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"value": "- convert to FP32, calculate imatrix, quantize to Q8_0 and Q4_K_M",
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"value": "- convert to FP16, calculate imatrix, quantize to Q8_0 and Q4_K_M",
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"value": "I calculated the kld base from the FP32 model:",
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"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`",
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"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",
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"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.",
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"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??",
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] | 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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"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...",
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"value": "๐ I've made a long post summarizing the main stages of building LLMs: neural networks, optimization, backpropagation, attention layers...",
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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).",
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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).",
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"type": "text",
"value": "Pour le post en franรงais ๐ ",
"raw": "Pour le post en franรงais ๐ ",
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] | ๐ญ๐ฒ๐ฟ๐ผ-๐บ๐ฎ๐๐ต ๐ถ๐ป๐๐ฟ๐ผ ๐๐ผ ๐๐ ๐ต๐ถ๐๐๐ผ๐ฟ๐: ๐ณ๐ฟ๐ผ๐บ ๐๐ต๐ฒ ๐ญ๐ต๐ฑ๐ฌ๐ ๐๐ผ ๐๐ผ๐ฑ๐ฎ๐'๐ ๐๐๐ ๐ ๐
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! ๐
๐ I've made a long post summarizing the main stages of building LLMs: neural networks, optimization, backpropagation, attention layers...
โ
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 ๐ https://aymeric-roucher.github.io/brief-history-of-ai/
Pour le post en franรงais ๐ https://aymeric-roucher.github.io/breve-histoire-de-l-ia/ | {
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] | Some personal and professional news โจ
I'm writing a book on ML metrics.
Together with Wojtek Kuberski, weโre 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:
โข Regression
โข Classification
โข Clustering
โข Ranking
โข Vision
โข Text
โข GenAI
โข Bias and Fairness
๐ check out the book: https://www.nannyml.com/metrics | {
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https://huggingface.co/spaces/Jofthomas/Everchanging-Quest
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] | 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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] | ๐ฃ Introducing Dataset Viber: your chill repo for data collection, annotation and vibe checks! ๐
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.
๐ง 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
๐ฏ 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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Full snippet is here: https://gist.github.com/sayakpaul/cfaebd221820d7b43fae638b4dfa01ba
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] | 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]
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๐Comparable performances with commercial models or specific OCR model but with dialogue capabilities!
โจ2B/26B Models Here! https://huggingface.co/AI4Chem/ChemVLM-26B
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] | ๐ธPhoto LoRA Drop๐ธ
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!
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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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] | 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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] | Introducing Fineweb-Edu-Fortified: An enhanced Fineweb-Edu dataset. ๐
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. ๐ค
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] | Letโs see JEPA in action๐ค
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.
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] | 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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"raw": "But, why? ๐ค ",
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"value": "Compute, data, and expertise. All three come at a great cost! โ๏ธ๐๐ก",
"raw": "Compute, data, and expertise. All three come at a great cost! โ๏ธ๐๐ก",
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"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! ๐",
"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! ๐",
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"value": " competition and more fine-tunes! ๐๐",
"raw": " competition and more fine-tunes! ๐๐",
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"raw": "Data [Stanford Universityโs 2024 AI Index Report]: ",
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"value": "Many thanks to everyone spending tons of resources and open-sourcing the models! ๐ค",
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] | It took Googleโs Transformer model from 2017 a whopping $900 to train. ๐ธ
This in contrast to the $191 million Google spent on Gemini Ultra sounds like a bargain! ๐ฐ
Gemini Ultra required 50 billion petaFLOPS (one petaFLOP equals one quadrillion FLOPs). ๐ค
Compared to OpenAIโs GPT-4, which required 21 billion petaFLOPS, at a cost of $78 million. ๐ก
2017: Original Transformer Model: $930 [@Google ] ๐ป
2018: BERT-Large: $3,288 [@Google] ๐
2019: RoBERTa Large: 160k [@Meta] ๐
2020: GPT-3(175B): $4.32M [@OpenAI] ๐ง
2023: Llama 2 70B: $3.93M [@Meta] ๐
2023: GPT-4: $78.35M [@OpenAI] ๐
Now, Gemini Ultra: $191.4M [@Google] ๐
This forms an exponential curve! ๐คฏ
But, why? ๐ค
Compute, data, and expertise. All three come at a great cost! โ๏ธ๐๐ก
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! ๐
This barrier of cost is going to result in fewer new foundational models/less competition and more fine-tunes! ๐๐
Data [Stanford Universityโs 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! ๐ค | {
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TLDR: You can just ask LLMs which hyperparameters to use, and it works pretty well! You can even directly optimize your modelโs code as a hyperparameter with this.
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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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99% of the performance across various benchmarks!
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"raw": "๐๐๐ ๐ @๐๐๐๐๐๐๐๐๐๐๐/๐๐๐๐๐๐๐๐๐๐๐๐",
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] | I'm excited to announce that Transformers.js V3 is finally available on NPM! ๐ฅ State-of-the-art Machine Learning for the web, now with WebGPU support! ๐คฏโก๏ธ
Install it from NPM with:
๐๐๐ ๐ @๐๐๐๐๐๐๐๐๐๐๐/๐๐๐๐๐๐๐๐๐๐๐๐
or via CDN, for example: https://v2.scrimba.com/s0lmm0qh1q
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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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] | 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.
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When you solve it just use a ๐ 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.
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] | 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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] | ๐ RAGoon is now available on PyPI, GitHub, and as a Space on Hugging Face for batched embeddings generation ๐ค
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.
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- 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
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] | 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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] | AutoGen from @Microsoft is crazy! ๐ It's an open-source framework that allows LLM agents to chat with each other to solve your tasks. ๐ค๐ฌ
They use the Assistant-Agent and User-Proxy-Agent framework! ๐ ๏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! ๐งโ๐ปโ
Both Assistant-Agent and User-Proxy-Agent can be the same or different LLMs. ๐ค๐
AutoGen is an open-source programming framework for building AI agents and facilitating cooperation among multiple agents to solve tasks. ๐
This is truly amazing for building agentic AI quickly! ๐โจ
GitHub: https://github.com/microