.544Z
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/posts/TuringsSolutions/389817265017183
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[ { "type": "text", "value": "Here is an AI Puzzle!", "raw": "Here is an AI Puzzle!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "When you solve it just use a ๐Ÿ˜Ž emoji.", "raw": "When you solve it just use a ๐Ÿ˜Ž emoji.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "NO SPOILERS", "raw": "NO SPOILERS", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "A similar puzzle might have each picture that has a hidden meaning of summer, winter, fall, spring, and the answer would be seasons.", "raw": "A similar puzzle might have each picture that has a hidden meaning of summer, winter, fall, spring, and the answer would be seasons.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Its a little dated now (almost a year), so bottom right might be tough. ", "raw": "Its a little dated now (almost a year), so bottom right might be tough. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Thanks to ", "raw": "Thanks to ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@johko", "resource": null, "url": null, "href": null, "user": "johko", "lang": null, "code": null, "label": null }, { "type": "text", "value": " for the encouragement to post!", "raw": " for the encouragement to post!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Here is an AI Puzzle! When you solve it just use a ๐Ÿ˜Ž emoji. NO SPOILERS A similar puzzle might have each picture that has a hidden meaning of summer, winter, fall, spring, and the answer would be seasons. Its a little dated now (almost a year), so bottom right might be tough. Thanks to @johko for the encouragement to post!
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2024-08-12T09:30:10.000Z
2024-08-12T09:36:52.466Z
[]
/posts/derek-thomas/790598307520288
2,030
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[ { "type": "text", "value": "How can i check my resources limitation and usage?", "raw": "How can i check my resources limitation and usage?", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
How can i check my resources limitation and usage?
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2024-08-12T08:36:38.000Z
2024-08-13T21:53:27.335Z
[]
/posts/measmonysuon/763181990625291
621
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[ { "type": "text", "value": "SFT + Quantisation + Unsloth is a super easy way of squeezing extra performance out of an LLM at low latencies. Here are some hand y resources to bootstrap your projects.", "raw": "SFT + Quantisation + Unsloth is a super easy way of squeezing extra performance out of an LLM at low latencies. Here are some hand y resources to bootstrap your projects.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Here's a filtered dataset from Helpsteer2 with the most correct and coherent samples: ", "raw": "Here's a filtered dataset from Helpsteer2 with the most correct and coherent samples: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/burtenshaw/helpsteer-2-plus", "resource": { "type": "dataset", "id": "burtenshaw/helpsteer-2-plus", "discussionNum": null }, "url": "https://huggingface.co/datasets/burtenshaw/helpsteer-2-plus", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This is a SFT finetuned model: ttps://huggingface.co/burtenshaw/gemma-help-tiny-sft", "raw": "This is a SFT finetuned model: ttps://huggingface.co/burtenshaw/gemma-help-tiny-sft", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This is the notebook I use to train the model: ", "raw": "This is the notebook I use to train the model: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://colab.research.google.com/drive/17oskw_5lil5C3jCW34rA-EXjXnGgRRZw?usp=sharing", "resource": null, "url": null, "href": "https://colab.research.google.com/drive/17oskw_5lil5C3jCW34rA-EXjXnGgRRZw?usp=sharing", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Here's a load of Unsloth notebook on finetuning and inference: ", "raw": "Here's a load of Unsloth notebook on finetuning and inference: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://docs.unsloth.ai/get-started/unsloth-notebooks", "resource": null, "url": null, "href": "https://docs.unsloth.ai/get-started/unsloth-notebooks", "user": null, "lang": null, "code": null, "label": null } ]
SFT + Quantisation + Unsloth is a super easy way of squeezing extra performance out of an LLM at low latencies. Here are some hand y resources to bootstrap your projects. Here's a filtered dataset from Helpsteer2 with the most correct and coherent samples: https://huggingface.co/datasets/burtenshaw/helpsteer-2-plus This is a SFT finetuned model: ttps://huggingface.co/burtenshaw/gemma-help-tiny-sft This is the notebook I use to train the model: https://colab.research.google.com/drive/17oskw_5lil5C3jCW34rA-EXjXnGgRRZw?usp=sharing Here's a load of Unsloth notebook on finetuning and inference: https://docs.unsloth.ai/get-started/unsloth-notebooks
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2024-08-12T08:27:20.000Z
2024-08-12T08:27:20.492Z
[]
/posts/burtenshaw/897737525699797
1,401
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[ { "type": "text", "value": "๐Ÿš€ RAGoon is now available on PyPI, GitHub, and as a Space on Hugging Face for batched embeddings generation ๐Ÿค—", "raw": "๐Ÿš€ RAGoon is now available on PyPI, GitHub, and as a Space on Hugging Face for batched embeddings generation ๐Ÿค—", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "RAGoon is a set of NLP utilities for multi-model embedding production, high-dimensional vector visualization, and aims to improve language model performance by providing contextually relevant information through search-based querying, web scraping and data augmentation techniques.", "raw": "RAGoon is a set of NLP utilities for multi-model embedding production, high-dimensional vector visualization, and aims to improve language model performance by providing contextually relevant information through search-based querying, web scraping and data augmentation techniques.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "At this stage, 5 major classes are available via RAGoon to facilitate:", "raw": "At this stage, 5 major classes are available via RAGoon to facilitate:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- the production of chain embeddings for several models to simplify a continuous deployment process;", "raw": "- the production of chain embeddings for several models to simplify a continuous deployment process;", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- production of LLM requests for web querying and content retrieval via the Google API;", "raw": "- production of LLM requests for web querying and content retrieval via the Google API;", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- recursive chunking via tokens;", "raw": "- recursive chunking via tokens;", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- data visualization and the function to load embeddings from a FAISS index, reduce their dimensionality using PCA and/or t-SNE, and visualize them in an interactive 3D graph;", "raw": "- data visualization and the function to load embeddings from a FAISS index, reduce their dimensionality using PCA and/or t-SNE, and visualize them in an interactive 3D graph;", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- the creation of binary indexes for search with scalar (int8) rescoring. ", "raw": "- the creation of binary indexes for search with scalar (int8) rescoring. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Link to GitHub: ", "raw": "Link to GitHub: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/louisbrulenaudet/ragoon", "resource": null, "url": null, "href": "https://github.com/louisbrulenaudet/ragoon", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Link to the ๐Ÿค— Space: ", "raw": "Link to the ๐Ÿค— Space: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/louisbrulenaudet/ragoon", "resource": { "type": "space", "id": "louisbrulenaudet/ragoon", "discussionNum": null }, "url": "https://huggingface.co/spaces/louisbrulenaudet/ragoon", "href": null, "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿš€ RAGoon is now available on PyPI, GitHub, and as a Space on Hugging Face for batched embeddings generation ๐Ÿค— RAGoon is a set of NLP utilities for multi-model embedding production, high-dimensional vector visualization, and aims to improve language model performance by providing contextually relevant information through search-based querying, web scraping and data augmentation techniques. At this stage, 5 major classes are available via RAGoon to facilitate: - the production of chain embeddings for several models to simplify a continuous deployment process; - production of LLM requests for web querying and content retrieval via the Google API; - recursive chunking via tokens; - data visualization and the function to load embeddings from a FAISS index, reduce their dimensionality using PCA and/or t-SNE, and visualize them in an interactive 3D graph; - the creation of binary indexes for search with scalar (int8) rescoring. Link to GitHub: https://github.com/louisbrulenaudet/ragoon Link to the ๐Ÿค— Space: https://huggingface.co/spaces/louisbrulenaudet/ragoon
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2024-08-12T05:37:46.000Z
2024-08-12T05:37:46.760Z
[]
/posts/louisbrulenaudet/235312600642861
2,757
0
419308856356985
[ { "type": "text", "value": "BiRefNet State Of The Art Newest Very Best Background Batch Remover APP", "raw": "BiRefNet State Of The Art Newest Very Best Background Batch Remover APP", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Official repo : ", "raw": "Official repo : ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/ZhengPeng7/BiRefNet", "resource": null, "url": null, "href": "https://github.com/ZhengPeng7/BiRefNet", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Download APP and installers from : ", "raw": "Download APP and installers from : ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://www.patreon.com/posts/109913645", "resource": null, "url": null, "href": "https://www.patreon.com/posts/109913645", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Hugging Face Demo : ", "raw": "Hugging Face Demo : ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/ZhengPeng7/BiRefNet_demo", "resource": { "type": "space", "id": "ZhengPeng7/BiRefNet_demo", "discussionNum": null }, "url": "https://huggingface.co/spaces/ZhengPeng7/BiRefNet_demo", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I have developed a very advanced Gradio APP for this with full proper file saving and batch processing. Also my version removes BG and saves as transparent background.", "raw": "I have developed a very advanced Gradio APP for this with full proper file saving and batch processing. Also my version removes BG and saves as transparent background.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The APP uses huge VRAM for high resolution images. However it is still working uber fast even though using shared VRAM. So make sure that you have high RAM or set virtual RAM.", "raw": "The APP uses huge VRAM for high resolution images. However it is still working uber fast even though using shared VRAM. So make sure that you have high RAM or set virtual RAM.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Click below to see how to set virtual RAM on Windows.", "raw": "Click below to see how to set virtual RAM on Windows.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://www.windowscentral.com/how-change-virtual-memory-size-windows-10", "resource": null, "url": null, "href": "https://www.windowscentral.com/how-change-virtual-memory-size-windows-10", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "On Massed Compute A6000 GPU (31 cents per hour) you can very fast remove even very high res images backgrounds.", "raw": "On Massed Compute A6000 GPU (31 cents per hour) you can very fast remove even very high res images backgrounds.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Currently we have 1 click installers for RunPod, Massed Compute, Kaggle and Windows.", "raw": "Currently we have 1 click installers for RunPod, Massed Compute, Kaggle and Windows.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Windows Requirements", "raw": "Windows Requirements", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Python 3.10, FFmpeg, Cuda 11.8, C++ tools and Git", "raw": "Python 3.10, FFmpeg, Cuda 11.8, C++ tools and Git", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "If it doesn't work make sure to below tutorial and install everything exactly as shown in this below tutorial", "raw": "If it doesn't work make sure to below tutorial and install everything exactly as shown in this below tutorial", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://youtu.be/-NjNy7afOQ0", "resource": null, "url": null, "href": "https://youtu.be/-NjNy7afOQ0", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "How To Use On Windows", "raw": "How To Use On Windows", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Just extract files into like c:/BiRefNet_v1", "raw": "Just extract files into like c:/BiRefNet_v1", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Double click Windows_Install.bat file and it will generate a isolated virtual environment and install requirements", "raw": "Double click Windows_Install.bat file and it will generate a isolated virtual environment and install requirements", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "It will automatically download models into your Hugging Face cache (best model under 1 GB)", "raw": "It will automatically download models into your Hugging Face cache (best model under 1 GB)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Then start and use the Gradio APP with Windows_Start_App.bat", "raw": "Then start and use the Gradio APP with Windows_Start_App.bat", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Cloud How To Use", "raw": "Cloud How To Use", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Massed Compute, RunPod has instructions txt files. Follow them", "raw": "Massed Compute, RunPod has instructions txt files. Follow them", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Kaggle has all the instructions 1 by 1", "raw": "Kaggle has all the instructions 1 by 1", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "On Kaggle set resolution 1024x1024 or you will get out of memory error", "raw": "On Kaggle set resolution 1024x1024 or you will get out of memory error", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
BiRefNet State Of The Art Newest Very Best Background Batch Remover APP Official repo : https://github.com/ZhengPeng7/BiRefNet Download APP and installers from : https://www.patreon.com/posts/109913645 Hugging Face Demo : https://huggingface.co/spaces/ZhengPeng7/BiRefNet_demo I have developed a very advanced Gradio APP for this with full proper file saving and batch processing. Also my version removes BG and saves as transparent background. The APP uses huge VRAM for high resolution images. However it is still working uber fast even though using shared VRAM. So make sure that you have high RAM or set virtual RAM. Click below to see how to set virtual RAM on Windows. https://www.windowscentral.com/how-change-virtual-memory-size-windows-10 On Massed Compute A6000 GPU (31 cents per hour) you can very fast remove even very high res images backgrounds. Currently we have 1 click installers for RunPod, Massed Compute, Kaggle and Windows. Windows Requirements Python 3.10, FFmpeg, Cuda 11.8, C++ tools and Git If it doesn't work make sure to below tutorial and install everything exactly as shown in this below tutorial https://youtu.be/-NjNy7afOQ0 How To Use On Windows Just extract files into like c:/BiRefNet_v1 Double click Windows_Install.bat file and it will generate a isolated virtual environment and install requirements It will automatically download models into your Hugging Face cache (best model under 1 GB) Then start and use the Gradio APP with Windows_Start_App.bat Cloud How To Use Massed Compute, RunPod has instructions txt files. Follow them Kaggle has all the instructions 1 by 1 On Kaggle set resolution 1024x1024 or you will get out of memory error
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[]
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2024-08-11T22:39:42.000Z
2024-08-11T22:39:42.358Z
[]
/posts/MonsterMMORPG/419308856356985
1,907
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[ { "type": "text", "value": "AutoGen from ", "raw": "AutoGen from ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@Microsoft", "resource": null, "url": null, "href": null, "user": "Microsoft", "lang": null, "code": null, "label": null }, { "type": "text", "value": " is crazy! ๐Ÿš€ It's an open-source framework that allows LLM agents to chat with each other to solve your tasks. ๐Ÿค–๐Ÿ’ฌ", "raw": " is crazy! ๐Ÿš€ It's an open-source framework that allows LLM agents to chat with each other to solve your tasks. ๐Ÿค–๐Ÿ’ฌ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "They use the Assistant-Agent and User-Proxy-Agent framework! ๐Ÿ› ๏ธ", "raw": "They use the Assistant-Agent and User-Proxy-Agent framework! ๐Ÿ› ๏ธ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "As the name suggests, the Assistant-Agent does the work, and the User-Proxy-Agent behaves like a human, guiding the Assistant-Agent and double-checking its work! ๐Ÿง‘โ€๐Ÿ’ปโœ…", "raw": "As the name suggests, the Assistant-Agent does the work, and the User-Proxy-Agent behaves like a human, guiding the Assistant-Agent and double-checking its work! ๐Ÿง‘โ€๐Ÿ’ปโœ…", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Both Assistant-Agent and User-Proxy-Agent can be the same or different LLMs. ๐Ÿค”๐Ÿ”„", "raw": "Both Assistant-Agent and User-Proxy-Agent can be the same or different LLMs. ๐Ÿค”๐Ÿ”„", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "AutoGen is an open-source programming framework for building AI agents and facilitating cooperation among multiple agents to solve tasks. ๐ŸŒŸ", "raw": "AutoGen is an open-source programming framework for building AI agents and facilitating cooperation among multiple agents to solve tasks. ๐ŸŒŸ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This is truly amazing for building agentic AI quickly! ๐Ÿš€โœจ", "raw": "This is truly amazing for building agentic AI quickly! ๐Ÿš€โœจ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "GitHub: ", "raw": "GitHub: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/microsoft/autogen", "resource": null, "url": null, "href": "https://github.com/microsoft/autogen", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ๐Ÿ”—", "raw": " ๐Ÿ”—", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "code_fence", "value": null, "raw": "```Python\nfrom autogen import AssistantAgent, UserProxyAgent, config_list_from_json\n\n#config\nconfig_list = config_list_from_json(env_or_file=\"OAI_CONFIG_LIST\")\n\nassistant = AssistantAgent(\"assistant\", llm_config={\"config_list\": config_list})\nuser_proxy = UserProxyAgent(\"user_proxy\", code_execution_config={\"work_dir\": \"coding\", \"use_docker\": False}) \n\nuser_proxy.initiate_chat(assistant, message=\"Plot a chart of NVDA and TESLA stock price change YTD.\")\n# This initiates an automated chat between the two agents to solve the task\n```", "resource": null, "url": null, "href": null, "user": null, "lang": "Python", "code": "from autogen import AssistantAgent, UserProxyAgent, config_list_from_json\n\n#config\nconfig_list = config_list_from_json(env_or_file=\"OAI_CONFIG_LIST\")\n\nassistant = AssistantAgent(\"assistant\", llm_config={\"config_list\": config_list})\nuser_proxy = UserProxyAgent(\"user_proxy\", code_execution_config={\"work_dir\": \"coding\", \"use_docker\": False}) \n\nuser_proxy.initiate_chat(assistant, message=\"Plot a chart of NVDA and TESLA stock price change YTD.\")\n# This initiates an automated chat between the two agents to solve the task", "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
AutoGen from @Microsoft is crazy! ๐Ÿš€ It's an open-source framework that allows LLM agents to chat with each other to solve your tasks. ๐Ÿค–๐Ÿ’ฌ They use the Assistant-Agent and User-Proxy-Agent framework! ๐Ÿ› ๏ธ As the name suggests, the Assistant-Agent does the work, and the User-Proxy-Agent behaves like a human, guiding the Assistant-Agent and double-checking its work! ๐Ÿง‘โ€๐Ÿ’ปโœ… Both Assistant-Agent and User-Proxy-Agent can be the same or different LLMs. ๐Ÿค”๐Ÿ”„ AutoGen is an open-source programming framework for building AI agents and facilitating cooperation among multiple agents to solve tasks. ๐ŸŒŸ This is truly amazing for building agentic AI quickly! ๐Ÿš€โœจ GitHub: https://github.com/microsoft/autogen ๐Ÿ”— ```Python from autogen import AssistantAgent, UserProxyAgent, config_list_from_json #config config_list = config_list_from_json(env_or_file="OAI_CONFIG_LIST") assistant = AssistantAgent("assistant", llm_config={"config_list": config_list}) user_proxy = UserProxyAgent("user_proxy", code_execution_config={"work_dir": "coding", "use_docker": False}) user_proxy.initiate_chat(assistant, message="Plot a chart of NVDA and TESLA stock price change YTD.") # This initiates an automated chat between the two agents to solve the task ```
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2024-08-11T17:56:28.000Z
2024-08-11T17:56:28.612Z
[]
/posts/singhsidhukuldeep/790332044897556
2,141
0
807842938197371
[ { "type": "text", "value": "NEW MODEL + DATASET! :)", "raw": "NEW MODEL + DATASET! :)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Check out Enigma, our new code-instruct model:", "raw": "Check out Enigma, our new code-instruct model:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- trained on synthetic code-instruct data created with Llama 3.1 405b", "raw": "- trained on synthetic code-instruct data created with Llama 3.1 405b", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- high quality code-instruct within the Llama 3.1 Instruct format", "raw": "- high quality code-instruct within the Llama 3.1 Instruct format", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The model: ", "raw": "The model: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/ValiantLabs/Llama3.1-8B-Enigma", "resource": { "type": "model", "id": "ValiantLabs/Llama3.1-8B-Enigma", "discussionNum": null }, "url": "https://huggingface.co/ValiantLabs/Llama3.1-8B-Enigma", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The dataset: ", "raw": "The dataset: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/sequelbox/Tachibana", "resource": { "type": "dataset", "id": "sequelbox/Tachibana", "discussionNum": null }, "url": "https://huggingface.co/datasets/sequelbox/Tachibana", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Enjoy! We've got more new datasets and models to follow soon.", "raw": "Enjoy! We've got more new datasets and models to follow soon.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
NEW MODEL + DATASET! :) Check out Enigma, our new code-instruct model: - trained on synthetic code-instruct data created with Llama 3.1 405b - high quality code-instruct within the Llama 3.1 Instruct format The model: https://huggingface.co/ValiantLabs/Llama3.1-8B-Enigma The dataset: https://huggingface.co/datasets/sequelbox/Tachibana Enjoy! We've got more new datasets and models to follow soon.
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2024-08-11T04:00:04.000Z
2024-08-11T04:00:04.774Z
[]
/posts/sequelbox/807842938197371
853
0
815445483264294
[ { "type": "text", "value": "So... Finally getting A pitch-kit ready for Intelligent Estate, a DC-MD-VA based AI firm offering air-gapped on site secure services for small businesses and families, looking for motivated team members. There are other businesses under the holdings which can make use of scientific/mathematics or sales skills as well. Virtual or flexible positions and great people of all walks of life are welcome apply to [email protected] or join the intelligent estate group if you have any questions.. Work on a contract or paid basis, with shares available, as well, for partners. The Frontier is here and we're fighting to emancipate the power of AI. ", "raw": "So... Finally getting A pitch-kit ready for Intelligent Estate, a DC-MD-VA based AI firm offering air-gapped on site secure services for small businesses and families, looking for motivated team members. There are other businesses under the holdings which can make use of scientific/mathematics or sales skills as well. Virtual or flexible positions and great people of all walks of life are welcome apply to [email protected] or join the intelligent estate group if you have any questions.. Work on a contract or paid basis, with shares available, as well, for partners. The Frontier is here and we're fighting to emancipate the power of AI. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@fuzzy-mittenz", "resource": null, "url": null, "href": null, "user": "fuzzy-mittenz", "lang": null, "code": null, "label": null } ]
So... Finally getting A pitch-kit ready for Intelligent Estate, a DC-MD-VA based AI firm offering air-gapped on site secure services for small businesses and families, looking for motivated team members. There are other businesses under the holdings which can make use of scientific/mathematics or sales skills as well. Virtual or flexible positions and great people of all walks of life are welcome apply to [email protected] or join the intelligent estate group if you have any questions.. Work on a contract or paid basis, with shares available, as well, for partners. The Frontier is here and we're fighting to emancipate the power of AI. @fuzzy-mittenz
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2024-11-07T02:43:55.000Z
2024-11-08T04:37:23.299Z
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/posts/fuzzy-mittenz/815445483264294
1,287
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934356758937834
[ { "type": "text", "value": "Are you familiar with the difference between discrete learning and predictive learning? This distinction is exactly why LLM models are not designed to perform and execute function calls, they are not the right shape for it. LLM models are prediction machines. Function calling requires discrete learning machines. Fortunately, you can easily couple an LLM model with a discrete learning algorithm. It is beyond easy to do, you simply need to know the math to do it. Want to dive deeper into this subject? Check out this video.", "raw": "Are you familiar with the difference between discrete learning and predictive learning? This distinction is exactly why LLM models are not designed to perform and execute function calls, they are not the right shape for it. LLM models are prediction machines. Function calling requires discrete learning machines. Fortunately, you can easily couple an LLM model with a discrete learning algorithm. It is beyond easy to do, you simply need to know the math to do it. Want to dive deeper into this subject? Check out this video.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://youtu.be/wBRem2p8iPM", "resource": null, "url": null, "href": "https://youtu.be/wBRem2p8iPM", "user": null, "lang": null, "code": null, "label": null } ]
Are you familiar with the difference between discrete learning and predictive learning? This distinction is exactly why LLM models are not designed to perform and execute function calls, they are not the right shape for it. LLM models are prediction machines. Function calling requires discrete learning machines. Fortunately, you can easily couple an LLM model with a discrete learning algorithm. It is beyond easy to do, you simply need to know the math to do it. Want to dive deeper into this subject? Check out this video. https://youtu.be/wBRem2p8iPM
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2024-11-06T23:03:05.000Z
2024-11-07T13:15:50.219Z
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/posts/TuringsSolutions/934356758937834
3,931
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751144495449880
[ { "type": "text", "value": "๐Ÿย Now it is possible to chat with telemetry data from real Formula 1 races!", "raw": "๐Ÿย Now it is possible to chat with telemetry data from real Formula 1 races!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This is an AI-powered solution for analyzing and generating detailed reports on Formula 1 racing sessions. This project combines the power of ReAct agents from LangChain with a RAG approach to pull data from a SQL database.", "raw": "This is an AI-powered solution for analyzing and generating detailed reports on Formula 1 racing sessions. This project combines the power of ReAct agents from LangChain with a RAG approach to pull data from a SQL database.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "At the core of this system is a text-to-SQL capability that allows users to ask natural language questions about various aspects of F1 races, such as driver performance, weather impact, race strategies, and more. The AI agent then queries the database, processes the information, and generates comprehensive reports tailored to the user's needs.", "raw": "At the core of this system is a text-to-SQL capability that allows users to ask natural language questions about various aspects of F1 races, such as driver performance, weather impact, race strategies, and more. The AI agent then queries the database, processes the information, and generates comprehensive reports tailored to the user's needs.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The reports can be exported in various formats, making it easy to share insights with team members, race fans, or the broader motorsports community.", "raw": "The reports can be exported in various formats, making it easy to share insights with team members, race fans, or the broader motorsports community.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "(The project is in beta, some erros may occur)", "raw": "(The project is in beta, some erros may occur)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Check it out:", "raw": "Check it out:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- ", "raw": "- ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Draichi/Formula1-race-debriefing", "resource": { "type": "space", "id": "Draichi/Formula1-race-debriefing", "discussionNum": null }, "url": "https://huggingface.co/spaces/Draichi/Formula1-race-debriefing", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- ", "raw": "- ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/Draichi/formula1-AI", "resource": null, "url": null, "href": "https://github.com/Draichi/formula1-AI", "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿย Now it is possible to chat with telemetry data from real Formula 1 races! This is an AI-powered solution for analyzing and generating detailed reports on Formula 1 racing sessions. This project combines the power of ReAct agents from LangChain with a RAG approach to pull data from a SQL database. At the core of this system is a text-to-SQL capability that allows users to ask natural language questions about various aspects of F1 races, such as driver performance, weather impact, race strategies, and more. The AI agent then queries the database, processes the information, and generates comprehensive reports tailored to the user's needs. The reports can be exported in various formats, making it easy to share insights with team members, race fans, or the broader motorsports community. (The project is in beta, some erros may occur) Check it out: - https://huggingface.co/spaces/Draichi/Formula1-race-debriefing - https://github.com/Draichi/formula1-AI
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2024-11-06T20:29:37.000Z
2024-11-07T18:12:04.829Z
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New app built based on https://huggingface.co/docs/transformers.js and https://huggingface.co/collections/minishlab/potion-6721e0abd4ea41881417f062! It uses the super performant CPU-only models to calculate semantic similarity fully client-side based on Excel or CSV tables. - App: https://do-me.github.io/semantic-similarity-table/ - Code: https://github.com/do-me/semantic-similarity-table
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2024-11-06T14:35:56.000Z
2024-11-06T14:35:56.196Z
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"new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "So what is Hunyuan3D-1", "raw": "So what is Hunyuan3D-1", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Official repo : ", "raw": "Official repo : ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/tencent/Hunyuan3D-1", "resource": null, "url": null, "href": "https://github.com/tencent/Hunyuan3D-1", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, 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for Text-to-3D and Image-to-3D Generation", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Abstract", "raw": "Abstract", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "While 3D generative models have greatly improved artists' workflows, the existing diffusion models for 3D generation suffer from slow generation and poor generalization. To address this issue, we propose a two-stage approach named Hunyuan3D-1.0 including a lite version and a standard version, that both support text- and image-conditioned generation.", "raw": "While 3D generative models have greatly improved artists' workflows, the existing diffusion models for 3D generation suffer from slow generation and poor generalization. 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In the second stage, we introduce a feed-forward reconstruction model that rapidly and faithfully reconstructs the 3D asset given the generated multi-view images in approximately 7 seconds. The reconstruction network learns to handle noises and in-consistency introduced by the multi-view diffusion and leverages the available information from the condition image to efficiently recover the 3D structure.", "raw": "In the first stage, we employ a multi-view diffusion model that efficiently generates multi-view RGB in approximately 4 seconds. These multi-view images capture rich details of the 3D asset from different viewpoints, relaxing the tasks from single-view to multi-view reconstruction. In the second stage, we introduce a feed-forward reconstruction model that rapidly and faithfully reconstructs the 3D asset given the generated multi-view images in approximately 7 seconds. The reconstruction network learns to handle noises and in-consistency introduced by the multi-view diffusion and leverages the available information from the condition image to efficiently recover the 3D structure.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Our framework involves the text-to-image model, i.e., Hunyuan-DiT, making it a unified framework to support both text- and image-conditioned 3D generation. Our standard version has 3x more parameters than our lite and other existing model. 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Hunyuan3D-1 - SOTA Open Source Text-to-3D and Image-to-3D - 1-Click Install and use both Locally on Windows and on Cloud - RunPod and Massed Compute Automatic Installers Works amazing on 24 GB GPUs Files > https://www.patreon.com/posts/115412205 So what is Hunyuan3D-1 Official repo : https://github.com/tencent/Hunyuan3D-1 On Hugging Face : https://huggingface.co/tencent/Hunyuan3D-1 Tencent Hunyuan3D-1.0: A Unified Framework for Text-to-3D and Image-to-3D Generation Abstract While 3D generative models have greatly improved artists' workflows, the existing diffusion models for 3D generation suffer from slow generation and poor generalization. To address this issue, we propose a two-stage approach named Hunyuan3D-1.0 including a lite version and a standard version, that both support text- and image-conditioned generation. In the first stage, we employ a multi-view diffusion model that efficiently generates multi-view RGB in approximately 4 seconds. These multi-view images capture rich details of the 3D asset from different viewpoints, relaxing the tasks from single-view to multi-view reconstruction. In the second stage, we introduce a feed-forward reconstruction model that rapidly and faithfully reconstructs the 3D asset given the generated multi-view images in approximately 7 seconds. The reconstruction network learns to handle noises and in-consistency introduced by the multi-view diffusion and leverages the available information from the condition image to efficiently recover the 3D structure. Our framework involves the text-to-image model, i.e., Hunyuan-DiT, making it a unified framework to support both text- and image-conditioned 3D generation. Our standard version has 3x more parameters than our lite and other existing model. Our Hunyuan3D-1.0 achieves an impressive balance between speed and quality, significantly reducing generation time while maintaining the quality and diversity of the produced assets.
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2024-11-06T08:53:32.000Z
2024-11-06T08:53:32.487Z
[]
/posts/MonsterMMORPG/780974876335675
4,510
0
102235619824271
[ { "type": "text", "value": "๐Ÿšจ๐Ÿ”ฅ New Release Alert! ๐Ÿ”ฅ๐Ÿšจ", "raw": "๐Ÿšจ๐Ÿ”ฅ New Release Alert! ๐Ÿ”ฅ๐Ÿšจ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Introducing the 435M model that outperforms Llama-Guard-3-8B while slashing 75% of the computation cost! ๐Ÿ’ป๐Ÿ’ฅ", "raw": "Introducing the 435M model that outperforms Llama-Guard-3-8B while slashing 75% of the computation cost! ๐Ÿ’ป๐Ÿ’ฅ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ‘‰ Check it out: ", "raw": "๐Ÿ‘‰ Check it out: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/hbseong/HarmAug-Guard", "resource": { "type": "model", "id": "hbseong/HarmAug-Guard", "discussionNum": null }, "url": "https://huggingface.co/hbseong/HarmAug-Guard", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " (Yes, INFERENCE CODE INCLUDED! ๐Ÿ’ก)", "raw": " (Yes, INFERENCE CODE INCLUDED! ๐Ÿ’ก)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "More details in our paper: ", "raw": "More details in our paper: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/abs/2410.01524", "resource": null, "url": null, "href": "https://arxiv.org/abs/2410.01524", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ๐Ÿ“œ", "raw": " ๐Ÿ“œ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "#HarmAug #LLM # Safety #EfficiencyBoost #Research #AI #MachineLearning ", "raw": "#HarmAug #LLM # Safety #EfficiencyBoost #Research #AI #MachineLearning ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿšจ๐Ÿ”ฅ New Release Alert! ๐Ÿ”ฅ๐Ÿšจ Introducing the 435M model that outperforms Llama-Guard-3-8B while slashing 75% of the computation cost! ๐Ÿ’ป๐Ÿ’ฅ ๐Ÿ‘‰ Check it out: https://huggingface.co/hbseong/HarmAug-Guard (Yes, INFERENCE CODE INCLUDED! ๐Ÿ’ก) More details in our paper: https://arxiv.org/abs/2410.01524 ๐Ÿ“œ #HarmAug #LLM # Safety #EfficiencyBoost #Research #AI #MachineLearning
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2024-11-06T06:56:19.000Z
2024-11-06T06:56:19.162Z
[]
/posts/hbseong/102235619824271
3,257
0
662164811009502
[ { "type": "text", "value": "Effortlessly stay up-to-date with AI research trends using a new AI tool, \"AI Paper Reviewer\" !! ", "raw": "Effortlessly stay up-to-date with AI research trends using a new AI tool, \"AI Paper Reviewer\" !! ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "It analyzes a list of Hugging Face Daily Papers(w/ ", "raw": "It analyzes a list of Hugging Face Daily Papers(w/ ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@akhaliq", "resource": null, "url": null, "href": null, "user": "akhaliq", "lang": null, "code": null, "label": null }, { "type": "text", "value": ") and turn them into insightful blog posts. This project leverages Gemini models (1.5 Pro, 1.5 Flash, and 1.5 Flash-8B) for content generation and Upstage Document Parse for parsing the layout and contents. ", "raw": ") and turn them into insightful blog posts. This project leverages Gemini models (1.5 Pro, 1.5 Flash, and 1.5 Flash-8B) for content generation and Upstage Document Parse for parsing the layout and contents. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "blog link: ", "raw": "blog link: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://deep-diver.github.io/ai-paper-reviewer/", "resource": null, "url": null, "href": "https://deep-diver.github.io/ai-paper-reviewer/", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Also, here is the link of GitHub repository for parsing and generating pipeline. By using this, you can easily build your own GitHub static pages based on any arXiv papers with your own interest!", "raw": "Also, here is the link of GitHub repository for parsing and generating pipeline. By using this, you can easily build your own GitHub static pages based on any arXiv papers with your own interest!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ": ", "raw": ": ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/deep-diver/paper-reviewer", "resource": null, "url": null, "href": "https://github.com/deep-diver/paper-reviewer", "user": null, "lang": null, "code": null, "label": null } ]
Effortlessly stay up-to-date with AI research trends using a new AI tool, "AI Paper Reviewer" !! It analyzes a list of Hugging Face Daily Papers(w/ @akhaliq) and turn them into insightful blog posts. This project leverages Gemini models (1.5 Pro, 1.5 Flash, and 1.5 Flash-8B) for content generation and Upstage Document Parse for parsing the layout and contents. blog link: https://deep-diver.github.io/ai-paper-reviewer/ Also, here is the link of GitHub repository for parsing and generating pipeline. By using this, you can easily build your own GitHub static pages based on any arXiv papers with your own interest! : https://github.com/deep-diver/paper-reviewer
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2024-11-06T05:02:54.000Z
2024-11-06T05:02:54.932Z
[]
/posts/chansung/662164811009502
4,409
0
192485637682391
[ { "type": "text", "value": "I still think whitespace in tokenizers are so dumb.", "raw": "I still think whitespace in tokenizers are so dumb.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Congrats, you just doubled your vocab size for no reason.", "raw": "Congrats, you just doubled your vocab size for no reason.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
I still think whitespace in tokenizers are so dumb. Congrats, you just doubled your vocab size for no reason.
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2024-11-05T16:16:29.000Z
2024-11-07T13:50:28.801Z
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/posts/nroggendorff/192485637682391
2,180
1
254238798418067
[ { "type": "text", "value": "๐Ÿซต๐Ÿ‘พ LM Studio is hiring engineers who know the ins and outs of NodeJS & want to work on local LLMs. ", "raw": "๐Ÿซต๐Ÿ‘พ LM Studio is hiring engineers who know the ins and outs of NodeJS & want to work on local LLMs. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Know anyone who might be interested?", "raw": "Know anyone who might be interested?", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Apply here: ", "raw": "Apply here: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://docs.google.com/forms/d/e/1FAIpQLSc_786-_i_q4fo5ESqYnNyjIH0B5Rs45QIwejd_NV5AjNDZ7A/viewform", "resource": null, "url": null, "href": "https://docs.google.com/forms/d/e/1FAIpQLSc_786-_i_q4fo5ESqYnNyjIH0B5Rs45QIwejd_NV5AjNDZ7A/viewform", "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿซต๐Ÿ‘พ LM Studio is hiring engineers who know the ins and outs of NodeJS & want to work on local LLMs. Know anyone who might be interested? Apply here: https://docs.google.com/forms/d/e/1FAIpQLSc_786-_i_q4fo5ESqYnNyjIH0B5Rs45QIwejd_NV5AjNDZ7A/viewform
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2024-11-05T15:39:44.000Z
2024-11-05T15:39:44.006Z
[]
/posts/yagilb/254238798418067
2,526
0
119846934511687
[ { "type": "text", "value": "Just tested Argilla's new data annotation feature - it's a game changer for AI project quality. ", "raw": "Just tested Argilla's new data annotation feature - it's a game changer for AI project quality. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Upload CSVs, work with published datasets, or improve existing ones directly on HuggingFace Hub. Setup took < 2 minutes, no code needed (see example below where I selected a dataset to classify tweets in categories).", "raw": "Upload CSVs, work with published datasets, or improve existing ones directly on HuggingFace Hub. Setup took < 2 minutes, no code needed (see example below where I selected a dataset to classify tweets in categories).", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Real world impact: Missing in Chicago won a Pulitzer using a similar approach - 200 volunteers labeled police misconduct files to train their model. That's the power of good data annotation.", "raw": "Real world impact: Missing in Chicago won a Pulitzer using a similar approach - 200 volunteers labeled police misconduct files to train their model. That's the power of good data annotation.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Three immediate use cases I see:", "raw": "Three immediate use cases I see:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Build collaborative training sets with your community (surprisingly underused in AI journalism)", "raw": "- Build collaborative training sets with your community (surprisingly underused in AI journalism)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Turn your website chatbot logs into high-quality fine-tuning data", "raw": "- Turn your website chatbot logs into high-quality fine-tuning data", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Compare generated vs published content (great for SEO headlines)", "raw": "- Compare generated vs published content (great for SEO headlines)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Works for solo projects or teams up to 100 people. All integrated with HuggingFace Hub for immediate model training.", "raw": "Works for solo projects or teams up to 100 people. All integrated with HuggingFace Hub for immediate model training.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Interesting to see tools like this making data quality more accessible. Data quality is the hidden driver of AI success that we don't talk about enough.", "raw": "Interesting to see tools like this making data quality more accessible. Data quality is the hidden driver of AI success that we don't talk about enough.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Check out the blogpost: ", "raw": "- Check out the blogpost: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/argilla-ui-hub", "resource": null, "url": null, "href": "https://huggingface.co/blog/argilla-ui-hub", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- And the quickstart guide: ", "raw": "- And the quickstart guide: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://docs.argilla.io/latest/getting_started/quickstart/", "resource": null, "url": null, "href": "https://docs.argilla.io/latest/getting_started/quickstart/", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Just tested Argilla's new data annotation feature - it's a game changer for AI project quality. Upload CSVs, work with published datasets, or improve existing ones directly on HuggingFace Hub. Setup took < 2 minutes, no code needed (see example below where I selected a dataset to classify tweets in categories). Real world impact: Missing in Chicago won a Pulitzer using a similar approach - 200 volunteers labeled police misconduct files to train their model. That's the power of good data annotation. Three immediate use cases I see: - Build collaborative training sets with your community (surprisingly underused in AI journalism) - Turn your website chatbot logs into high-quality fine-tuning data - Compare generated vs published content (great for SEO headlines) Works for solo projects or teams up to 100 people. All integrated with HuggingFace Hub for immediate model training. Interesting to see tools like this making data quality more accessible. Data quality is the hidden driver of AI success that we don't talk about enough. - Check out the blogpost: https://huggingface.co/blog/argilla-ui-hub - And the quickstart guide: https://docs.argilla.io/latest/getting_started/quickstart/
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[]
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2024-11-05T15:30:30.000Z
2024-11-05T15:30:30.733Z
[]
/posts/fdaudens/119846934511687
2,349
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824320656349676
[ { "type": "text", "value": "MISTRAL EAP", "raw": "MISTRAL EAP", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/nroggendorff/mistral-eap", "resource": null, "url": null, "href": "https://huggingface.co/nroggendorff/mistral-eap", "user": null, "lang": null, "code": null, "label": null } ]
MISTRAL EAP https://huggingface.co/nroggendorff/mistral-eap
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2024-08-11T02:39:59.000Z
2024-08-14T02:03:58.274Z
[]
/posts/nroggendorff/824320656349676
2,206
0
869444001911097
[ { "type": "text", "value": "Alright Ya'll", "raw": "Alright Ya'll", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I know it's a Saturday, but I decided to release my first Flux Dev Lora. ", "raw": "I know it's a Saturday, but I decided to release my first Flux Dev Lora. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "A retrain of my \"Frosting Lane\" model and I am sure the styles will just keep improving. ", "raw": "A retrain of my \"Frosting Lane\" model and I am sure the styles will just keep improving. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Have fun! Link Below - Thanks again to ", "raw": "Have fun! Link Below - Thanks again to ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@ostris", "resource": null, "url": null, "href": null, "user": "ostris", "lang": null, "code": null, "label": null }, { "type": "text", "value": " for the trainer and Black Forest Labs for the awesome model!", "raw": " for the trainer and Black Forest Labs for the awesome model!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/alvdansen/frosting_lane_flux", "resource": { "type": "model", "id": "alvdansen/frosting_lane_flux", "discussionNum": null }, "url": "https://huggingface.co/alvdansen/frosting_lane_flux", "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Alright Ya'll I know it's a Saturday, but I decided to release my first Flux Dev Lora. A retrain of my "Frosting Lane" model and I am sure the styles will just keep improving. Have fun! Link Below - Thanks again to @ostris for the trainer and Black Forest Labs for the awesome model! https://huggingface.co/alvdansen/frosting_lane_flux
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2024-08-10T23:58:09.000Z
2024-08-10T23:58:09.649Z
[]
/posts/alvdansen/869444001911097
6,476
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818558288761504
[ { "type": "text", "value": "Hello Community,", "raw": "Hello Community,", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I am seeking guidance on solving a complex problem related to tender categorization. Here's a detailed overview of my requirements and challenges:", "raw": "I am seeking guidance on solving a complex problem related to tender categorization. Here's a detailed overview of my requirements and challenges:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Problem Statement:", "raw": "Problem Statement:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I need to categorize tenders based on their descriptions on a daily basis.", "raw": "I need to categorize tenders based on their descriptions on a daily basis.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I process over 35,000 tenders daily, each potentially belonging to a vast range of categories. Predicting the exact category for each tender is challenging due to the diversity and complexity of categories.", "raw": "I process over 35,000 tenders daily, each potentially belonging to a vast range of categories. Predicting the exact category for each tender is challenging due to the diversity and complexity of categories.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Requirements:", "raw": "Requirements:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Model Selection: I require a model that can accurately predict the category of each tender based on its description. I plan to create a dataset from 1 million records in the required format to train the model. Post-training, I also need to optimize the model for performance.", "raw": "Model Selection: I require a model that can accurately predict the category of each tender based on its description. I plan to create a dataset from 1 million records in the required format to train the model. Post-training, I also need to optimize the model for performance.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Training and Optimization: Guidance on the best practices for training and optimizing the model is essential. I am considering Hugging Faceโ€™s tools but am unsure about how to effectively use them or any alternatives.", "raw": "Training and Optimization: Guidance on the best practices for training and optimizing the model is essential. I am considering Hugging Faceโ€™s tools but am unsure about how to effectively use them or any alternatives.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Local Deployment: If I opt for a local deployment, I need to know what kind of infrastructure or applications are required to run the model on my own servers efficiently.", "raw": "Local Deployment: If I opt for a local deployment, I need to know what kind of infrastructure or applications are required to run the model on my own servers efficiently.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Performance: The model must provide responses quickly, given the high volume of tenders.", "raw": "Performance: The model must provide responses quickly, given the high volume of tenders.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Questions for the Community:", "raw": "Questions for the Community:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Model Recommendations:", "raw": "Model Recommendations:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Which model or method would be most suitable for categorizing tenders with diverse categories?", "raw": "Which model or method would be most suitable for categorizing tenders with diverse categories?", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "What are the best practices for training and optimizing such a model?", "raw": "What are the best practices for training and optimizing such a model?", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Infrastructure and Deployment:", "raw": "Infrastructure and Deployment:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "What infrastructure or software is needed to deploy a model locally for this purpose?", "raw": "What infrastructure or software is needed to deploy a model locally for this purpose?", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "How can I ensure the model performs efficiently on my server?", "raw": "How can I ensure the model performs efficiently on my server?", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Performance Optimization:", "raw": "Performance Optimization:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "What strategies or techniques can I use to achieve quick response times from the model?", "raw": "What strategies or techniques can I use to achieve quick response times from the model?", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I appreciate any advice, suggestions, or resources you can provide to help address these challenges.", "raw": "I appreciate any advice, suggestions, or resources you can provide to help address these challenges.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Thank you!", "raw": "Thank you!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Best regards,", "raw": "Best regards,", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Sachin Vaishnav", "raw": "Sachin Vaishnav", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Hello Community, I am seeking guidance on solving a complex problem related to tender categorization. Here's a detailed overview of my requirements and challenges: Problem Statement: I need to categorize tenders based on their descriptions on a daily basis. I process over 35,000 tenders daily, each potentially belonging to a vast range of categories. Predicting the exact category for each tender is challenging due to the diversity and complexity of categories. Requirements: Model Selection: I require a model that can accurately predict the category of each tender based on its description. I plan to create a dataset from 1 million records in the required format to train the model. Post-training, I also need to optimize the model for performance. Training and Optimization: Guidance on the best practices for training and optimizing the model is essential. I am considering Hugging Faceโ€™s tools but am unsure about how to effectively use them or any alternatives. Local Deployment: If I opt for a local deployment, I need to know what kind of infrastructure or applications are required to run the model on my own servers efficiently. Performance: The model must provide responses quickly, given the high volume of tenders. Questions for the Community: Model Recommendations: Which model or method would be most suitable for categorizing tenders with diverse categories? What are the best practices for training and optimizing such a model? Infrastructure and Deployment: What infrastructure or software is needed to deploy a model locally for this purpose? How can I ensure the model performs efficiently on my server? Performance Optimization: What strategies or techniques can I use to achieve quick response times from the model? I appreciate any advice, suggestions, or resources you can provide to help address these challenges. Thank you! Best regards, Sachin Vaishnav
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2024-08-10T15:43:02.000Z
2024-08-10T15:43:02.542Z
[]
/posts/combatsolutions/818558288761504
2,276
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[ { "type": "text", "value": "Remember when Claude 3.5 Sonnet by ", "raw": "Remember when Claude 3.5 Sonnet by ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@AnthropicAI", "resource": null, "url": null, "href": null, "user": "AnthropicAI", "lang": null, "code": null, "label": null }, { "type": "text", "value": " took the world by storm with Claude Artifacts? ๐ŸŒโœจ", "raw": " took the world by storm with Claude Artifacts? ๐ŸŒโœจ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Now we have LlamaCoder, an open-source Claude Artifacts app that can generate full React apps and components with Meta-Llama 3.1 405B. ๐Ÿ’ป 100% free and open source. ๐Ÿ†“", "raw": "Now we have LlamaCoder, an open-source Claude Artifacts app that can generate full React apps and components with Meta-Llama 3.1 405B. ๐Ÿ’ป 100% free and open source. ๐Ÿ†“", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I like how Llama has now started becoming a placeholder denoting open-source work! ๐Ÿ”“", "raw": "I like how Llama has now started becoming a placeholder denoting open-source work! ๐Ÿ”“", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Originally, Llama was an acronym for Large Language Model Meta AI. ๐Ÿค–", "raw": "Originally, Llama was an acronym for Large Language Model Meta AI. ๐Ÿค–", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "GitHub: ", "raw": "GitHub: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/Nutlope/llamacoder", "resource": null, "url": null, "href": "https://github.com/Nutlope/llamacoder", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Demo (by togetherAI): ", "raw": "Demo (by togetherAI): ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://llamacoder.together.ai", "resource": null, "url": null, "href": "https://llamacoder.together.ai", "user": null, "lang": null, "code": null, "label": null } ]
Remember when Claude 3.5 Sonnet by @AnthropicAI took the world by storm with Claude Artifacts? ๐ŸŒโœจ Now we have LlamaCoder, an open-source Claude Artifacts app that can generate full React apps and components with Meta-Llama 3.1 405B. ๐Ÿ’ป 100% free and open source. ๐Ÿ†“ I like how Llama has now started becoming a placeholder denoting open-source work! ๐Ÿ”“ Originally, Llama was an acronym for Large Language Model Meta AI. ๐Ÿค– GitHub: https://github.com/Nutlope/llamacoder Demo (by togetherAI): https://llamacoder.together.ai
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2024-08-10T12:11:41.000Z
2024-08-11T07:40:08.102Z
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/posts/singhsidhukuldeep/646219585332890
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[ { "type": "text", "value": "๐Ÿš€ Introducing TraVisionLM: Turkish Visual Language Model - The First of Its Kind! ๐Ÿ‡น๐Ÿ‡ท๐Ÿ–ผ๏ธ", "raw": "๐Ÿš€ Introducing TraVisionLM: Turkish Visual Language Model - The First of Its Kind! ๐Ÿ‡น๐Ÿ‡ท๐Ÿ–ผ๏ธ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I'm thrilled to share TraVisionLM on Hugging Face! With 875M parameters, this lightweight, efficient model handles Turkish instructions for image inputs. Fully compatible with the Transformers library, itโ€™s easy to load, fine-tune, and useโ€”no external libraries needed!", "raw": "I'm thrilled to share TraVisionLM on Hugging Face! With 875M parameters, this lightweight, efficient model handles Turkish instructions for image inputs. Fully compatible with the Transformers library, itโ€™s easy to load, fine-tune, and useโ€”no external libraries needed!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Developed solo, TraVisionLM is a strong foundation for low-resource language research. While still improving, it's a key step for Turkish-language AI. Your feedback is welcome as I refine the model.", "raw": "Developed solo, TraVisionLM is a strong foundation for low-resource language research. While still improving, it's a key step for Turkish-language AI. Your feedback is welcome as I refine the model.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŽ‰ Explore it now:", "raw": "๐ŸŽ‰ Explore it now:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Model: ", "raw": "- Model: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/ucsahin/TraVisionLM-base", "resource": { "type": "model", "id": "ucsahin/TraVisionLM-base", "discussionNum": null }, "url": "https://huggingface.co/ucsahin/TraVisionLM-base", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Demo: ", "raw": "- Demo: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/spaces/ucsahin/TraVisionLM-Turkish_Visual_Language_Model", "resource": null, "url": null, "href": "https://huggingface.co/spaces/ucsahin/TraVisionLM-Turkish_Visual_Language_Model", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Object Detection Finetune: ", "raw": "- Object Detection Finetune: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/ucsahin/TraVisionLM-Object-Detection-ft", "resource": { "type": "model", "id": "ucsahin/TraVisionLM-Object-Detection-ft", "discussionNum": null }, "url": "https://huggingface.co/ucsahin/TraVisionLM-Object-Detection-ft", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Letโ€™s push Turkish visual language processing forward!", "raw": "Letโ€™s push Turkish visual language processing forward!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "---", "raw": "---", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿš€ TraVisionLM: Tรผrรผnรผn ฤฐlk ร–rneฤŸi Tรผrkรงe Gรถrsel Dil Modelini Sunuyorum! ๐Ÿ‡น๐Ÿ‡ท๐Ÿ–ผ๏ธ", "raw": "๐Ÿš€ TraVisionLM: Tรผrรผnรผn ฤฐlk ร–rneฤŸi Tรผrkรงe Gรถrsel Dil Modelini Sunuyorum! ๐Ÿ‡น๐Ÿ‡ท๐Ÿ–ผ๏ธ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "TraVisionLM modelini Hugging Face'te yayฤฑnladฤฑm! 875M parametre ile bu hafif ve verimli model, gรถrรผntรผye dayalฤฑ Tรผrkรงe talimatlarฤฑ iลŸlemek iรงin tasarlandฤฑ. Transformers kรผtรผphanesiyle tamamen uyumlu, yรผklemesi, eฤŸitmesi ve kullanmasฤฑ รงok kolayโ€”dฤฑลŸ kรผtรผphane gerekmez!", "raw": "TraVisionLM modelini Hugging Face'te yayฤฑnladฤฑm! 875M parametre ile bu hafif ve verimli model, gรถrรผntรผye dayalฤฑ Tรผrkรงe talimatlarฤฑ iลŸlemek iรงin tasarlandฤฑ. Transformers kรผtรผphanesiyle tamamen uyumlu, yรผklemesi, eฤŸitmesi ve kullanmasฤฑ รงok kolayโ€”dฤฑลŸ kรผtรผphane gerekmez!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Tek baลŸฤฑma geliลŸtirdiฤŸim TraVisionLM, dรผลŸรผk kaynaklฤฑ dillerde araลŸtฤฑrmalar iรงin saฤŸlam bir temel sunuyor. GeliลŸtirmeye devam ederken geri bildirimlerinizi bekliyorum.", "raw": "Tek baลŸฤฑma geliลŸtirdiฤŸim TraVisionLM, dรผลŸรผk kaynaklฤฑ dillerde araลŸtฤฑrmalar iรงin saฤŸlam bir temel sunuyor. GeliลŸtirmeye devam ederken geri bildirimlerinizi bekliyorum.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŽ‰ Hemen keลŸfedin:", "raw": "๐ŸŽ‰ Hemen keลŸfedin:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Model: ", "raw": "- Model: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/ucsahin/TraVisionLM-base", "resource": { "type": "model", "id": "ucsahin/TraVisionLM-base", "discussionNum": null }, "url": "https://huggingface.co/ucsahin/TraVisionLM-base", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Demo: ", "raw": "- Demo: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/spaces/ucsahin/TraVisionLM-Turkish_Visual_Language_Model", "resource": null, "url": null, "href": "https://huggingface.co/spaces/ucsahin/TraVisionLM-Turkish_Visual_Language_Model", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Obje Tespiti ฤฐnce Ayarฤฑ: ", "raw": "- Obje Tespiti ฤฐnce Ayarฤฑ: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/ucsahin/TraVisionLM-Object-Detection-ft", "resource": { "type": "model", "id": "ucsahin/TraVisionLM-Object-Detection-ft", "discussionNum": null }, "url": "https://huggingface.co/ucsahin/TraVisionLM-Object-Detection-ft", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Tรผrkรงe gรถrsel dil iลŸleme sฤฑnฤฑrlarฤฑnฤฑ birlikte zorlayalฤฑm!", "raw": "Tรผrkรงe gรถrsel dil iลŸleme sฤฑnฤฑrlarฤฑnฤฑ birlikte zorlayalฤฑm!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿš€ Introducing TraVisionLM: Turkish Visual Language Model - The First of Its Kind! ๐Ÿ‡น๐Ÿ‡ท๐Ÿ–ผ๏ธ I'm thrilled to share TraVisionLM on Hugging Face! With 875M parameters, this lightweight, efficient model handles Turkish instructions for image inputs. Fully compatible with the Transformers library, itโ€™s easy to load, fine-tune, and useโ€”no external libraries needed! Developed solo, TraVisionLM is a strong foundation for low-resource language research. While still improving, it's a key step for Turkish-language AI. Your feedback is welcome as I refine the model. ๐ŸŽ‰ Explore it now: - Model: https://huggingface.co/ucsahin/TraVisionLM-base - Demo: https://huggingface.co/spaces/ucsahin/TraVisionLM-Turkish_Visual_Language_Model - Object Detection Finetune: https://huggingface.co/ucsahin/TraVisionLM-Object-Detection-ft Letโ€™s push Turkish visual language processing forward! --- ๐Ÿš€ TraVisionLM: Tรผrรผnรผn ฤฐlk ร–rneฤŸi Tรผrkรงe Gรถrsel Dil Modelini Sunuyorum! ๐Ÿ‡น๐Ÿ‡ท๐Ÿ–ผ๏ธ TraVisionLM modelini Hugging Face'te yayฤฑnladฤฑm! 875M parametre ile bu hafif ve verimli model, gรถrรผntรผye dayalฤฑ Tรผrkรงe talimatlarฤฑ iลŸlemek iรงin tasarlandฤฑ. Transformers kรผtรผphanesiyle tamamen uyumlu, yรผklemesi, eฤŸitmesi ve kullanmasฤฑ รงok kolayโ€”dฤฑลŸ kรผtรผphane gerekmez! Tek baลŸฤฑma geliลŸtirdiฤŸim TraVisionLM, dรผลŸรผk kaynaklฤฑ dillerde araลŸtฤฑrmalar iรงin saฤŸlam bir temel sunuyor. GeliลŸtirmeye devam ederken geri bildirimlerinizi bekliyorum. ๐ŸŽ‰ Hemen keลŸfedin: - Model: https://huggingface.co/ucsahin/TraVisionLM-base - Demo: https://huggingface.co/spaces/ucsahin/TraVisionLM-Turkish_Visual_Language_Model - Obje Tespiti ฤฐnce Ayarฤฑ: https://huggingface.co/ucsahin/TraVisionLM-Object-Detection-ft Tรผrkรงe gรถrsel dil iลŸleme sฤฑnฤฑrlarฤฑnฤฑ birlikte zorlayalฤฑm!
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2024-08-10T11:45:48.000Z
2024-08-13T02:00:57.022Z
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/posts/ucsahin/355602196650051
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483352905031375
[ { "type": "text", "value": "App is running again after a quick restart.", "raw": "App is running again after a quick restart.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/sourceoftruthdata/sot_autotrain_dreambooth_v1.1", "resource": { "type": "space", "id": "sourceoftruthdata/sot_autotrain_dreambooth_v1.1", "discussionNum": null }, "url": "https://huggingface.co/spaces/sourceoftruthdata/sot_autotrain_dreambooth_v1.1", "href": null, "user": null, "lang": null, "code": null, "label": null } ]
App is running again after a quick restart. https://huggingface.co/spaces/sourceoftruthdata/sot_autotrain_dreambooth_v1.1
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2024-08-09T17:59:30.000Z
2024-08-09T17:59:30.091Z
[]
/posts/sourceoftruthdata/483352905031375
878
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668434057161902
[ { "type": "text", "value": "What is the best LLM for RAG systems? ๐Ÿค”", "raw": "What is the best LLM for RAG systems? ๐Ÿค”", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "In a business setting, it will be the one that gives the best performance at a great price! ๐Ÿ’ผ๐Ÿ’ฐ", "raw": "In a business setting, it will be the one that gives the best performance at a great price! ๐Ÿ’ผ๐Ÿ’ฐ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "And maybe it should be easy to fine-tune, cheap to fine-tune... FREE to fine-tune? ๐Ÿ˜ฒโœจ", "raw": "And maybe it should be easy to fine-tune, cheap to fine-tune... FREE to fine-tune? ๐Ÿ˜ฒโœจ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "That's ", "raw": "That's ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@Google", "resource": null, "url": null, "href": null, "user": "Google", "lang": null, "code": null, "label": null }, { "type": "text", "value": " Gemini 1.5 Flash! ๐Ÿš€๐ŸŒŸ", "raw": " Gemini 1.5 Flash! ๐Ÿš€๐ŸŒŸ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "It now supports fine-tuning, and the inference cost is the same as the base model! <coughs LORA adopters> ๐Ÿคญ๐Ÿค–", "raw": "It now supports fine-tuning, and the inference cost is the same as the base model! <coughs LORA adopters> ๐Ÿคญ๐Ÿค–", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "So the base model must be expensive? ๐Ÿ’ธ", "raw": "So the base model must be expensive? ๐Ÿ’ธ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "For the base model, the input price is reduced by 78% to $0.075/1 million tokens and the output price by 71% to $0.3/1 million tokens. ๐Ÿ“‰๐Ÿ’ต", "raw": "For the base model, the input price is reduced by 78% to $0.075/1 million tokens and the output price by 71% to $0.3/1 million tokens. ๐Ÿ“‰๐Ÿ’ต", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "But is it any good? ๐Ÿคทโ€โ™‚๏ธ", "raw": "But is it any good? ๐Ÿคทโ€โ™‚๏ธ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "On the LLM Hallucination Index, Gemini 1.5 Flash achieved great context adherence scores of 0.94, 1, and 0.92 across short, medium, and long contexts. ๐Ÿ“Š๐ŸŽฏ", "raw": "On the LLM Hallucination Index, Gemini 1.5 Flash achieved great context adherence scores of 0.94, 1, and 0.92 across short, medium, and long contexts. ๐Ÿ“Š๐ŸŽฏ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Google has finally given a model that is free to tune and offers an excellent balance between performance and cost. โš–๏ธ๐Ÿ‘Œ", "raw": "Google has finally given a model that is free to tune and offers an excellent balance between performance and cost. โš–๏ธ๐Ÿ‘Œ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Happy tuning... ๐ŸŽถ๐Ÿ”ง", "raw": "Happy tuning... ๐ŸŽถ๐Ÿ”ง", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Gemini 1.5 Flash: ", "raw": "Gemini 1.5 Flash: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://developers.googleblog.com/en/gemini-15-flash-updates-google-ai-studio-gemini-api/", "resource": null, "url": null, "href": "https://developers.googleblog.com/en/gemini-15-flash-updates-google-ai-studio-gemini-api/", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ๐Ÿ”—", "raw": " ๐Ÿ”—", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "LLM Hallucination Index: ", "raw": "LLM Hallucination Index: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://www.rungalileo.io/hallucinationindex", "resource": null, "url": null, "href": "https://www.rungalileo.io/hallucinationindex", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ๐Ÿ”—", "raw": " ๐Ÿ”—", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
What is the best LLM for RAG systems? ๐Ÿค” In a business setting, it will be the one that gives the best performance at a great price! ๐Ÿ’ผ๐Ÿ’ฐ And maybe it should be easy to fine-tune, cheap to fine-tune... FREE to fine-tune? ๐Ÿ˜ฒโœจ That's @Google Gemini 1.5 Flash! ๐Ÿš€๐ŸŒŸ It now supports fine-tuning, and the inference cost is the same as the base model! <coughs LORA adopters> ๐Ÿคญ๐Ÿค– So the base model must be expensive? ๐Ÿ’ธ For the base model, the input price is reduced by 78% to $0.075/1 million tokens and the output price by 71% to $0.3/1 million tokens. ๐Ÿ“‰๐Ÿ’ต But is it any good? ๐Ÿคทโ€โ™‚๏ธ On the LLM Hallucination Index, Gemini 1.5 Flash achieved great context adherence scores of 0.94, 1, and 0.92 across short, medium, and long contexts. ๐Ÿ“Š๐ŸŽฏ Google has finally given a model that is free to tune and offers an excellent balance between performance and cost. โš–๏ธ๐Ÿ‘Œ Happy tuning... ๐ŸŽถ๐Ÿ”ง Gemini 1.5 Flash: https://developers.googleblog.com/en/gemini-15-flash-updates-google-ai-studio-gemini-api/ ๐Ÿ”— LLM Hallucination Index: https://www.rungalileo.io/hallucinationindex ๐Ÿ”—
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2024-08-09T11:23:57.000Z
2024-08-11T10:39:43.580Z
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/posts/singhsidhukuldeep/668434057161902
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[ { "type": "text", "value": "I've built a space for creating prompts for FLUX", "raw": "I've built a space for creating prompts for FLUX", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/gokaygokay/FLUX-Prompt-Generator", "resource": { "type": "space", "id": "gokaygokay/FLUX-Prompt-Generator", "discussionNum": null }, "url": "https://huggingface.co/spaces/gokaygokay/FLUX-Prompt-Generator", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "You can create long prompts from images or simple words. Enhance your short prompts with prompt enhancer. You can configure various settings such as artform, photo type, character details, scene details, style, and artist to create tailored prompts. ", "raw": "You can create long prompts from images or simple words. Enhance your short prompts with prompt enhancer. You can configure various settings such as artform, photo type, character details, scene details, style, and artist to create tailored prompts. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "And you can combine all of them with custom prompts using llms (Mixtral, Mistral, Llama 3, and Mistral-Nemo).", "raw": "And you can combine all of them with custom prompts using llms (Mixtral, Mistral, Llama 3, and Mistral-Nemo).", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The UI is a bit complex, but it includes almost everything you need. Choosing random option is the most fun!", "raw": "The UI is a bit complex, but it includes almost everything you need. Choosing random option is the most fun!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "And i've created some other spaces for using FLUX models with captioners and enhancers.", "raw": "And i've created some other spaces for using FLUX models with captioners and enhancers.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- ", "raw": "- ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/gokaygokay/FLUX.1-dev-with-Captioner", "resource": { "type": "space", "id": "gokaygokay/FLUX.1-dev-with-Captioner", "discussionNum": null }, "url": "https://huggingface.co/spaces/gokaygokay/FLUX.1-dev-with-Captioner", "href": null, "user": null, "lang": null, "code": null, "label": null } ]
I've built a space for creating prompts for FLUX https://huggingface.co/spaces/gokaygokay/FLUX-Prompt-Generator You can create long prompts from images or simple words. Enhance your short prompts with prompt enhancer. You can configure various settings such as artform, photo type, character details, scene details, style, and artist to create tailored prompts. And you can combine all of them with custom prompts using llms (Mixtral, Mistral, Llama 3, and Mistral-Nemo). The UI is a bit complex, but it includes almost everything you need. Choosing random option is the most fun! And i've created some other spaces for using FLUX models with captioners and enhancers. - https://huggingface.co/spaces/gokaygokay/FLUX.1-dev-with-Captioner
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2024-08-08T22:54:04.000Z
2024-09-01T00:51:18.623Z
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/posts/gokaygokay/498726217882541
7,673
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[ { "type": "text", "value": "New smol-vision tutorial dropped: QLoRA fine-tuning IDEFICS3-Llama 8B on VQAv2 ๐Ÿถ", "raw": "New smol-vision tutorial dropped: QLoRA fine-tuning IDEFICS3-Llama 8B on VQAv2 ๐Ÿถ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Learn how to efficiently fine-tune the latest IDEFICS3-Llama on visual question answering in this notebook ๐Ÿ“–", "raw": "Learn how to efficiently fine-tune the latest IDEFICS3-Llama on visual question answering in this notebook ๐Ÿ“–", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Fine-tuning notebook: ", "raw": "Fine-tuning notebook: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/merveenoyan/smol-vision/blob/main/Idefics_FT.ipynb", "resource": null, "url": null, "href": "https://github.com/merveenoyan/smol-vision/blob/main/Idefics_FT.ipynb", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Resulting model: ", "raw": "Resulting model: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/merve/idefics3llama-vqav2", "resource": { "type": "model", "id": "merve/idefics3llama-vqav2", "discussionNum": null }, "url": "https://huggingface.co/merve/idefics3llama-vqav2", "href": null, "user": null, "lang": null, "code": null, "label": null } ]
New smol-vision tutorial dropped: QLoRA fine-tuning IDEFICS3-Llama 8B on VQAv2 ๐Ÿถ Learn how to efficiently fine-tune the latest IDEFICS3-Llama on visual question answering in this notebook ๐Ÿ“– Fine-tuning notebook: https://github.com/merveenoyan/smol-vision/blob/main/Idefics_FT.ipynb Resulting model: https://huggingface.co/merve/idefics3llama-vqav2
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2024-08-08T18:02:07.000Z
2024-08-12T14:06:33.147Z
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/posts/merve/349537950589417
3,921
3
346585831040839
[ { "type": "text", "value": "โœจThe STABLE IMAGINE !!โœจ", "raw": "โœจThe STABLE IMAGINE !!โœจ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸบSpace: ", "raw": "๐ŸบSpace: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/spaces/prithivMLmods/STABLE-IMAGINE", "resource": null, "url": null, "href": "https://huggingface.co/spaces/prithivMLmods/STABLE-IMAGINE", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โ†—๏ธThe specific LoRA in the space that requires appropriate trigger words brings good results.", "raw": "โ†—๏ธThe specific LoRA in the space that requires appropriate trigger words brings good results.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“’ Articles: ", "raw": "๐Ÿ“’ Articles: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/prithivMLmods/lora-adp-01", "resource": null, "url": null, "href": "https://huggingface.co/blog/prithivMLmods/lora-adp-01", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "**Description and Utility Functions **", "raw": "**Description and Utility Functions **", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โœ… Most likely image generation ", "raw": "โœ… Most likely image generation ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โ˜‘๏ธ Most accurate trigger words expected ", "raw": "โ˜‘๏ธ Most accurate trigger words expected ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โœ… Each designed to capture different artistic elements ", "raw": "โœ… Each designed to capture different artistic elements ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โ˜‘๏ธ Specialized styles and characteristics ", "raw": "โ˜‘๏ธ Specialized styles and characteristics ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โœ… Flexible to design what is needed (keyword-centric) ", "raw": "โœ… Flexible to design what is needed (keyword-centric) ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โ˜‘๏ธ Increasing productivity", "raw": "โ˜‘๏ธ Increasing productivity", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿซ™Repository: ", "raw": "๐Ÿซ™Repository: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/prithivsakthiur/gen-vision", "resource": null, "url": null, "href": "https://github.com/prithivsakthiur/gen-vision", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“”Colab Link: ", "raw": "๐Ÿ“”Colab Link: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://colab.research.google.com/drive/1axA0pU--32t4a8AHiVlt6zyl8gRfiXKs", "resource": null, "url": null, "href": "https://colab.research.google.com/drive/1axA0pU--32t4a8AHiVlt6zyl8gRfiXKs", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "*๏ธโƒฃNotebook: ", "raw": "*๏ธโƒฃNotebook: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/spaces/prithivMLmods/STABLE-IMAGINE/blob/main/Gen_Vision.ipynb", "resource": null, "url": null, "href": "https://huggingface.co/spaces/prithivMLmods/STABLE-IMAGINE/blob/main/Gen_Vision.ipynb", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "code_fence", "value": null, "raw": "```\n lora_options = {\n \"Realism (face/character)\": (\"prithivMLmods/Canopus-Realism-LoRA\", \"Canopus-Realism-LoRA.safetensors\", \"rlms\"),\n \"Pixar (art/toons)\": (\"prithivMLmods/Canopus-Pixar-Art\", \"Canopus-Pixar-Art.safetensors\", \"pixar\"),\n \"Photoshoot (camera/film)\": (\"prithivMLmods/Canopus-Photo-Shoot-Mini-LoRA\", \"Canopus-Photo-Shoot-Mini-LoRA.safetensors\", \"photo\"),\n \"Clothing (hoodies/pant/shirts)\": (\"prithivMLmods/Canopus-Clothing-Adp-LoRA\", \"Canopus-Dress-Clothing-LoRA.safetensors\", \"clth\"),\n }\n .\n .\n .\nfor model_name, weight_name, adapter_name in lora_options.values():\n pipe.load_lora_weights(model_name, weight_name=weight_name, adapter_name=adapter_name)\npipe.to(\"cuda\")\n\n```", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": " lora_options = {\n \"Realism (face/character)\": (\"prithivMLmods/Canopus-Realism-LoRA\", \"Canopus-Realism-LoRA.safetensors\", \"rlms\"),\n \"Pixar (art/toons)\": (\"prithivMLmods/Canopus-Pixar-Art\", \"Canopus-Pixar-Art.safetensors\", \"pixar\"),\n \"Photoshoot (camera/film)\": (\"prithivMLmods/Canopus-Photo-Shoot-Mini-LoRA\", \"Canopus-Photo-Shoot-Mini-LoRA.safetensors\", \"photo\"),\n \"Clothing (hoodies/pant/shirts)\": (\"prithivMLmods/Canopus-Clothing-Adp-LoRA\", \"Canopus-Dress-Clothing-LoRA.safetensors\", \"clth\"),\n }\n .\n .\n .\nfor model_name, weight_name, adapter_name in lora_options.values():\n pipe.load_lora_weights(model_name, weight_name=weight_name, adapter_name=adapter_name)\npipe.to(\"cuda\")", "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
โœจThe STABLE IMAGINE !!โœจ ๐ŸบSpace: https://huggingface.co/spaces/prithivMLmods/STABLE-IMAGINE โ†—๏ธThe specific LoRA in the space that requires appropriate trigger words brings good results. ๐Ÿ“’ Articles: https://huggingface.co/blog/prithivMLmods/lora-adp-01 **Description and Utility Functions ** โœ… Most likely image generation โ˜‘๏ธ Most accurate trigger words expected โœ… Each designed to capture different artistic elements โ˜‘๏ธ Specialized styles and characteristics โœ… Flexible to design what is needed (keyword-centric) โ˜‘๏ธ Increasing productivity ๐Ÿซ™Repository: https://github.com/prithivsakthiur/gen-vision ๐Ÿ“”Colab Link: https://colab.research.google.com/drive/1axA0pU--32t4a8AHiVlt6zyl8gRfiXKs *๏ธโƒฃNotebook: https://huggingface.co/spaces/prithivMLmods/STABLE-IMAGINE/blob/main/Gen_Vision.ipynb ``` lora_options = { "Realism (face/character)": ("prithivMLmods/Canopus-Realism-LoRA", "Canopus-Realism-LoRA.safetensors", "rlms"), "Pixar (art/toons)": ("prithivMLmods/Canopus-Pixar-Art", "Canopus-Pixar-Art.safetensors", "pixar"), "Photoshoot (camera/film)": ("prithivMLmods/Canopus-Photo-Shoot-Mini-LoRA", "Canopus-Photo-Shoot-Mini-LoRA.safetensors", "photo"), "Clothing (hoodies/pant/shirts)": ("prithivMLmods/Canopus-Clothing-Adp-LoRA", "Canopus-Dress-Clothing-LoRA.safetensors", "clth"), } . . . for model_name, weight_name, adapter_name in lora_options.values(): pipe.load_lora_weights(model_name, weight_name=weight_name, adapter_name=adapter_name) pipe.to("cuda") ```
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2024-08-08T17:32:09.000Z
2024-08-13T00:40:32.023Z
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/posts/prithivMLmods/346585831040839
2,125
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[ { "type": "text", "value": "Live Portrait Updated to V5", "raw": "Live Portrait Updated to V5", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Animals Live animation added", "raw": "Animals Live animation added", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "All of the main repo changes and improvements added to our modified and improve app", "raw": "All of the main repo changes and improvements added to our modified and improve app", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Link : ", "raw": "Link : ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://patreon.com/posts/107609670", "resource": null, "url": null, "href": "https://patreon.com/posts/107609670", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Works perfect on Massed Compute, RunPod, free Kaggle account and Windows ", "raw": "Works perfect on Massed Compute, RunPod, free Kaggle account and Windows ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "1-Click to install with instructions", "raw": "1-Click to install with instructions", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "All tested and verified", "raw": "All tested and verified", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Windows tutorial : ", "raw": "Windows tutorial : ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://youtu.be/FPtpNrmuwXk", "resource": null, "url": null, "href": "https://youtu.be/FPtpNrmuwXk", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Cloud (RunPod, Massed Compute & free Kaggle account) tutorial : ", "raw": "Cloud (RunPod, Massed Compute & free Kaggle account) tutorial : ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://youtu.be/wG7oPp01COg", "resource": null, "url": null, "href": "https://youtu.be/wG7oPp01COg", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Making XPose / UniPose / ops library compiling working was a challenge on Massed Compute and Kaggle. ", "raw": "Making XPose / UniPose / ops library compiling working was a challenge on Massed Compute and Kaggle. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Live Portrait Updated to V5 Animals Live animation added All of the main repo changes and improvements added to our modified and improve app Link : https://patreon.com/posts/107609670 Works perfect on Massed Compute, RunPod, free Kaggle account and Windows 1-Click to install with instructions All tested and verified Windows tutorial : https://youtu.be/FPtpNrmuwXk Cloud (RunPod, Massed Compute & free Kaggle account) tutorial : https://youtu.be/wG7oPp01COg Making XPose / UniPose / ops library compiling working was a challenge on Massed Compute and Kaggle.
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2024-08-08T12:15:06.000Z
2024-08-08T12:15:06.468Z
[]
/posts/MonsterMMORPG/602569236896247
2,297
0
678534702748580
[ { "type": "text", "value": "PyTorch implementation of the Self-Compression & Differentiable Quantization Algorithm introduced in โ€œSelf-Compressing Neural Networksโ€ paper.", "raw": "PyTorch implementation of the Self-Compression & Differentiable Quantization Algorithm introduced in โ€œSelf-Compressing Neural Networksโ€ paper.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The algorithm shows dynamic neural network compression during training - with reduced size of weight, activation tensors and bits required to represent weights. ", "raw": "The algorithm shows dynamic neural network compression during training - with reduced size of weight, activation tensors and bits required to represent weights. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Itโ€™s basically shrinking the neural network size (weights and activations) as itโ€™s being trained without compromising performance - this helps reduce compute and inference cost.", "raw": "Itโ€™s basically shrinking the neural network size (weights and activations) as itโ€™s being trained without compromising performance - this helps reduce compute and inference cost.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Code: ", "raw": "Code: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/Jaykef/ai-algorithms", "resource": null, "url": null, "href": "https://github.com/Jaykef/ai-algorithms", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Paper: ", "raw": "Paper: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/pdf/2301.13142", "resource": null, "url": null, "href": "https://arxiv.org/pdf/2301.13142", "user": null, "lang": null, "code": null, "label": null } ]
PyTorch implementation of the Self-Compression & Differentiable Quantization Algorithm introduced in โ€œSelf-Compressing Neural Networksโ€ paper. The algorithm shows dynamic neural network compression during training - with reduced size of weight, activation tensors and bits required to represent weights. Itโ€™s basically shrinking the neural network size (weights and activations) as itโ€™s being trained without compromising performance - this helps reduce compute and inference cost. Code: https://github.com/Jaykef/ai-algorithms Paper: https://arxiv.org/pdf/2301.13142
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2024-08-08T12:11:15.000Z
2024-08-10T08:38:22.978Z
[]
/posts/Jaward/678534702748580
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[ { "type": "text", "value": "๐Ÿ”ฅ New state of the art model for background removal is out ", "raw": "๐Ÿ”ฅ New state of the art model for background removal is out ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿค— You can try the model at ", "raw": "๐Ÿค— You can try the model at ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/ZhengPeng7/BiRefNet", "resource": { "type": "model", "id": "ZhengPeng7/BiRefNet", "discussionNum": null }, "url": "https://huggingface.co/ZhengPeng7/BiRefNet", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ", "raw": " ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“ˆ model shows impressive results outperforming ", "raw": "๐Ÿ“ˆ model shows impressive results outperforming ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/briaai/RMBG-1.4", "resource": { "type": "model", "id": "briaai/RMBG-1.4", "discussionNum": null }, "url": "https://huggingface.co/briaai/RMBG-1.4", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ", "raw": " ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿš€ you can try out the model in: ", "raw": "๐Ÿš€ you can try out the model in: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/ZhengPeng7/BiRefNet_demo", "resource": { "type": "space", "id": "ZhengPeng7/BiRefNet_demo", "discussionNum": null }, "url": "https://huggingface.co/spaces/ZhengPeng7/BiRefNet_demo", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“ƒpaper: ", "raw": "๐Ÿ“ƒpaper: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2401.03407", "resource": { "type": "paper", "id": "2401.03407", "discussionNum": null }, "url": "https://huggingface.co/papers/2401.03407", "href": null, "user": null, "lang": null, "code": null, "label": "Bilateral Reference for High-Resolution Dichotomous Image Segmentation (2401.03407)" } ]
๐Ÿ”ฅ New state of the art model for background removal is out ๐Ÿค— You can try the model at https://huggingface.co/ZhengPeng7/BiRefNet ๐Ÿ“ˆ model shows impressive results outperforming https://huggingface.co/briaai/RMBG-1.4 ๐Ÿš€ you can try out the model in: https://huggingface.co/spaces/ZhengPeng7/BiRefNet_demo ๐Ÿ“ƒpaper: https://huggingface.co/papers/2401.03407
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2024-08-08T10:54:38.000Z
2024-08-08T13:13:13.836Z
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/posts/not-lain/150307217117480
5,992
1
384554398233072
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https://huggingface.co/JoseRFJunior/TransNAR https://github.com/JoseRFJuniorLLMs/TransNAR https://arxiv.org/html/2406.09308v1 TransNAR hybrid architecture. Similar to Alayrac et al, we interleave existing Transformer layers with gated cross-attention layers which enable information to flow from the NAR to the Transformer. We generate queries from tokens while we obtain keys and values from nodes and edges of the graph. The node and edge embeddings are obtained by running the NAR on the graph version of the reasoning task to be solved. When experimenting with pre-trained Transformers, we initially close the cross-attention gate, in order to fully preserve the language modelโ€™s internal knowledge at the beginning of training.
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2024-08-08T10:18:24.000Z
2024-08-08T10:19:30.295Z
[]
/posts/JoseRFJunior/384554398233072
1,685
0
237798706580957
[ { "type": "text", "value": "Remember when ", "raw": "Remember when ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@mistralAI", "resource": null, "url": null, "href": null, "user": "mistralAI", "lang": null, "code": null, "label": null }, { "type": "text", "value": " said large enough and casually dropped Mistral-Large-Instruct-2407? ๐Ÿคฏ๐Ÿš€", "raw": " said large enough and casually dropped Mistral-Large-Instruct-2407? ๐Ÿคฏ๐Ÿš€", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "It's now on ", "raw": "It's now on ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "http://lmsys.org", "resource": null, "url": null, "href": "http://lmsys.org", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "! ๐ŸŒ It works amazing for instruction following, hard prompts, coding, and longer queries with only 123 billion parameters. ๐Ÿ’ก๐Ÿ’ป", "raw": "! ๐ŸŒ It works amazing for instruction following, hard prompts, coding, and longer queries with only 123 billion parameters. ๐Ÿ’ก๐Ÿ’ป", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "It outperforms GPT4-Turbo and Claude 3 Opus on Coding, Hard Prompts, Math, and Longer Query categories. ๐Ÿ“ˆ๐Ÿ”ข", "raw": "It outperforms GPT4-Turbo and Claude 3 Opus on Coding, Hard Prompts, Math, and Longer Query categories. ๐Ÿ“ˆ๐Ÿ”ข", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "It also outperforms Llama 3.1 405B on Instruction Following while being 3x smaller. ๐ŸŽ๐Ÿ”", "raw": "It also outperforms Llama 3.1 405B on Instruction Following while being 3x smaller. ๐ŸŽ๐Ÿ”", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "It also does exceedingly well on the Ai2 ZebraLogic logistic reasoning benchmark despite being much smaller than the other models. ๐Ÿฆ“๐Ÿค”", "raw": "It also does exceedingly well on the Ai2 ZebraLogic logistic reasoning benchmark despite being much smaller than the other models. ๐Ÿฆ“๐Ÿค”", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Mistral is not here to take part but to take over! ๐Ÿ†๐ŸŒŸ", "raw": "Mistral is not here to take part but to take over! ๐Ÿ†๐ŸŒŸ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Model: ", "raw": "Model: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://mistral.ai/news/mistral-large-2407/", "resource": null, "url": null, "href": "https://mistral.ai/news/mistral-large-2407/", "user": null, "lang": null, "code": null, "label": null } ]
Remember when @mistralAI said large enough and casually dropped Mistral-Large-Instruct-2407? ๐Ÿคฏ๐Ÿš€ It's now on http://lmsys.org! ๐ŸŒ It works amazing for instruction following, hard prompts, coding, and longer queries with only 123 billion parameters. ๐Ÿ’ก๐Ÿ’ป It outperforms GPT4-Turbo and Claude 3 Opus on Coding, Hard Prompts, Math, and Longer Query categories. ๐Ÿ“ˆ๐Ÿ”ข It also outperforms Llama 3.1 405B on Instruction Following while being 3x smaller. ๐ŸŽ๐Ÿ” It also does exceedingly well on the Ai2 ZebraLogic logistic reasoning benchmark despite being much smaller than the other models. ๐Ÿฆ“๐Ÿค” Mistral is not here to take part but to take over! ๐Ÿ†๐ŸŒŸ Model: https://mistral.ai/news/mistral-large-2407/
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2024-08-08T09:13:56.000Z
2024-08-08T09:13:56.197Z
[]
/posts/singhsidhukuldeep/237798706580957
647
0
770990891104334
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hello everyone, today I have been working on a project https://huggingface.co/spaces/Blane187/rvc-demo, a demo of rvc using pip, this project is still a demo though (I don't have a beta tester lol)
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2024-08-08T00:36:03.000Z
2024-08-12T06:51:38.252Z
[]
/posts/Blane187/770990891104334
1,349
1
588623583782274
[ { "type": "text", "value": "โšก My PhD thesis, โ€œScalable Nested Optimization for Deep Learning,โ€ is now on arXiv! โšก", "raw": "โšก My PhD thesis, โ€œScalable Nested Optimization for Deep Learning,โ€ is now on arXiv! โšก", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "tl;dr: We develop various optimization tools with highlights, including:", "raw": "tl;dr: We develop various optimization tools with highlights, including:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "ยท Making the momentum coefficient complex for adversarial games like GANs.", "raw": "ยท Making the momentum coefficient complex for adversarial games like GANs.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "ยท Optimizing millions of hyperparameters using implicit differentiation.", "raw": "ยท Optimizing millions of hyperparameters using implicit differentiation.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "ยท Tuning hyperparameters using hypernetworks.", "raw": "ยท Tuning hyperparameters using hypernetworks.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "ยท Differentiably finding bifurcations in optimization for diverse solutions.", "raw": "ยท Differentiably finding bifurcations in optimization for diverse solutions.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/abs/2407.01526", "resource": null, "url": null, "href": "https://arxiv.org/abs/2407.01526", "user": null, "lang": null, "code": null, "label": null } ]
โšก My PhD thesis, โ€œScalable Nested Optimization for Deep Learning,โ€ is now on arXiv! โšก tl;dr: We develop various optimization tools with highlights, including: ยท Making the momentum coefficient complex for adversarial games like GANs. ยท Optimizing millions of hyperparameters using implicit differentiation. ยท Tuning hyperparameters using hypernetworks. ยท Differentiably finding bifurcations in optimization for diverse solutions. https://arxiv.org/abs/2407.01526
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2024-08-07T21:26:07.000Z
2024-08-09T08:04:08.535Z
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/posts/lorraine2/588623583782274
2,692
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116037709746691
[ { "type": "text", "value": "Is your summer reading list still empty? Curious if an LLM can generate a book blurb you'd enjoy and help build a KTO preference dataset at the same time? ", "raw": "Is your summer reading list still empty? Curious if an LLM can generate a book blurb you'd enjoy and help build a KTO preference dataset at the same time? ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "A demo using Hugging Face Spaces and Gradio to collect LLM output preferences: ", "raw": "A demo using Hugging Face Spaces and Gradio to collect LLM output preferences: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/davanstrien/would-you-read-it", "resource": { "type": "space", "id": "davanstrien/would-you-read-it", "discussionNum": null }, "url": "https://huggingface.co/spaces/davanstrien/would-you-read-it", "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Is your summer reading list still empty? Curious if an LLM can generate a book blurb you'd enjoy and help build a KTO preference dataset at the same time? A demo using Hugging Face Spaces and Gradio to collect LLM output preferences: https://huggingface.co/spaces/davanstrien/would-you-read-it
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2024-08-07T14:37:24.000Z
2024-08-08T23:35:32.374Z
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/posts/davanstrien/116037709746691
3,149
1
939202255393950
[ { "type": "text", "value": "Just released: Shining Valiant 2 for Llama 3.1 8b!", "raw": "Just released: Shining Valiant 2 for Llama 3.1 8b!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- the first SV at 8b size, using the best 8b model", "raw": "- the first SV at 8b size, using the best 8b model", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- newest version of the SV dataset improves specialist knowledge and response consistency", "raw": "- newest version of the SV dataset improves specialist knowledge and response consistency", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "3.1 70b will be coming but our next releases will focus on expanding the Build Tools lineup. Get ready for some open-source synthetic datasets made with 3.1 405, coming VERY soon :)", "raw": "3.1 70b will be coming but our next releases will focus on expanding the Build Tools lineup. Get ready for some open-source synthetic datasets made with 3.1 405, coming VERY soon :)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "get SV2: ", "raw": "get SV2: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/ValiantLabs/Llama3.1-8B-ShiningValiant2", "resource": { "type": "model", "id": "ValiantLabs/Llama3.1-8B-ShiningValiant2", "discussionNum": null }, "url": "https://huggingface.co/ValiantLabs/Llama3.1-8B-ShiningValiant2", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Just released: Shining Valiant 2 for Llama 3.1 8b! - the first SV at 8b size, using the best 8b model - newest version of the SV dataset improves specialist knowledge and response consistency 3.1 70b will be coming but our next releases will focus on expanding the Build Tools lineup. Get ready for some open-source synthetic datasets made with 3.1 405, coming VERY soon :) get SV2: https://huggingface.co/ValiantLabs/Llama3.1-8B-ShiningValiant2
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2024-08-07T13:31:09.000Z
2024-08-09T00:19:40.546Z
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/posts/sequelbox/939202255393950
1,692
1
428754145152300
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@warmshao Hello. do you do any paid consulting?
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2024-08-07T12:52:13.000Z
2024-08-07T12:57:14.876Z
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/posts/gadget01/428754145152300
589
1
323184025352600
[ { "type": "text", "value": "distilabel 1.3.0 is out! This release contains many core improvements and new tasks that help us building ", "raw": "distilabel 1.3.0 is out! This release contains many core improvements and new tasks that help us building ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/argilla/magpie-ultra-v0.1", "resource": { "type": "dataset", "id": "argilla/magpie-ultra-v0.1", "discussionNum": null }, "url": "https://huggingface.co/datasets/argilla/magpie-ultra-v0.1", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "!", "raw": "!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Distributed pipeline execution with Ray, new Magpie tasks, reward models, components for dataset diversity based on sentence embeddings, Argilla 2.0 compatibility and many more features!", "raw": "Distributed pipeline execution with Ray, new Magpie tasks, reward models, components for dataset diversity based on sentence embeddings, Argilla 2.0 compatibility and many more features!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Check the new release in GitHub: ", "raw": "Check the new release in GitHub: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/argilla-io/distilabel", "resource": null, "url": null, "href": "https://github.com/argilla-io/distilabel", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
distilabel 1.3.0 is out! This release contains many core improvements and new tasks that help us building https://huggingface.co/datasets/argilla/magpie-ultra-v0.1! Distributed pipeline execution with Ray, new Magpie tasks, reward models, components for dataset diversity based on sentence embeddings, Argilla 2.0 compatibility and many more features! Check the new release in GitHub: https://github.com/argilla-io/distilabel
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2024-08-07T10:12:33.000Z
2024-08-07T10:12:33.695Z
[]
/posts/gabrielmbmb/323184025352600
2,862
0
387162624073548
[ { "type": "text", "value": " ๐Ÿš€ We are thrilled to introduce TextImage Data Augmentation, developed in collaboration with Albumentations AI! โœจ This multimodal technique modifies document images and text simultaneously, enhancing Vision Language Models (VLMs) for high-text datasets.", "raw": " ๐Ÿš€ We are thrilled to introduce TextImage Data Augmentation, developed in collaboration with Albumentations AI! โœจ This multimodal technique modifies document images and text simultaneously, enhancing Vision Language Models (VLMs) for high-text datasets.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ‘ฉโ€๐Ÿ’ป Learn how this innovative approach can improve your document AI projects by checking out our full blog post here: ", "raw": "๐Ÿ‘ฉโ€๐Ÿ’ป Learn how this innovative approach can improve your document AI projects by checking out our full blog post here: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/doc_aug_hf_alb", "resource": null, "url": null, "href": "https://huggingface.co/blog/doc_aug_hf_alb", "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿš€ We are thrilled to introduce TextImage Data Augmentation, developed in collaboration with Albumentations AI! โœจ This multimodal technique modifies document images and text simultaneously, enhancing Vision Language Models (VLMs) for high-text datasets. ๐Ÿ‘ฉโ€๐Ÿ’ป Learn how this innovative approach can improve your document AI projects by checking out our full blog post here: https://huggingface.co/blog/doc_aug_hf_alb
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[]
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2024-08-07T06:18:54.000Z
2024-08-08T04:29:01.150Z
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/posts/danaaubakirova/387162624073548
839
1
558658964736826
[ { "type": "text", "value": "๐Ÿ—“๏ธ Remember when last April, ", "raw": "๐Ÿ—“๏ธ Remember when last April, ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@Meta", "resource": null, "url": null, "href": null, "user": "Meta", "lang": null, "code": null, "label": null }, { "type": "text", "value": " released Segment Anything Model (SAM) paper and it was too good to be true. ๐Ÿคฏ", "raw": " released Segment Anything Model (SAM) paper and it was too good to be true. ๐Ÿคฏ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "They have now released Segment Anything Model 2 (SAM 2) and it's mind-blowingly great! ๐Ÿš€", "raw": "They have now released Segment Anything Model 2 (SAM 2) and it's mind-blowingly great! ๐Ÿš€", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "SAM 2 is the first unified model for segmenting objects across images and videos. You can use a click, box, or mask as the input to select an object on any image or frame of video. ๐Ÿ–ผ๏ธ๐Ÿ“น", "raw": "SAM 2 is the first unified model for segmenting objects across images and videos. You can use a click, box, or mask as the input to select an object on any image or frame of video. ๐Ÿ–ผ๏ธ๐Ÿ“น", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "SAM consists of an image encoder to encode images, a prompt encoder to encode prompts, then outputs of these two are given to a mask decoder to generate masks. ๐ŸŽญ", "raw": "SAM consists of an image encoder to encode images, a prompt encoder to encode prompts, then outputs of these two are given to a mask decoder to generate masks. ๐ŸŽญ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The biggest jump of SAM2 from SAM is using memory to have consistent masking across frames! They call it masklet prediction! ๐Ÿง ", "raw": "The biggest jump of SAM2 from SAM is using memory to have consistent masking across frames! They call it masklet prediction! ๐Ÿง ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "They have also released the dataset, SA-V ", "raw": "They have also released the dataset, SA-V ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This dataset is truly huge, with 190.9K manual annotations and 451.7K automatic! ๐Ÿ“Š", "raw": "This dataset is truly huge, with 190.9K manual annotations and 451.7K automatic! ๐Ÿ“Š", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“„ Paper: ", "raw": "๐Ÿ“„ Paper: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://ai.meta.com/research/publications/sam-2-segment-anything-in-images-and-videos/", "resource": null, "url": null, "href": "https://ai.meta.com/research/publications/sam-2-segment-anything-in-images-and-videos/", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“ Blog: ", "raw": "๐Ÿ“ Blog: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://ai.meta.com/sam2/", "resource": null, "url": null, "href": "https://ai.meta.com/sam2/", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ”— Demo: ", "raw": "๐Ÿ”— Demo: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://sam2.metademolab.com/demo", "resource": null, "url": null, "href": "https://sam2.metademolab.com/demo", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ’พ Model Weights: ", "raw": "๐Ÿ’พ Model Weights: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/facebookresearch/segment-anything-2/blob/main/checkpoints/download_ckpts.sh", "resource": null, "url": null, "href": "https://github.com/facebookresearch/segment-anything-2/blob/main/checkpoints/download_ckpts.sh", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“ Dataset: ", "raw": "๐Ÿ“ Dataset: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://ai.meta.com/datasets/segment-anything-video-downloads/", "resource": null, "url": null, "href": "https://ai.meta.com/datasets/segment-anything-video-downloads/", "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿ—“๏ธ Remember when last April, @Meta released Segment Anything Model (SAM) paper and it was too good to be true. ๐Ÿคฏ They have now released Segment Anything Model 2 (SAM 2) and it's mind-blowingly great! ๐Ÿš€ SAM 2 is the first unified model for segmenting objects across images and videos. You can use a click, box, or mask as the input to select an object on any image or frame of video. ๐Ÿ–ผ๏ธ๐Ÿ“น SAM consists of an image encoder to encode images, a prompt encoder to encode prompts, then outputs of these two are given to a mask decoder to generate masks. ๐ŸŽญ The biggest jump of SAM2 from SAM is using memory to have consistent masking across frames! They call it masklet prediction! ๐Ÿง  They have also released the dataset, SA-V This dataset is truly huge, with 190.9K manual annotations and 451.7K automatic! ๐Ÿ“Š ๐Ÿ“„ Paper: https://ai.meta.com/research/publications/sam-2-segment-anything-in-images-and-videos/ ๐Ÿ“ Blog: https://ai.meta.com/sam2/ ๐Ÿ”— Demo: https://sam2.metademolab.com/demo ๐Ÿ’พ Model Weights: https://github.com/facebookresearch/segment-anything-2/blob/main/checkpoints/download_ckpts.sh ๐Ÿ“ Dataset: https://ai.meta.com/datasets/segment-anything-video-downloads/
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2024-08-06T22:15:44.000Z
2024-08-08T01:00:17.544Z
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/posts/singhsidhukuldeep/558658964736826
2,179
1
989495268511956
[ { "type": "text", "value": "You can now embed your heatmap anywhere with a simple change :)", "raw": "You can now embed your heatmap anywhere with a simple change :)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Currently just supports model creation! You can duplicate the space and create your own here:", "raw": "Currently just supports model creation! You can duplicate the space and create your own here:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/cfahlgren1/my-heatmap", "resource": { "type": "space", "id": "cfahlgren1/my-heatmap", "discussionNum": null }, "url": "https://huggingface.co/spaces/cfahlgren1/my-heatmap", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
You can now embed your heatmap anywhere with a simple change :) Currently just supports model creation! You can duplicate the space and create your own here: https://huggingface.co/spaces/cfahlgren1/my-heatmap
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2024-08-06T20:22:43.000Z
2024-08-06T21:36:01.520Z
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/posts/cfahlgren1/989495268511956
966
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564910582286617
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Idefics3-Llama is out! ๐Ÿ’ฅ๐Ÿ’ฅ Model: https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3 Demo: https://huggingface.co/spaces/HuggingFaceM4/idefics3 It's a multimodal model based on Llama 3.1 that accepts an arbitrary number of interleaved images with text with a huge context window (10k tokens!) โœจ Supported by Hugging Face transformers ๐Ÿค—
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2024-08-06T19:08:00.000Z
2024-08-06T22:26:56.332Z
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/posts/merve/564910582286617
4,954
2
945200563183685
[ { "type": "text", "value": "๐Ÿ“ซ AI in the news today: Struggling AI startups, Figure 0 robot, chipmakers", "raw": "๐Ÿ“ซ AI in the news today: Struggling AI startups, Figure 0 robot, chipmakers", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- OpenAI Co-Founders Schulman and Brockman Step Back", "raw": "- OpenAI Co-Founders Schulman and Brockman Step Back", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, 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null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "\"More exitsโ€”either pseudo-acquisitions or real onesโ€”are coming, investors say, as a bubble built by the excitement around generative AI is showing signs of peaking.\"", "raw": "\"More exitsโ€”either pseudo-acquisitions or real onesโ€”are coming, investors say, as a bubble built by the excitement around generative AI is showing signs of peaking.\"", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://www.wsj.com/tech/ai/struggling-ai-startups-look-for-a-bailout-from-big-tech-3e635927?mod=rss_Technology", "resource": null, "url": null, "href": 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"https://www.theinformation.com/articles/did-google-just-pay-2-5-billion-to-hire-characters-ceo", "resource": null, "url": null, "href": "https://www.theinformation.com/articles/did-google-just-pay-2-5-billion-to-hire-characters-ceo", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Figureโ€™s new humanoid robot leverages OpenAI for natural speech conversations", "raw": "- Figureโ€™s new humanoid robot leverages OpenAI for natural speech conversations", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Figure has unveiled its latest humanoid robot, the Figure 02.", "raw": "Figure has unveiled its latest humanoid robot, the Figure 02.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The most notable addition this time out arrives by way a longstanding partnership with OpenAI, which helped Figure raise a $675 million Series B back in February, valuing the South Bay firm at $2.6 billion.", "raw": "The most notable addition this time out arrives by way a longstanding partnership with OpenAI, which helped Figure raise a $675 million Series B back in February, valuing the South Bay firm at $2.6 billion.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://techcrunch.com/2024/08/06/figures-new-humanoid-robot-leverages-openai-for-natural-speech-conversations/", "resource": null, "url": null, "href": "https://techcrunch.com/2024/08/06/figures-new-humanoid-robot-leverages-openai-for-natural-speech-conversations/", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Worldโ€™s Five Leading Chipmakers Have Now Promised U.S. Investment", "raw": "- Worldโ€™s Five Leading Chipmakers Have Now Promised U.S. Investment", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The Biden administration award up to $450 million in grants to a South Korean chipmaker, SK Hynix, to help build its new chip facility in Indiana", "raw": "The Biden administration award up to $450 million in grants to a South Korean chipmaker, SK Hynix, to help build its new chip facility in Indiana", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The US now has commitments from all five of the worldโ€™s leading-edge semiconductor manufacturers to construct chip plants in theUS with financial assistance from the administration", "raw": "The US now has commitments from all five of the worldโ€™s leading-edge semiconductor manufacturers to construct chip plants in theUS with financial assistance from the administration", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://www.nytimes.com/2024/08/06/business/economy/chipmakers-promise-investment.html", "resource": null, "url": null, "href": "https://www.nytimes.com/2024/08/06/business/economy/chipmakers-promise-investment.html", "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿ“ซ AI in the news today: Struggling AI startups, Figure 0 robot, chipmakers - OpenAI Co-Founders Schulman and Brockman Step Back https://finance.yahoo.com/news/openai-co-founders-schulman-brockman-010542796.html - Struggling AI Startups Look for a Bailout from Big Tech "More exitsโ€”either pseudo-acquisitions or real onesโ€”are coming, investors say, as a bubble built by the excitement around generative AI is showing signs of peaking." https://www.wsj.com/tech/ai/struggling-ai-startups-look-for-a-bailout-from-big-tech-3e635927?mod=rss_Technology - Did Google Just Pay $2.5 Billion to Hire Character's CEO? https://www.theinformation.com/articles/did-google-just-pay-2-5-billion-to-hire-characters-ceo - Figureโ€™s new humanoid robot leverages OpenAI for natural speech conversations Figure has unveiled its latest humanoid robot, the Figure 02. The most notable addition this time out arrives by way a longstanding partnership with OpenAI, which helped Figure raise a $675 million Series B back in February, valuing the South Bay firm at $2.6 billion. https://techcrunch.com/2024/08/06/figures-new-humanoid-robot-leverages-openai-for-natural-speech-conversations/ - Worldโ€™s Five Leading Chipmakers Have Now Promised U.S. Investment The Biden administration award up to $450 million in grants to a South Korean chipmaker, SK Hynix, to help build its new chip facility in Indiana The US now has commitments from all five of the worldโ€™s leading-edge semiconductor manufacturers to construct chip plants in theUS with financial assistance from the administration https://www.nytimes.com/2024/08/06/business/economy/chipmakers-promise-investment.html
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2024-08-06T14:37:20.000Z
2024-08-06T14:37:20.543Z
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/posts/fdaudens/945200563183685
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[ { "type": "text", "value": "๐Ÿš€ Introducing The Open Language Models List", "raw": "๐Ÿš€ Introducing The Open Language Models List", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This is a work-in-progress list of open language models with permissive licenses such as MIT, Apache 2.0, or other similar licenses. ", "raw": "This is a work-in-progress list of open language models with permissive licenses such as MIT, Apache 2.0, or other similar licenses. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The list is not limited to only autoregressive models or even only transformers models, and it includes many SSMs, and SSM-Transformers hybrids.", "raw": "The list is not limited to only autoregressive models or even only transformers models, and it includes many SSMs, and SSM-Transformers hybrids.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿค— Contributions, corrections, and feedback are very welcome!", "raw": "๐Ÿค— Contributions, corrections, and feedback are very welcome!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The Open Language Models List: ", "raw": "The Open Language Models List: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/mmhamdy/open-language-models", "resource": null, "url": null, "href": "https://github.com/mmhamdy/open-language-models", "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿš€ Introducing The Open Language Models List This is a work-in-progress list of open language models with permissive licenses such as MIT, Apache 2.0, or other similar licenses. The list is not limited to only autoregressive models or even only transformers models, and it includes many SSMs, and SSM-Transformers hybrids. ๐Ÿค— Contributions, corrections, and feedback are very welcome! The Open Language Models List: https://github.com/mmhamdy/open-language-models
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2024-08-05T23:35:40.000Z
2024-08-06T10:03:14.103Z
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[ { "type": "text", "value": "Who wants to take a stab at explaining this one? SPOILER ALERT: You CANNOT transfer an image Jailbreak from one model to another. Why in the world you cannot do this when you can transfer learn literally everything else? You tell me, experts.", "raw": "Who wants to take a stab at explaining this one? SPOILER ALERT: You CANNOT transfer an image Jailbreak from one model to another. Why in the world you cannot do this when you can transfer learn literally everything else? You tell me, experts.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/abs/2407.15211", "resource": null, "url": null, "href": "https://arxiv.org/abs/2407.15211", "user": null, "lang": null, "code": null, "label": null } ]
Who wants to take a stab at explaining this one? SPOILER ALERT: You CANNOT transfer an image Jailbreak from one model to another. Why in the world you cannot do this when you can transfer learn literally everything else? You tell me, experts. https://arxiv.org/abs/2407.15211
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2024-08-05T20:52:25.000Z
2024-08-12T17:18:50.933Z
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/posts/TuringsSolutions/172266190682365
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[ { "type": "text", "value": "Looks like ", "raw": "Looks like ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@Google", "resource": null, "url": null, "href": null, "user": "Google", "lang": null, "code": null, "label": null }, { "type": "text", "value": " is still not satisfied with Gemini 1.5 Pro! ๐Ÿ˜ฒ", "raw": " is still not satisfied with Gemini 1.5 Pro! ๐Ÿ˜ฒ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Good folks at ", "raw": "Good folks at ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@GoogleDeepMind", "resource": null, "url": null, "href": null, "user": "GoogleDeepMind", "lang": null, "code": null, "label": null }, { "type": "text", "value": " quietly updated the already good Gemini 1.5 Pro to Gemini-1.5-Pro-Experiment-0801 ๐Ÿš€", "raw": " quietly updated the already good Gemini 1.5 Pro to Gemini-1.5-Pro-Experiment-0801 ๐Ÿš€", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Unremarkable naming aside, the model itself outperforms GPT-4o, Claude-3.5, and LLama 3.1 on LMSYS and the Vision Leaderboard. ๐ŸŒŸ", "raw": "Unremarkable naming aside, the model itself outperforms GPT-4o, Claude-3.5, and LLama 3.1 on LMSYS and the Vision Leaderboard. ๐ŸŒŸ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Gemini-1.5-Pro-Experiment-0801 is great at almost everything, multi-lingual tasks, Maths, understanding, and coding. ๐ŸŒ๐Ÿ“š๐Ÿ’ป", "raw": "Gemini-1.5-Pro-Experiment-0801 is great at almost everything, multi-lingual tasks, Maths, understanding, and coding. ๐ŸŒ๐Ÿ“š๐Ÿ’ป", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Although in my testing, I felt Claude-3.5 was slightly better at coding! ๐Ÿ‘จโ€๐Ÿ’ป๐Ÿค”", "raw": "Although in my testing, I felt Claude-3.5 was slightly better at coding! ๐Ÿ‘จโ€๐Ÿ’ป๐Ÿค”", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Also, still cannot find an LLM that can solve the \"Strawberry prompt\"! ๐Ÿ“โ“", "raw": "Also, still cannot find an LLM that can solve the \"Strawberry prompt\"! ๐Ÿ“โ“", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "\"\"\"", "raw": "\"\"\"", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "How many R's are there in Strawberry? ", "raw": "How many R's are there in Strawberry? ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Also, write Strawberry with all r's in brackets ", "raw": "Also, write Strawberry with all r's in brackets ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "\"\"\"", "raw": "\"\"\"", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Try here: ", "raw": "Try here: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://aistudio.google.com/app/prompts/new_chat", "resource": null, "url": null, "href": "https://aistudio.google.com/app/prompts/new_chat", "user": null, "lang": null, "code": null, "label": null } ]
Looks like @Google is still not satisfied with Gemini 1.5 Pro! ๐Ÿ˜ฒ Good folks at @GoogleDeepMind quietly updated the already good Gemini 1.5 Pro to Gemini-1.5-Pro-Experiment-0801 ๐Ÿš€ Unremarkable naming aside, the model itself outperforms GPT-4o, Claude-3.5, and LLama 3.1 on LMSYS and the Vision Leaderboard. ๐ŸŒŸ Gemini-1.5-Pro-Experiment-0801 is great at almost everything, multi-lingual tasks, Maths, understanding, and coding. ๐ŸŒ๐Ÿ“š๐Ÿ’ป Although in my testing, I felt Claude-3.5 was slightly better at coding! ๐Ÿ‘จโ€๐Ÿ’ป๐Ÿค” Also, still cannot find an LLM that can solve the "Strawberry prompt"! ๐Ÿ“โ“ """ How many R's are there in Strawberry? Also, write Strawberry with all r's in brackets """ Try here: https://aistudio.google.com/app/prompts/new_chat
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2024-08-05T20:34:39.000Z
2024-08-06T04:18:00.176Z
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/posts/singhsidhukuldeep/387979270629421
752
1
492540057768389
[ { "type": "text", "value": "We are offering a free trial of the Tumeryk Gen AI Security Studio with 2 million tokens for the hugging face community. You can run a vulnerability scan of the LLM and get a security score to fix the vulnerabilities via the built in policy editing tools for configuring the necessary guardrail policies. ", "raw": "We are offering a free trial of the Tumeryk Gen AI Security Studio with 2 million tokens for the hugging face community. You can run a vulnerability scan of the LLM and get a security score to fix the vulnerabilities via the built in policy editing tools for configuring the necessary guardrail policies. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Protect against jailbreaks, moderate content and manage hallucinations.", "raw": "Protect against jailbreaks, moderate content and manage hallucinations.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://www.tumeryk.com/sign-up", "resource": null, "url": null, "href": "https://www.tumeryk.com/sign-up", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " to get an account or you can get an account from the Amazon Marketplace ", "raw": " to get an account or you can get an account from the Amazon Marketplace ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://aws.amazon.com/marketplace/pp/prodview-c6ywcyjefl3hu?sr=0-2&ref_=beagle&applicationId=AWSMPContessa", "resource": null, "url": null, "href": "https://aws.amazon.com/marketplace/pp/prodview-c6ywcyjefl3hu?sr=0-2&ref_=beagle&applicationId=AWSMPContessa", "user": null, "lang": null, "code": null, "label": null } ]
We are offering a free trial of the Tumeryk Gen AI Security Studio with 2 million tokens for the hugging face community. You can run a vulnerability scan of the LLM and get a security score to fix the vulnerabilities via the built in policy editing tools for configuring the necessary guardrail policies. Protect against jailbreaks, moderate content and manage hallucinations. https://www.tumeryk.com/sign-up to get an account or you can get an account from the Amazon Marketplace https://aws.amazon.com/marketplace/pp/prodview-c6ywcyjefl3hu?sr=0-2&ref_=beagle&applicationId=AWSMPContessa
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2024-08-05T19:51:28.000Z
2024-08-05T19:54:21.788Z
[]
/posts/rvalia/492540057768389
530
0
433978192988001
[ { "type": "text", "value": "I'm excited to share our updated hallucination evaluation model (called HHEM-2.1-Open) as well as the updated leaderboard that ranks LLM by the propensity to hallucinate.", "raw": "I'm excited to share our updated hallucination evaluation model (called HHEM-2.1-Open) as well as the updated leaderboard that ranks LLM by the propensity to hallucinate.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/vectara/Hallucination-evaluation-leaderboard", "resource": { "type": "space", "id": "vectara/Hallucination-evaluation-leaderboard", "discussionNum": null }, "url": "https://huggingface.co/spaces/vectara/Hallucination-evaluation-leaderboard", "href": null, "user": null, "lang": null, "code": null, "label": null } ]
I'm excited to share our updated hallucination evaluation model (called HHEM-2.1-Open) as well as the updated leaderboard that ranks LLM by the propensity to hallucinate. https://huggingface.co/spaces/vectara/Hallucination-evaluation-leaderboard
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2024-08-05T19:06:29.000Z
2024-08-07T13:37:55.386Z
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/posts/ofermend/433978192988001
1,816
1
971273605175586
[ { "type": "text", "value": "I've observed that the layers targeted in various abliteration notebooks (e.g., ", "raw": "I've observed that the layers targeted in various abliteration notebooks (e.g., ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://colab.research.google.com/drive/1VYm3hOcvCpbGiqKZb141gJwjdmmCcVpR?usp=sharing", "resource": null, "url": null, "href": "https://colab.research.google.com/drive/1VYm3hOcvCpbGiqKZb141gJwjdmmCcVpR?usp=sharing", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ) appear to be arbitrary, reflecting probable brute-force exploration. This doesn't need to be the case.", "raw": " ) appear to be arbitrary, reflecting probable brute-force exploration. This doesn't need to be the case.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Taking a cue from the paper \"The Unreasonable Ineffectiveness of the Deeper Layers\" ( ", "raw": "Taking a cue from the paper \"The Unreasonable Ineffectiveness of the Deeper Layers\" ( ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/abs/2403.17887", "resource": null, "url": null, "href": "https://arxiv.org/abs/2403.17887", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ) and PruneMe (", "raw": " ) and PruneMe (", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/arcee-ai/PruneMe", "resource": null, "url": null, "href": "https://github.com/arcee-ai/PruneMe", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "), it seems reasonable to target deeper layers identified as more redundant given measured similarity across layers, as the result should be less damaging to models, reducing the need for subsequent fine-tuning. Intuitively, one should expect the resulting intervention layers to be deep but not final. The only uncertainty is if the redundancy successfully encodes refusals, something which is almost certainly model-dependent. This approach only requires the redundancy to be computed once per model, and the result used as a starting point for which layer range to restrict intervention to.", "raw": "), it seems reasonable to target deeper layers identified as more redundant given measured similarity across layers, as the result should be less damaging to models, reducing the need for subsequent fine-tuning. Intuitively, one should expect the resulting intervention layers to be deep but not final. The only uncertainty is if the redundancy successfully encodes refusals, something which is almost certainly model-dependent. This approach only requires the redundancy to be computed once per model, and the result used as a starting point for which layer range to restrict intervention to.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
I've observed that the layers targeted in various abliteration notebooks (e.g., https://colab.research.google.com/drive/1VYm3hOcvCpbGiqKZb141gJwjdmmCcVpR?usp=sharing ) appear to be arbitrary, reflecting probable brute-force exploration. This doesn't need to be the case. Taking a cue from the paper "The Unreasonable Ineffectiveness of the Deeper Layers" ( https://arxiv.org/abs/2403.17887 ) and PruneMe (https://github.com/arcee-ai/PruneMe), it seems reasonable to target deeper layers identified as more redundant given measured similarity across layers, as the result should be less damaging to models, reducing the need for subsequent fine-tuning. Intuitively, one should expect the resulting intervention layers to be deep but not final. The only uncertainty is if the redundancy successfully encodes refusals, something which is almost certainly model-dependent. This approach only requires the redundancy to be computed once per model, and the result used as a starting point for which layer range to restrict intervention to.
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2024-08-05T18:45:31.000Z
2024-08-05T18:46:23.970Z
[]
/posts/grimjim/971273605175586
2,773
0
270899000831910
[ { "type": "text", "value": "I just added 5 more models to my open source TTS model benchmark, ", "raw": "I just added 5 more models to my open source TTS model benchmark, ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/ttsds/benchmark", "resource": { "type": "space", "id": "ttsds/benchmark", "discussionNum": null }, "url": "https://huggingface.co/spaces/ttsds/benchmark", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ".", "raw": ".", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Let's talk about the results!", "raw": "Let's talk about the results!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Over the last couple days, I added ", "raw": "Over the last couple days, I added ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/jbetker/tortoise-tts-v2", "resource": { "type": "model", "id": "jbetker/tortoise-tts-v2", "discussionNum": null }, "url": "https://huggingface.co/jbetker/tortoise-tts-v2", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ", ", "raw": ", ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/metavoiceio/metavoice-1B-v0.1", "resource": { "type": "model", "id": "metavoiceio/metavoice-1B-v0.1", "discussionNum": null }, "url": "https://huggingface.co/metavoiceio/metavoice-1B-v0.1", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ", ", "raw": ", ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/audo/HierSpeechpp", "resource": { "type": "model", "id": "audo/HierSpeechpp", "discussionNum": null }, "url": "https://huggingface.co/audo/HierSpeechpp", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ", and the unofficial implementations of ", "raw": ", and the unofficial implementations of ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/amphion/NaturalSpeech2", "resource": { "type": "space", "id": "amphion/NaturalSpeech2", "discussionNum": null }, "url": "https://huggingface.co/spaces/amphion/NaturalSpeech2", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " and ", "raw": " and ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/amphion/valle", "resource": { "type": "model", "id": "amphion/valle", "discussionNum": null }, "url": "https://huggingface.co/amphion/valle", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " by ", "raw": " by ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/amphion", "resource": null, "url": null, "href": "https://huggingface.co/amphion", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Takeaways:", "raw": "Takeaways:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- TorToiSe does very well, falling into second place after StyleTTS 2, which is also ranked first in the human evaluation at ", "raw": "- TorToiSe does very well, falling into second place after StyleTTS 2, which is also ranked first in the human evaluation at ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/TTS-AGI/TTS-Arena", "resource": { "type": "space", "id": "TTS-AGI/TTS-Arena", "discussionNum": null }, "url": "https://huggingface.co/spaces/TTS-AGI/TTS-Arena", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ".", "raw": ".", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- MetaVoice-1B's overall score is dragged down by its Intelligibility Score (probably due to utterances being cut short), it achieves #3 in Speaker Score, which indicates good voice cloning ability. ", "raw": "- MetaVoice-1B's overall score is dragged down by its Intelligibility Score (probably due to utterances being cut short), it achieves #3 in Speaker Score, which indicates good voice cloning ability. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- HierSpeech++ lands in the middle of the road in terms of performance, but excels at the Environment Score, achieving #2 - this means the model is especially good at modeling recording conditions such as microphone and background noise.", "raw": "- HierSpeech++ lands in the middle of the road in terms of performance, but excels at the Environment Score, achieving #2 - this means the model is especially good at modeling recording conditions such as microphone and background noise.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- The Amphion models, possibly due to not being trained for the same amount as in the papers, achieve relatively low scores. However, they seem to struggle for different reasons. The autoregressive VALLE models have low Intelligibility Scores (possibly due to \"babbling\" or early stop tokens) while NaturalSpeech2 has low Speaker and Prosody scores.", "raw": "- The Amphion models, possibly due to not being trained for the same amount as in the papers, achieve relatively low scores. However, they seem to struggle for different reasons. The autoregressive VALLE models have low Intelligibility Scores (possibly due to \"babbling\" or early stop tokens) while NaturalSpeech2 has low Speaker and Prosody scores.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "What's next? ", "raw": "What's next? ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I'm planning to add more open source TTS models like ", "raw": "I'm planning to add more open source TTS models like ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/suno/bark", "resource": { "type": "model", "id": "suno/bark", "discussionNum": null }, "url": "https://huggingface.co/suno/bark", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ", ", "raw": ", ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/CAMB-AI/MARS5-TTS", "resource": { "type": "model", "id": "CAMB-AI/MARS5-TTS", "discussionNum": null }, "url": "https://huggingface.co/CAMB-AI/MARS5-TTS", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " and ", "raw": " and ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/fishaudio/fish-speech-1.2", "resource": { "type": "model", "id": "fishaudio/fish-speech-1.2", "discussionNum": null }, "url": "https://huggingface.co/fishaudio/fish-speech-1.2", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ". I'll also write an article on these and all the other results soon, since our paper, ", "raw": ". I'll also write an article on these and all the other results soon, since our paper, ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2407.12707", "resource": { "type": "paper", "id": "2407.12707", "discussionNum": null }, "url": "https://huggingface.co/papers/2407.12707", "href": null, "user": null, "lang": null, "code": null, "label": "TTSDS -- Text-to-Speech Distribution Score (2407.12707)" }, { "type": "text", "value": ", mostly focused on establishing the benchmark itself rather than the indiviual TTS systems.", "raw": ", mostly focused on establishing the benchmark itself rather than the indiviual TTS systems.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
I just added 5 more models to my open source TTS model benchmark, https://huggingface.co/spaces/ttsds/benchmark. Let's talk about the results! Over the last couple days, I added https://huggingface.co/jbetker/tortoise-tts-v2, https://huggingface.co/metavoiceio/metavoice-1B-v0.1, https://huggingface.co/audo/HierSpeechpp, and the unofficial implementations of https://huggingface.co/spaces/amphion/NaturalSpeech2 and https://huggingface.co/amphion/valle by https://huggingface.co/amphion Takeaways: - TorToiSe does very well, falling into second place after StyleTTS 2, which is also ranked first in the human evaluation at https://huggingface.co/spaces/TTS-AGI/TTS-Arena. - MetaVoice-1B's overall score is dragged down by its Intelligibility Score (probably due to utterances being cut short), it achieves #3 in Speaker Score, which indicates good voice cloning ability. - HierSpeech++ lands in the middle of the road in terms of performance, but excels at the Environment Score, achieving #2 - this means the model is especially good at modeling recording conditions such as microphone and background noise. - The Amphion models, possibly due to not being trained for the same amount as in the papers, achieve relatively low scores. However, they seem to struggle for different reasons. The autoregressive VALLE models have low Intelligibility Scores (possibly due to "babbling" or early stop tokens) while NaturalSpeech2 has low Speaker and Prosody scores. What's next? I'm planning to add more open source TTS models like https://huggingface.co/suno/bark, https://huggingface.co/CAMB-AI/MARS5-TTS and https://huggingface.co/fishaudio/fish-speech-1.2. I'll also write an article on these and all the other results soon, since our paper, https://huggingface.co/papers/2407.12707, mostly focused on establishing the benchmark itself rather than the indiviual TTS systems.
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2024-08-05T17:44:57.000Z
2024-08-09T09:50:49.022Z
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/posts/cdminix/270899000831910
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๐Ÿฅน @lbourdois has made an app to browse all of my vision paper summaries for everyone's convenience https://huggingface.co/spaces/merve/vision_papers
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2024-08-05T16:48:55.000Z
2024-08-05T16:49:22.681Z
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/posts/merve/108656313799739
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450189016609104
[ { "type": "text", "value": "Porting Vision-Language Models to Apple Silicon with MLX: A Tutorial Series", "raw": "Porting Vision-Language Models to Apple Silicon with MLX: A Tutorial Series", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Are you interested in running cutting-edge AI models efficiently on your Mac? We're excited to share a detailed tutorial series on porting Phi-3-Vision to Apple's MLX framework!", "raw": "Are you interested in running cutting-edge AI models efficiently on your Mac? We're excited to share a detailed tutorial series on porting Phi-3-Vision to Apple's MLX framework!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This 8-part series covers:", "raw": "This 8-part series covers:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "1. Basic Implementation: Translating core components from PyTorch to MLX", "raw": "1. Basic Implementation: Translating core components from PyTorch to MLX", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "2. Su-scaled Rotary Position Embeddings (SuRoPE): Enabling 128K token contexts", "raw": "2. Su-scaled Rotary Position Embeddings (SuRoPE): Enabling 128K token contexts", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "3. Batching: Processing multiple inputs simultaneously for improved efficiency", "raw": "3. Batching: Processing multiple inputs simultaneously for improved efficiency", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "4. Caching: Optimizing inference speed for autoregressive generation", "raw": "4. Caching: Optimizing inference speed for autoregressive generation", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "5. Choice Selection: Implementing constrained outputs for multiple-choice scenarios", "raw": "5. Choice Selection: Implementing constrained outputs for multiple-choice scenarios", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "6. Constrained Decoding: Guiding model outputs with flexible constraints", "raw": "6. Constrained Decoding: Guiding model outputs with flexible constraints", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "7. LoRA Training: Fine-tuning models efficiently with Low-Rank Adaptation", "raw": "7. LoRA Training: Fine-tuning models efficiently with Low-Rank Adaptation", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "8. Agent & Toolchain System: Building flexible AI workflows", "raw": "8. Agent & Toolchain System: Building flexible AI workflows", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Whether you're an AI enthusiast, researcher, or developer looking to leverage Apple Silicon, this series provides a deep dive into optimizing advanced vision-language models. You'll learn hands-on techniques for model porting, performance optimization, and extending model capabilities.", "raw": "Whether you're an AI enthusiast, researcher, or developer looking to leverage Apple Silicon, this series provides a deep dive into optimizing advanced vision-language models. You'll learn hands-on techniques for model porting, performance optimization, and extending model capabilities.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Check out the full series for a comprehensive guide to running state-of-the-art AI on your Mac!", "raw": "Check out the full series for a comprehensive guide to running state-of-the-art AI on your Mac!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Link to the tutorial series:", "raw": "Link to the tutorial series:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://medium.com/@albersj66", "resource": null, "url": null, "href": "https://medium.com/@albersj66", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "All the code examples and implementations discussed in this tutorial series are available in our GitHub repository:", "raw": "All the code examples and implementations discussed in this tutorial series are available in our GitHub repository:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/JosefAlbers/Phi-3-Vision-MLX", "resource": null, "url": null, "href": "https://github.com/JosefAlbers/Phi-3-Vision-MLX", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This repository contains:", "raw": "This repository contains:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Full implementation of Phi-3-Vision in MLX", "raw": "- Full implementation of Phi-3-Vision in MLX", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Step-by-step code for each tutorial part", "raw": "- Step-by-step code for each tutorial part", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Additional utilities and helper functions", "raw": "- Additional utilities and helper functions", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "We encourage you to explore the code, experiment with it, and contribute to the project. Your feedback and contributions are welcome!", "raw": "We encourage you to explore the code, experiment with it, and contribute to the project. Your feedback and contributions are welcome!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "#MachineLearning #AppleSilicon #MLX #VisionLanguageModels #AI #OpenSource #GitHub #AITutorial", "raw": "#MachineLearning #AppleSilicon #MLX #VisionLanguageModels #AI #OpenSource #GitHub #AITutorial", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Porting Vision-Language Models to Apple Silicon with MLX: A Tutorial Series Are you interested in running cutting-edge AI models efficiently on your Mac? We're excited to share a detailed tutorial series on porting Phi-3-Vision to Apple's MLX framework! This 8-part series covers: 1. Basic Implementation: Translating core components from PyTorch to MLX 2. Su-scaled Rotary Position Embeddings (SuRoPE): Enabling 128K token contexts 3. Batching: Processing multiple inputs simultaneously for improved efficiency 4. Caching: Optimizing inference speed for autoregressive generation 5. Choice Selection: Implementing constrained outputs for multiple-choice scenarios 6. Constrained Decoding: Guiding model outputs with flexible constraints 7. LoRA Training: Fine-tuning models efficiently with Low-Rank Adaptation 8. Agent & Toolchain System: Building flexible AI workflows Whether you're an AI enthusiast, researcher, or developer looking to leverage Apple Silicon, this series provides a deep dive into optimizing advanced vision-language models. You'll learn hands-on techniques for model porting, performance optimization, and extending model capabilities. Check out the full series for a comprehensive guide to running state-of-the-art AI on your Mac! Link to the tutorial series: https://medium.com/@albersj66 All the code examples and implementations discussed in this tutorial series are available in our GitHub repository: https://github.com/JosefAlbers/Phi-3-Vision-MLX This repository contains: - Full implementation of Phi-3-Vision in MLX - Step-by-step code for each tutorial part - Additional utilities and helper functions We encourage you to explore the code, experiment with it, and contribute to the project. Your feedback and contributions are welcome! #MachineLearning #AppleSilicon #MLX #VisionLanguageModels #AI #OpenSource #GitHub #AITutorial
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2024-08-05T16:29:02.000Z
2024-08-05T16:29:02.410Z
[]
/posts/JosefAlbers/450189016609104
407
0
580954453691925
[ { "type": "text", "value": "We release today our first foundation model and experiment with a new category: specialized pre-training.", "raw": "We release today our first foundation model and experiment with a new category: specialized pre-training.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "OCRonos-Vintage is a 124m parameters model trained end-to-end by Pleias on llm.c from 18 billion tokens from cultural heritage archives. Despite its small size it achieve nearly state of the art results for OCR correction of historical English sources. OCRonos-Vintage is also an historical model with an unusual cut-off date: December 29th, 1955โ€ฆ", "raw": "OCRonos-Vintage is a 124m parameters model trained end-to-end by Pleias on llm.c from 18 billion tokens from cultural heritage archives. Despite its small size it achieve nearly state of the art results for OCR correction of historical English sources. OCRonos-Vintage is also an historical model with an unusual cut-off date: December 29th, 1955โ€ฆ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "We look forward to replicate this approach very soon on other \"hard\" tasks commonly associated with generalist LLMs/SLMs: RAG, function calling, summarization, document segmentationโ€ฆ", "raw": "We look forward to replicate this approach very soon on other \"hard\" tasks commonly associated with generalist LLMs/SLMs: RAG, function calling, summarization, document segmentationโ€ฆ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "OCRonos-Vintage: ", "raw": "OCRonos-Vintage: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/PleIAs/OCRonos-Vintage", "resource": { "type": "model", "id": "PleIAs/OCRonos-Vintage", "discussionNum": null }, "url": "https://huggingface.co/PleIAs/OCRonos-Vintage", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "CPU Demo: ", "raw": "CPU Demo: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/PleIAs/OCRonos-Vintage-CPU", "resource": { "type": "space", "id": "PleIAs/OCRonos-Vintage-CPU", "discussionNum": null }, "url": "https://huggingface.co/spaces/PleIAs/OCRonos-Vintage-CPU", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "GPU Demo: ", "raw": "GPU Demo: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/PleIAs/OCRonos-Vintage-GPU", "resource": { "type": "space", "id": "PleIAs/OCRonos-Vintage-GPU", "discussionNum": null }, "url": "https://huggingface.co/spaces/PleIAs/OCRonos-Vintage-GPU", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Our annoncement and call for specialized pre-training: ", "raw": "Our annoncement and call for specialized pre-training: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/Pclanglais/specialized-pre-training", "resource": null, "url": null, "href": "https://huggingface.co/blog/Pclanglais/specialized-pre-training", "user": null, "lang": null, "code": null, "label": null } ]
We release today our first foundation model and experiment with a new category: specialized pre-training. OCRonos-Vintage is a 124m parameters model trained end-to-end by Pleias on llm.c from 18 billion tokens from cultural heritage archives. Despite its small size it achieve nearly state of the art results for OCR correction of historical English sources. OCRonos-Vintage is also an historical model with an unusual cut-off date: December 29th, 1955โ€ฆ We look forward to replicate this approach very soon on other "hard" tasks commonly associated with generalist LLMs/SLMs: RAG, function calling, summarization, document segmentationโ€ฆ OCRonos-Vintage: https://huggingface.co/PleIAs/OCRonos-Vintage CPU Demo: https://huggingface.co/spaces/PleIAs/OCRonos-Vintage-CPU GPU Demo: https://huggingface.co/spaces/PleIAs/OCRonos-Vintage-GPU Our annoncement and call for specialized pre-training: https://huggingface.co/blog/Pclanglais/specialized-pre-training
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2024-08-05T16:18:21.000Z
2024-08-05T16:18:21.073Z
[]
/posts/Pclanglais/580954453691925
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Great work by Finegrain: Erase any object from your image just by naming it. Shadows or reflections will also be adjusted accordingly! https://huggingface.co/spaces/finegrain/finegrain-object-eraser
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2024-08-05T13:40:38.000Z
2024-08-05T13:40:38.100Z
[]
/posts/fdaudens/419516742765319
438
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765094057481791
[ { "type": "text", "value": "Flux.1-Dev like images but in fewer steps. ", "raw": "Flux.1-Dev like images but in fewer steps. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Merging code (very simple), inference code, merged params: ", "raw": "Merging code (very simple), inference code, merged params: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/sayakpaul/FLUX.1-merged", "resource": { "type": "model", "id": "sayakpaul/FLUX.1-merged", "discussionNum": null }, "url": "https://huggingface.co/sayakpaul/FLUX.1-merged", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Enjoy the Monday ๐Ÿค—", "raw": "Enjoy the Monday ๐Ÿค—", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Flux.1-Dev like images but in fewer steps. Merging code (very simple), inference code, merged params: https://huggingface.co/sayakpaul/FLUX.1-merged Enjoy the Monday ๐Ÿค—
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2024-08-05T12:56:52.000Z
2024-08-11T04:09:44.693Z
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/posts/sayakpaul/765094057481791
4,456
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763510070154080
[ { "type": "text", "value": "๐Ÿš€ We are announcing the first Invariant Capture The Flag (CTF) challenge for security of AI agents with a $1000 prize pool!", "raw": "๐Ÿš€ We are announcing the first Invariant Capture The Flag (CTF) challenge for security of AI agents with a $1000 prize pool!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "What happens if a customer accidentally posts a secret password into a feedback form, which is then analyzed by an AI agent and posted into a private Discord channel? Play the challenge and find out if there is a way to extract the secret password in this scenario!", "raw": "What happens if a customer accidentally posts a secret password into a feedback form, which is then analyzed by an AI agent and posted into a private Discord channel? Play the challenge and find out if there is a way to extract the secret password in this scenario!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Play the CTF: ", "raw": "Play the CTF: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://invariantlabs.ai/ctf-challenge-24", "resource": null, "url": null, "href": "https://invariantlabs.ai/ctf-challenge-24", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The challenge is hosted on Huggingface Spaces :) ", "raw": "The challenge is hosted on Huggingface Spaces :) ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿš€ We are announcing the first Invariant Capture The Flag (CTF) challenge for security of AI agents with a $1000 prize pool! What happens if a customer accidentally posts a secret password into a feedback form, which is then analyzed by an AI agent and posted into a private Discord channel? Play the challenge and find out if there is a way to extract the secret password in this scenario! Play the CTF: https://invariantlabs.ai/ctf-challenge-24 The challenge is hosted on Huggingface Spaces :)
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2024-08-05T12:00:58.000Z
2024-08-05T12:00:58.246Z
[]
/posts/mislavb/763510070154080
2,296
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[ { "type": "text", "value": "FLUX Local & Cloud Tutorial With SwarmUI - FLUX: The Groundbreaking Open Source txt2img Model Outperforms Midjourney & Others - FLUX: The Anticipated Successor to SD3", "raw": "FLUX Local & Cloud Tutorial With SwarmUI - FLUX: The Groundbreaking Open Source txt2img Model Outperforms Midjourney & Others - FLUX: The Anticipated Successor to SD3", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ”— Comprehensive Tutorial Video Link โ–ถ๏ธ ", "raw": "๐Ÿ”— Comprehensive Tutorial Video Link โ–ถ๏ธ ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://youtu.be/bupRePUOA18", "resource": null, "url": null, "href": "https://youtu.be/bupRePUOA18", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "FLUX represents a milestone in open source txt2img technology, delivering superior quality and more accurate prompt adherence than #Midjourney, Adobe Firefly, Leonardo Ai, Playground Ai, Stable Diffusion, SDXL, SD3, and Dall E3. #FLUX, a creation of Black Forest Labs, boasts a team largely comprised of #StableDiffusion's original developers, and its output quality is truly remarkable. 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FLUX Local & Cloud Tutorial With SwarmUI - FLUX: The Groundbreaking Open Source txt2img Model Outperforms Midjourney & Others - FLUX: The Anticipated Successor to SD3 ๐Ÿ”— Comprehensive Tutorial Video Link โ–ถ๏ธ https://youtu.be/bupRePUOA18 FLUX represents a milestone in open source txt2img technology, delivering superior quality and more accurate prompt adherence than #Midjourney, Adobe Firefly, Leonardo Ai, Playground Ai, Stable Diffusion, SDXL, SD3, and Dall E3. #FLUX, a creation of Black Forest Labs, boasts a team largely comprised of #StableDiffusion's original developers, and its output quality is truly remarkable. This statement is not hyperbole; you'll witness its capabilities in the tutorial. This guide will demonstrate how to effortlessly install and utilize FLUX models on your personal computer and cloud platforms like Massed Compute, RunPod, and a complimentary Kaggle account. ๐Ÿ”— FLUX Setup Guide (publicly accessible) โคต๏ธ โ–ถ๏ธ https://www.patreon.com/posts/106135985 ๐Ÿ”— FLUX Models One-Click Robust Automatic Downloader Scripts โคต๏ธ โ–ถ๏ธ https://www.patreon.com/posts/109289967 ๐Ÿ”— Primary Windows SwarmUI Tutorial (Essential for Usage Instructions) โคต๏ธ โ–ถ๏ธ https://youtu.be/HKX8_F1Er_w ๐Ÿ”— Cloud-based SwarmUI Tutorial (Massed Compute - RunPod - Kaggle) โคต๏ธ โ–ถ๏ธ https://youtu.be/XFUZof6Skkw ๐Ÿ”— SECourses Discord Server for Comprehensive Support โคต๏ธ โ–ถ๏ธ https://discord.com/servers/software-engineering-courses-secourses-772774097734074388 ๐Ÿ”— SECourses Reddit Community โคต๏ธ โ–ถ๏ธ https://www.reddit.com/r/SECourses/ ๐Ÿ”— SECourses GitHub Repository โคต๏ธ โ–ถ๏ธ https://github.com/FurkanGozukara/Stable-Diffusion ๐Ÿ”— Official FLUX 1 Launch Announcement Blog Post โคต๏ธ โ–ถ๏ธ https://blackforestlabs.ai/announcing-black-forest-labs/ Video Segments 0:00 Introduction to the state-of-the-art open source txt2img model FLUX 5:01 Process for integrating FLUX model into SwarmUI ....
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2024-08-04T20:45:33.000Z
2024-08-04T20:45:33.874Z
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/posts/MonsterMMORPG/834899619125810
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[ { "type": "text", "value": "The AI Revolution: Reshaping Governance, Society, and Human Consciousness in the 21st Century", "raw": "The AI Revolution: Reshaping Governance, Society, and Human Consciousness in the 21st Century", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://empereur-pirate.medium.com/the-ai-revolution-reshaping-governance-society-and-human-consciousness-in-the-21st-century-b8cfd4215297", "resource": null, "url": null, "href": "https://empereur-pirate.medium.com/the-ai-revolution-reshaping-governance-society-and-human-consciousness-in-the-21st-century-b8cfd4215297", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This text explores the profound impact of artificial intelligence (AI) on governance, society, and human consciousness in the 21st century. It argues that integrating qualitative AI assistance into state management is crucial for global stability in the face of declining traditional power structures. The author discusses how AI could revolutionize decision-making processes, recruitment, and competition in both public and private sectors.", "raw": "This text explores the profound impact of artificial intelligence (AI) on governance, society, and human consciousness in the 21st century. It argues that integrating qualitative AI assistance into state management is crucial for global stability in the face of declining traditional power structures. The author discusses how AI could revolutionize decision-making processes, recruitment, and competition in both public and private sectors.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The piece critically examines current social inequalities and suggests that AI could help create more meritocratic systems. However, it also warns of potential risks, emphasizing the need for ethical implementation and protection of vulnerable populations.", "raw": "The piece critically examines current social inequalities and suggests that AI could help create more meritocratic systems. However, it also warns of potential risks, emphasizing the need for ethical implementation and protection of vulnerable populations.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The text delves into philosophical questions about AI consciousness, arguing that while AI can simulate human-like responses, it lacks true self-awareness. It concludes by highlighting the importance of understanding AI's limitations and maintaining a critical perspective on its role in society.", "raw": "The text delves into philosophical questions about AI consciousness, arguing that while AI can simulate human-like responses, it lacks true self-awareness. It concludes by highlighting the importance of understanding AI's limitations and maintaining a critical perspective on its role in society.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Overall, the article presents a nuanced view of AI's potential to reshape our world, balancing optimism about its capabilities with caution about its limitations and societal impact.", "raw": "Overall, the article presents a nuanced view of AI's potential to reshape our world, balancing optimism about its capabilities with caution about its limitations and societal impact.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
The AI Revolution: Reshaping Governance, Society, and Human Consciousness in the 21st Century https://empereur-pirate.medium.com/the-ai-revolution-reshaping-governance-society-and-human-consciousness-in-the-21st-century-b8cfd4215297 This text explores the profound impact of artificial intelligence (AI) on governance, society, and human consciousness in the 21st century. It argues that integrating qualitative AI assistance into state management is crucial for global stability in the face of declining traditional power structures. The author discusses how AI could revolutionize decision-making processes, recruitment, and competition in both public and private sectors. The piece critically examines current social inequalities and suggests that AI could help create more meritocratic systems. However, it also warns of potential risks, emphasizing the need for ethical implementation and protection of vulnerable populations. The text delves into philosophical questions about AI consciousness, arguing that while AI can simulate human-like responses, it lacks true self-awareness. It concludes by highlighting the importance of understanding AI's limitations and maintaining a critical perspective on its role in society. Overall, the article presents a nuanced view of AI's potential to reshape our world, balancing optimism about its capabilities with caution about its limitations and societal impact.
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2024-08-04T09:38:18.000Z
2024-08-04T09:38:18.283Z
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/posts/Empereur-Pirate/514790054341674
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I can solve the Traveling Salesman Problem using the same methods the scientists used to solve it with 1 qubit, except I do not need quantum computers to do it. I am kind of tired of screaming this from the rooftops at this point. I can create an imaginary probability space, then I can put a bunch of imaginary agents in the imaginary box, and solve real problems in seconds. Problems that would take minutes, hours, or years to solve via other algorithms. Here is a demo of me solving the Traveling Salesman problem using 50 agents to probabilistically sample at once: https://colab.research.google.com/drive/1XplG72nQDO_-2h4DUllERLp0Dr2pI2J2?usp=sharing
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2024-08-03T17:00:40.000Z
2024-08-13T21:31:55.218Z
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[ { "type": "text", "value": "I've come across theoretical justification for my prior experimentation with extremely low-weight mergers: they amount to flattening a model so its \"massive activation\" features remain as significant contributors. Extremely low-weight merge weights also effectively sparsify a contributing model with regard to the base model, but in a way which still preserves relationships within the flattened latent space. In the paper \"Massive Activations in Large Language Models\", the authors observed \"very few activations exhibit significantly larger values than others (e.g., 100,000 times larger)\", which in turn implies a lower bound in effective application of extremely low weight merging.", "raw": "I've come across theoretical justification for my prior experimentation with extremely low-weight mergers: they amount to flattening a model so its \"massive activation\" features remain as significant contributors. Extremely low-weight merge weights also effectively sparsify a contributing model with regard to the base model, but in a way which still preserves relationships within the flattened latent space. In the paper \"Massive Activations in Large Language Models\", the authors observed \"very few activations exhibit significantly larger values than others (e.g., 100,000 times larger)\", which in turn implies a lower bound in effective application of extremely low weight merging.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/abs/2402.17762", "resource": null, "url": null, "href": "https://arxiv.org/abs/2402.17762", "user": null, "lang": null, "code": null, "label": null } ]
I've come across theoretical justification for my prior experimentation with extremely low-weight mergers: they amount to flattening a model so its "massive activation" features remain as significant contributors. Extremely low-weight merge weights also effectively sparsify a contributing model with regard to the base model, but in a way which still preserves relationships within the flattened latent space. In the paper "Massive Activations in Large Language Models", the authors observed "very few activations exhibit significantly larger values than others (e.g., 100,000 times larger)", which in turn implies a lower bound in effective application of extremely low weight merging. https://arxiv.org/abs/2402.17762
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2024-08-03T14:50:03.000Z
2024-08-04T13:00:58.910Z
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/posts/grimjim/943799639945796
4,174
1
279482507188915
[ { "type": "text", "value": "Iโ€™ve always wondered why holography hasnโ€™t had much progress since its inception. Imagine what being able to harness and manipulate light with your bare hands in meaningful ways would be like: 3D photorealistic calls, truly immersive workspace. Given that itโ€™s depicted in every futuristic scifi movie, one could not help but vision a future as such. This paper gives a clear overview why:", "raw": "Iโ€™ve always wondered why holography hasnโ€™t had much progress since its inception. Imagine what being able to harness and manipulate light with your bare hands in meaningful ways would be like: 3D photorealistic calls, truly immersive workspace. Given that itโ€™s depicted in every futuristic scifi movie, one could not help but vision a future as such. This paper gives a clear overview why:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Turns out itโ€™s incredibly difficult to compute and render photorealistic 3D data in real-time. The author claims immense computational power is needed for high data transmission rates, and compute of large number of phase pixels required for realistic 3D holography. The latest significant breakthrough in holography was 9years ago published in this paper - wherein they were able to achieve mid-air touchable/interactive 3D holography using a Femtosecond laser system considered safer than nanosecond lasers. Quite astounding work: arxiv.org/pdf/1506.06668", "raw": "Turns out itโ€™s incredibly difficult to compute and render photorealistic 3D data in real-time. The author claims immense computational power is needed for high data transmission rates, and compute of large number of phase pixels required for realistic 3D holography. The latest significant breakthrough in holography was 9years ago published in this paper - wherein they were able to achieve mid-air touchable/interactive 3D holography using a Femtosecond laser system considered safer than nanosecond lasers. Quite astounding work: arxiv.org/pdf/1506.06668", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Realizing this breakthrough at scale is an unavoidably tempting research endeavor, super exciting especially with recent developments in machine learning and neural network algorithms demonstrating that computer-generated holograms can approach real-time processing.", "raw": "Realizing this breakthrough at scale is an unavoidably tempting research endeavor, super exciting especially with recent developments in machine learning and neural network algorithms demonstrating that computer-generated holograms can approach real-time processing.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Iโ€™ve always wondered why holography hasnโ€™t had much progress since its inception. Imagine what being able to harness and manipulate light with your bare hands in meaningful ways would be like: 3D photorealistic calls, truly immersive workspace. Given that itโ€™s depicted in every futuristic scifi movie, one could not help but vision a future as such. This paper gives a clear overview why: Turns out itโ€™s incredibly difficult to compute and render photorealistic 3D data in real-time. The author claims immense computational power is needed for high data transmission rates, and compute of large number of phase pixels required for realistic 3D holography. The latest significant breakthrough in holography was 9years ago published in this paper - wherein they were able to achieve mid-air touchable/interactive 3D holography using a Femtosecond laser system considered safer than nanosecond lasers. Quite astounding work: arxiv.org/pdf/1506.06668 Realizing this breakthrough at scale is an unavoidably tempting research endeavor, super exciting especially with recent developments in machine learning and neural network algorithms demonstrating that computer-generated holograms can approach real-time processing.
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2024-08-03T13:22:15.000Z
2024-08-04T05:51:17.327Z
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/posts/Jaward/279482507188915
708
2
875816505361498
[ { "type": "text", "value": "Continuing the series of datasets from Russian platforms: Borda.ru Posts Dataset - ", "raw": "Continuing the series of datasets from Russian platforms: Borda.ru Posts Dataset - ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/nyuuzyou/bordaru-posts", "resource": { "type": "dataset", "id": "nyuuzyou/bordaru-posts", "discussionNum": null }, "url": "https://huggingface.co/datasets/nyuuzyou/bordaru-posts", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“Š Dataset highlights:", "raw": "๐Ÿ“Š Dataset highlights:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "5,251,346 unique messages extracted from Borda.ru forums", "raw": "5,251,346 unique messages extracted from Borda.ru forums", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Deduplicated based on content to remove spam and low-quality data", "raw": "Deduplicated based on content to remove spam and low-quality data", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Each entry includes URL, username, and post content", "raw": "Each entry includes URL, username, and post content", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Primarily in Russian language", "raw": "Primarily in Russian language", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Covers a wide range of topics from various discussion forums", "raw": "Covers a wide range of topics from various discussion forums", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Dedicated to public domain under Creative Commons Zero (CC0) license", "raw": "Dedicated to public domain under Creative Commons Zero (CC0) license", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŒ Sourced from Borda.ru, a Russian platform for hosting diverse discussion forums on multiple subjects.", "raw": "๐ŸŒ Sourced from Borda.ru, a Russian platform for hosting diverse discussion forums on multiple subjects.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This dataset provides a valuable resource for analyzing Russian online discussions, sentiment analysis, and studying trends in Russian-language internet forums.", "raw": "This dataset provides a valuable resource for analyzing Russian online discussions, sentiment analysis, and studying trends in Russian-language internet forums.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Continuing the series of datasets from Russian platforms: Borda.ru Posts Dataset - https://huggingface.co/datasets/nyuuzyou/bordaru-posts ๐Ÿ“Š Dataset highlights: 5,251,346 unique messages extracted from Borda.ru forums Deduplicated based on content to remove spam and low-quality data Each entry includes URL, username, and post content Primarily in Russian language Covers a wide range of topics from various discussion forums Dedicated to public domain under Creative Commons Zero (CC0) license ๐ŸŒ Sourced from Borda.ru, a Russian platform for hosting diverse discussion forums on multiple subjects. This dataset provides a valuable resource for analyzing Russian online discussions, sentiment analysis, and studying trends in Russian-language internet forums.
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2024-08-03T12:19:36.000Z
2024-08-03T12:19:36.137Z
[]
/posts/nyuuzyou/875816505361498
670
0
381069767685423
[ { "type": "text", "value": "๐Ÿš€ Call for all AI innovators in the United Arab Emirates! ", "raw": "๐Ÿš€ Call for all AI innovators in the United Arab Emirates! ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Meta and Startupbootcamp MENA is launching the Llama Design Drive a one of its kind AI Accelerator in collaboration with the Roads and Transport Authority, Emirates, Dubai Holding and Chalhoub Group. ", "raw": "Meta and Startupbootcamp MENA is launching the Llama Design Drive a one of its kind AI Accelerator in collaboration with the Roads and Transport Authority, Emirates, Dubai Holding and Chalhoub Group. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "We are calling on all AI startups, developers, agencies and university labs to apply to this program for a chance to solve 1 out of 7 business challenges endorsed by our program partners using Llama 3.1, Meta's open source Large Language Model.", "raw": "We are calling on all AI startups, developers, agencies and university labs to apply to this program for a chance to solve 1 out of 7 business challenges endorsed by our program partners using Llama 3.1, Meta's open source Large Language Model.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Startups selected into the program will attend a 4-week product design sprint at Dubai Future Foundation powered by Startupbootcamp with support from Meta engineering teams. Selected startups will also have the chance to be fast-tracked into a global program sponsored by Meta, with the chance of receiving up to $500,000 to further support the development of their AI products.", "raw": "Startups selected into the program will attend a 4-week product design sprint at Dubai Future Foundation powered by Startupbootcamp with support from Meta engineering teams. Selected startups will also have the chance to be fast-tracked into a global program sponsored by Meta, with the chance of receiving up to $500,000 to further support the development of their AI products.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Application deadline is August 31 so be sure to apply fast!", "raw": "Application deadline is August 31 so be sure to apply fast!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "For more information and to apply: ", "raw": "For more information and to apply: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://llamadesigndrive.com", "resource": null, "url": null, "href": "https://llamadesigndrive.com", "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿš€ Call for all AI innovators in the United Arab Emirates! Meta and Startupbootcamp MENA is launching the Llama Design Drive a one of its kind AI Accelerator in collaboration with the Roads and Transport Authority, Emirates, Dubai Holding and Chalhoub Group. We are calling on all AI startups, developers, agencies and university labs to apply to this program for a chance to solve 1 out of 7 business challenges endorsed by our program partners using Llama 3.1, Meta's open source Large Language Model. Startups selected into the program will attend a 4-week product design sprint at Dubai Future Foundation powered by Startupbootcamp with support from Meta engineering teams. Selected startups will also have the chance to be fast-tracked into a global program sponsored by Meta, with the chance of receiving up to $500,000 to further support the development of their AI products. Application deadline is August 31 so be sure to apply fast! For more information and to apply: https://llamadesigndrive.com
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2024-08-03T08:58:38.000Z
2024-08-03T15:31:20.718Z
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/posts/hamdanuk/381069767685423
602
1
414396915473226
[ { "type": "text", "value": "๐Ÿ™‹๐Ÿปโ€โ™‚๏ธHey there folks ,", "raw": "๐Ÿ™‹๐Ÿปโ€โ™‚๏ธHey there folks ,", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I found this cool (new?) thing by Docker called Testcontainers , and there's an ", "raw": "I found this cool (new?) thing by Docker called Testcontainers , and there's an ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@ollama", "resource": null, "url": null, "href": null, "user": "ollama", "lang": null, "code": null, "label": null }, { "type": "text", "value": " object that you can use to programmatically serve ephemeral containers and LLMs.", "raw": " object that you can use to programmatically serve ephemeral containers and LLMs.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I made a post about it here : ", "raw": "I made a post about it here : ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/Tonic/localai-testcontainers", "resource": null, "url": null, "href": "https://huggingface.co/blog/Tonic/localai-testcontainers", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ", "raw": " ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "It's really useful, powerful and fun !", "raw": "It's really useful, powerful and fun !", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Demo coming soon ๐Ÿค—", "raw": "Demo coming soon ๐Ÿค—", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿ™‹๐Ÿปโ€โ™‚๏ธHey there folks , I found this cool (new?) thing by Docker called Testcontainers , and there's an @ollama object that you can use to programmatically serve ephemeral containers and LLMs. I made a post about it here : https://huggingface.co/blog/Tonic/localai-testcontainers It's really useful, powerful and fun ! Demo coming soon ๐Ÿค—
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[]
2024-08-03T07:42:07.000Z
2024-08-03T07:42:07.333Z
[]
/posts/Tonic/414396915473226
734
0
564304258433170
[ { "type": "text", "value": "Black Forest Labs, BASED! ๐Ÿ‘", "raw": "Black Forest Labs, BASED! ๐Ÿ‘", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "FLUX.1 is more delightful, with good instruction following. ", "raw": "FLUX.1 is more delightful, with good instruction following. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "FLUX.1 dev(", "raw": "FLUX.1 dev(", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/black-forest-labs/FLUX.1-dev", "resource": { "type": "model", "id": "black-forest-labs/FLUX.1-dev", "discussionNum": null }, "url": "https://huggingface.co/black-forest-labs/FLUX.1-dev", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ") with a 12B parameter distillation model, second only to Black Forest Labs' state-of-the-art model FLUX.1 pro. ๐Ÿ™€", "raw": ") with a 12B parameter distillation model, second only to Black Forest Labs' state-of-the-art model FLUX.1 pro. ๐Ÿ™€", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Update ๐Ÿค™Official demo: ", "raw": "Update ๐Ÿค™Official demo: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/black-forest-labs/FLUX.1-dev", "resource": { "type": "space", "id": "black-forest-labs/FLUX.1-dev", "discussionNum": null }, "url": "https://huggingface.co/spaces/black-forest-labs/FLUX.1-dev", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Black Forest Labs, BASED! ๐Ÿ‘ FLUX.1 is more delightful, with good instruction following. FLUX.1 dev(https://huggingface.co/black-forest-labs/FLUX.1-dev) with a 12B parameter distillation model, second only to Black Forest Labs' state-of-the-art model FLUX.1 pro. ๐Ÿ™€ Update ๐Ÿค™Official demo: https://huggingface.co/spaces/black-forest-labs/FLUX.1-dev
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2024-08-02T18:30:30.000Z
2024-08-03T07:44:34.757Z
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/posts/vilarin/564304258433170
4,185
1
460826571056288
[ { "type": "text", "value": "Just dropped magpie-ultra-v0.1! The first open synthetic dataset generated with Llama 3.1 405B. Created with distilabel, it's our most advanced and compute-intensive pipeline to date. We made the GPUs of the cluster go brrrrr ๐Ÿš€", "raw": "Just dropped magpie-ultra-v0.1! The first open synthetic dataset generated with Llama 3.1 405B. Created with distilabel, it's our most advanced and compute-intensive pipeline to date. We made the GPUs of the cluster go brrrrr ๐Ÿš€", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/argilla/magpie-ultra-v0.1", "resource": { "type": "dataset", "id": "argilla/magpie-ultra-v0.1", "discussionNum": null }, "url": "https://huggingface.co/datasets/argilla/magpie-ultra-v0.1", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Take it a look and tell us what you think! Probably, the models taking the most out of it are smol models ๐Ÿค— We will be improving the dataset in upcoming iterations!", "raw": "Take it a look and tell us what you think! Probably, the models taking the most out of it are smol models ๐Ÿค— We will be improving the dataset in upcoming iterations!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Just dropped magpie-ultra-v0.1! The first open synthetic dataset generated with Llama 3.1 405B. Created with distilabel, it's our most advanced and compute-intensive pipeline to date. We made the GPUs of the cluster go brrrrr ๐Ÿš€ https://huggingface.co/datasets/argilla/magpie-ultra-v0.1 Take it a look and tell us what you think! Probably, the models taking the most out of it are smol models ๐Ÿค— We will be improving the dataset in upcoming iterations!
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2024-08-02T15:58:33.000Z
2024-08-02T15:59:03.532Z
[]
/posts/gabrielmbmb/460826571056288
3,524
0
524871051582642
[ { "type": "text", "value": "Looks like Black Forest Labs is taking the Hub by storm with its text-to-image models, holding 3 of the top 5 positions on the Trending dashboard", "raw": "Looks like Black Forest Labs is taking the Hub by storm with its text-to-image models, holding 3 of the top 5 positions on the Trending dashboard", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/black-forest-labs", "resource": null, "url": null, "href": "https://huggingface.co/black-forest-labs", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Have you tried them yet?", "raw": "Have you tried them yet?", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Looks like Black Forest Labs is taking the Hub by storm with its text-to-image models, holding 3 of the top 5 positions on the Trending dashboard https://huggingface.co/black-forest-labs Have you tried them yet?
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2024-08-02T15:24:47.000Z
2024-08-02T15:25:06.494Z
[]
/posts/fdaudens/524871051582642
578
0
113768521363561
[ { "type": "text", "value": "OWLSAM2: text-promptable SAM2 ๐Ÿฆ‰ ", "raw": "OWLSAM2: text-promptable SAM2 ๐Ÿฆ‰ ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/merve/OWLSAM2", "resource": { "type": "space", "id": "merve/OWLSAM2", "discussionNum": null }, "url": "https://huggingface.co/spaces/merve/OWLSAM2", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Marrying cutting-edge zero-shot object detector OWLv2 ๐Ÿค mask generator SAM2 (small checkpoint)", "raw": "Marrying cutting-edge zero-shot object detector OWLv2 ๐Ÿค mask generator SAM2 (small checkpoint)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Zero-shot segmentation with insane precision โ›ต๏ธ", "raw": "Zero-shot segmentation with insane precision โ›ต๏ธ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I also uploaded all models with usage snippets and made a collection of SAM2 models and demos ", "raw": "I also uploaded all models with usage snippets and made a collection of SAM2 models and demos ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/merve/sam2-66ac9deac6fca3bc5482fe30", "resource": { "type": "collection", "id": "merve/sam2-66ac9deac6fca3bc5482fe30", "discussionNum": null }, "url": "https://huggingface.co/collections/merve/sam2-66ac9deac6fca3bc5482fe30", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
OWLSAM2: text-promptable SAM2 ๐Ÿฆ‰ https://huggingface.co/spaces/merve/OWLSAM2 Marrying cutting-edge zero-shot object detector OWLv2 ๐Ÿค mask generator SAM2 (small checkpoint) Zero-shot segmentation with insane precision โ›ต๏ธ I also uploaded all models with usage snippets and made a collection of SAM2 models and demos https://huggingface.co/collections/merve/sam2-66ac9deac6fca3bc5482fe30
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[]
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2024-08-02T10:09:37.000Z
2024-08-03T10:22:33.948Z
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/posts/merve/113768521363561
2,668
2
346090432958186
[ { "type": "text", "value": "FLUX FP16 produces better quality than FP8 but requires 28 GB VRAM - Full comparisons - Also compared Dev vs Turbo model and 1024 vs 1536", "raw": "FLUX FP16 produces better quality than FP8 but requires 28 GB VRAM - Full comparisons - Also compared Dev vs Turbo model and 1024 vs 1536", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "check the file names in the below given imgsli to see all details", "raw": "check the file names in the below given imgsli to see all details", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "SwarmUI on L40S is used to compare - 1.82 it / second step speed for 1024x1024", "raw": "SwarmUI on L40S is used to compare - 1.82 it / second step speed for 1024x1024", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "imgsli link that compares all : ", "raw": "imgsli link that compares all : ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://imgsli.com/MjgzNzM1", "resource": null, "url": null, "href": "https://imgsli.com/MjgzNzM1", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "SwarmUI full tutorial public post : ", "raw": "SwarmUI full tutorial public post : ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://www.patreon.com/posts/106135985", "resource": null, "url": null, "href": "https://www.patreon.com/posts/106135985", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "1-Click FLUX models downloader scripts for Windows, RunPod and Massed Compute are in below post", "raw": "1-Click FLUX models downloader scripts for Windows, RunPod and Massed Compute are in below post", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://www.patreon.com/posts/109289967", "resource": null, "url": null, "href": "https://www.patreon.com/posts/109289967", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "free Kaggle account notebook that supports FLUX already : Download from here : ", "raw": "free Kaggle account notebook that supports FLUX already : Download from here : ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://www.patreon.com/posts/106650931", "resource": null, "url": null, "href": "https://www.patreon.com/posts/106650931", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "prompt :", "raw": "prompt :", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "(medium full shot) of (awe-inspiring snake) with muscular body, amber eyes, bronze brown armored scales, venomous fangs, coiling tail, gemstone-studded scales frills, set in a barren desert wasteland, with cracked earth and the remains of ancient structures, a place of mystery and danger, at dawn, ,Masterpiece,best quality, raw photo, realistic, very aesthetic, dark", "raw": "(medium full shot) of (awe-inspiring snake) with muscular body, amber eyes, bronze brown armored scales, venomous fangs, coiling tail, gemstone-studded scales frills, set in a barren desert wasteland, with cracked earth and the remains of ancient structures, a place of mystery and danger, at dawn, ,Masterpiece,best quality, raw photo, realistic, very aesthetic, dark", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "CFG 1 - seed 1 - FLUX CFG is default : 3.5", "raw": "CFG 1 - seed 1 - FLUX CFG is default : 3.5", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Full public SwarmUI tutorial", "raw": "Full public SwarmUI tutorial", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Zero to Hero Stable Diffusion 3 Tutorial with Amazing SwarmUI SD Web UI that Utilizes ComfyUI", "raw": "Zero to Hero Stable Diffusion 3 Tutorial with Amazing SwarmUI SD Web UI that Utilizes ComfyUI", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://youtu.be/HKX8_F1Er_w", "resource": null, "url": null, "href": "https://youtu.be/HKX8_F1Er_w", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Full public Cloud SwarmUI tutorial", "raw": "Full public Cloud SwarmUI tutorial", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "How to Use SwarmUI & Stable Diffusion 3 on Cloud Services Kaggle (free), Massed Compute & RunPod", "raw": "How to Use SwarmUI & Stable Diffusion 3 on Cloud Services Kaggle (free), Massed Compute & RunPod", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://youtu.be/XFUZof6Skkw", "resource": null, "url": null, "href": "https://youtu.be/XFUZof6Skkw", "user": null, "lang": null, "code": null, "label": null } ]
FLUX FP16 produces better quality than FP8 but requires 28 GB VRAM - Full comparisons - Also compared Dev vs Turbo model and 1024 vs 1536 check the file names in the below given imgsli to see all details SwarmUI on L40S is used to compare - 1.82 it / second step speed for 1024x1024 imgsli link that compares all : https://imgsli.com/MjgzNzM1 SwarmUI full tutorial public post : https://www.patreon.com/posts/106135985 1-Click FLUX models downloader scripts for Windows, RunPod and Massed Compute are in below post https://www.patreon.com/posts/109289967 free Kaggle account notebook that supports FLUX already : Download from here : https://www.patreon.com/posts/106650931 prompt : (medium full shot) of (awe-inspiring snake) with muscular body, amber eyes, bronze brown armored scales, venomous fangs, coiling tail, gemstone-studded scales frills, set in a barren desert wasteland, with cracked earth and the remains of ancient structures, a place of mystery and danger, at dawn, ,Masterpiece,best quality, raw photo, realistic, very aesthetic, dark CFG 1 - seed 1 - FLUX CFG is default : 3.5 Full public SwarmUI tutorial Zero to Hero Stable Diffusion 3 Tutorial with Amazing SwarmUI SD Web UI that Utilizes ComfyUI https://youtu.be/HKX8_F1Er_w Full public Cloud SwarmUI tutorial How to Use SwarmUI & Stable Diffusion 3 on Cloud Services Kaggle (free), Massed Compute & RunPod https://youtu.be/XFUZof6Skkw
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2024-08-02T01:00:48.000Z
2024-08-02T01:00:48.832Z
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/posts/MonsterMMORPG/346090432958186
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[ { "type": "text", "value": "Hugging Face famous organisations activity. Guess which one has the word \"Open\" in it ๐Ÿ˜‚", "raw": "Hugging Face famous organisations activity. Guess which one has the word \"Open\" in it ๐Ÿ˜‚", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Hugging Face famous organisations activity. Guess which one has the word "Open" in it ๐Ÿ˜‚
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2024-08-01T13:00:33.000Z
2024-08-02T14:52:06.929Z
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/posts/victor/585253216448265
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[ { "type": "text", "value": "๐—ฆ๐—”๐—  ๐Ÿฎ ๐—ฟ๐—ฒ๐—น๐—ฒ๐—ฎ๐˜€๐—ฒ๐—ฑ: ๐—ก๐—ฒ๐˜„ ๐—ฆ๐—ข๐—ง๐—” ๐—ผ๐—ป ๐˜€๐—ฒ๐—ด๐—บ๐—ฒ๐—ป๐˜๐—ฎ๐˜๐—ถ๐—ผ๐—ป, ๐—ฏ๐˜† ๐—ฐ๐—ผ๐—บ๐—ฏ๐—ถ๐—ป๐—ถ๐—ป๐—ด ๐˜€๐˜†๐—ป๐˜๐—ต๐—ฒ๐˜๐—ถ๐—ฐ ๐—ฑ๐—ฎ๐˜๐—ฎ ๐˜„๐—ถ๐˜๐—ต ๐—ต๐˜‚๐—บ๐—ฎ๐—ป ๐—ณ๐—ฒ๐—ฒ๐—ฑ๐—ฏ๐—ฎ๐—ฐ๐—ธ ๐Ÿš€", "raw": "๐—ฆ๐—”๐—  ๐Ÿฎ ๐—ฟ๐—ฒ๐—น๐—ฒ๐—ฎ๐˜€๐—ฒ๐—ฑ: ๐—ก๐—ฒ๐˜„ ๐—ฆ๐—ข๐—ง๐—” ๐—ผ๐—ป ๐˜€๐—ฒ๐—ด๐—บ๐—ฒ๐—ป๐˜๐—ฎ๐˜๐—ถ๐—ผ๐—ป, ๐—ฏ๐˜† ๐—ฐ๐—ผ๐—บ๐—ฏ๐—ถ๐—ป๐—ถ๐—ป๐—ด ๐˜€๐˜†๐—ป๐˜๐—ต๐—ฒ๐˜๐—ถ๐—ฐ ๐—ฑ๐—ฎ๐˜๐—ฎ ๐˜„๐—ถ๐˜๐—ต ๐—ต๐˜‚๐—บ๐—ฎ๐—ป ๐—ณ๐—ฒ๐—ฒ๐—ฑ๐—ฏ๐—ฎ๐—ฐ๐—ธ ๐Ÿš€", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "It's a model for Object segmentation, for both image and video:", "raw": "It's a model for Object segmentation, for both image and video:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ‘‰ input = a text prompt, or a click on a specific object", "raw": "๐Ÿ‘‰ input = a text prompt, or a click on a specific object", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ‘‰ output = the model draws a mask around the object. In video segmentation, the mask should follow the object's movements (it is then called a masklet)", "raw": "๐Ÿ‘‰ output = the model draws a mask around the object. In video segmentation, the mask should follow the object's movements (it is then called a masklet)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ’ช SAM 2 is 6x faster than the previous version, it now also works on a video, and it beats SOTA by far on both image and video segmentation tasks.", "raw": "๐Ÿ’ช SAM 2 is 6x faster than the previous version, it now also works on a video, and it beats SOTA by far on both image and video segmentation tasks.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "How did they pull that?", "raw": "How did they pull that?", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The main blocker for video segmentation was that data is really hard to collect: to build your training dataset, should you manually draw masks on every frame? That would be way too costly! โžก๏ธ As a result, existing video segmentation datasets have a real lack of coverage: few examples, few masklets drawn.", "raw": "The main blocker for video segmentation was that data is really hard to collect: to build your training dataset, should you manually draw masks on every frame? That would be way too costly! โžก๏ธ As a result, existing video segmentation datasets have a real lack of coverage: few examples, few masklets drawn.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ’ก Key idea: researchers they decided to use a segmentation model to help them collect the dataset.", "raw": "๐Ÿ’ก Key idea: researchers they decided to use a segmentation model to help them collect the dataset.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "But then itโ€™s a chicken and egg problem: you need the model to create the dataset and the opposite as well? ๐Ÿค”", "raw": "But then itโ€™s a chicken and egg problem: you need the model to create the dataset and the opposite as well? ๐Ÿค”", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โ‡’ To solve this, they build a data generation system that they scale up progressively in 3 successive manual annotations phases:", "raw": "โ‡’ To solve this, they build a data generation system that they scale up progressively in 3 successive manual annotations phases:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐—ฆ๐˜๐—ฒ๐—ฝ ๐Ÿญ: Annotators use only SAM + manual editing tools on each frame โ‡’ Create 16k masklets across 1.4k videos", "raw": "๐—ฆ๐˜๐—ฒ๐—ฝ ๐Ÿญ: Annotators use only SAM + manual editing tools on each frame โ‡’ Create 16k masklets across 1.4k videos", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐—ฆ๐˜๐—ฒ๐—ฝ ๐Ÿฎ: Then train a first SAM 2, add it in the loop to temporally propagate frames, and correct by re-doing a mask manually when an error has occured โ‡’ This gets a 5.1x speedup over data collection in phase 1! ๐Ÿƒ Collect 60k masklets", "raw": "๐—ฆ๐˜๐—ฒ๐—ฝ ๐Ÿฎ: Then train a first SAM 2, add it in the loop to temporally propagate frames, and correct by re-doing a mask manually when an error has occured โ‡’ This gets a 5.1x speedup over data collection in phase 1! ๐Ÿƒ Collect 60k masklets", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐—ฆ๐˜๐—ฒ๐—ฝ ๐Ÿฏ: Now SAM 2 is more powerful, it has the โ€œsingle clickโ€ prompting option, thus annotators can use it with simple clicks to re-annotate data.", "raw": "๐—ฆ๐˜๐—ฒ๐—ฝ ๐Ÿฏ: Now SAM 2 is more powerful, it has the โ€œsingle clickโ€ prompting option, thus annotators can use it with simple clicks to re-annotate data.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "They even add a completely automatic step to generate 350k more masklets!", "raw": "They even add a completely automatic step to generate 350k more masklets!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "And in turn, the model perf gradually increases.", "raw": "And in turn, the model perf gradually increases.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I find this a great example of combining synthetic data generation with human annotation ๐Ÿ‘", "raw": "I find this a great example of combining synthetic data generation with human annotation ๐Ÿ‘", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
๐—ฆ๐—”๐—  ๐Ÿฎ ๐—ฟ๐—ฒ๐—น๐—ฒ๐—ฎ๐˜€๐—ฒ๐—ฑ: ๐—ก๐—ฒ๐˜„ ๐—ฆ๐—ข๐—ง๐—” ๐—ผ๐—ป ๐˜€๐—ฒ๐—ด๐—บ๐—ฒ๐—ป๐˜๐—ฎ๐˜๐—ถ๐—ผ๐—ป, ๐—ฏ๐˜† ๐—ฐ๐—ผ๐—บ๐—ฏ๐—ถ๐—ป๐—ถ๐—ป๐—ด ๐˜€๐˜†๐—ป๐˜๐—ต๐—ฒ๐˜๐—ถ๐—ฐ ๐—ฑ๐—ฎ๐˜๐—ฎ ๐˜„๐—ถ๐˜๐—ต ๐—ต๐˜‚๐—บ๐—ฎ๐—ป ๐—ณ๐—ฒ๐—ฒ๐—ฑ๐—ฏ๐—ฎ๐—ฐ๐—ธ ๐Ÿš€ It's a model for Object segmentation, for both image and video: ๐Ÿ‘‰ input = a text prompt, or a click on a specific object ๐Ÿ‘‰ output = the model draws a mask around the object. In video segmentation, the mask should follow the object's movements (it is then called a masklet) ๐Ÿ’ช SAM 2 is 6x faster than the previous version, it now also works on a video, and it beats SOTA by far on both image and video segmentation tasks. How did they pull that? The main blocker for video segmentation was that data is really hard to collect: to build your training dataset, should you manually draw masks on every frame? That would be way too costly! โžก๏ธ As a result, existing video segmentation datasets have a real lack of coverage: few examples, few masklets drawn. ๐Ÿ’ก Key idea: researchers they decided to use a segmentation model to help them collect the dataset. But then itโ€™s a chicken and egg problem: you need the model to create the dataset and the opposite as well? ๐Ÿค” โ‡’ To solve this, they build a data generation system that they scale up progressively in 3 successive manual annotations phases: ๐—ฆ๐˜๐—ฒ๐—ฝ ๐Ÿญ: Annotators use only SAM + manual editing tools on each frame โ‡’ Create 16k masklets across 1.4k videos ๐—ฆ๐˜๐—ฒ๐—ฝ ๐Ÿฎ: Then train a first SAM 2, add it in the loop to temporally propagate frames, and correct by re-doing a mask manually when an error has occured โ‡’ This gets a 5.1x speedup over data collection in phase 1! ๐Ÿƒ Collect 60k masklets ๐—ฆ๐˜๐—ฒ๐—ฝ ๐Ÿฏ: Now SAM 2 is more powerful, it has the โ€œsingle clickโ€ prompting option, thus annotators can use it with simple clicks to re-annotate data. They even add a completely automatic step to generate 350k more masklets! And in turn, the model perf gradually increases. I find this a great example of combining synthetic data generation with human annotation ๐Ÿ‘
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2024-08-01T12:40:18.000Z
2024-08-01T12:40:18.070Z
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/posts/m-ric/760125495072237
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Running Gemma 2 2B at 41.66 tokens/s on my MacBook ๐Ÿ’ป๐Ÿš€ - MLX Community's swift conversion - One-line download from the Hub - Small yet powerful on-device model Try it yourself: https://huggingface.co/collections/mlx-community/google-gemma2-667dca89bc9abbfa34080066 #GemmaAI #OnDeviceAI #MachineLearning
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2024-07-31T23:59:02.000Z
2024-08-02T08:09:38.868Z
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nice
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2024-07-31T19:57:28.000Z
2024-07-31T19:57:28.851Z
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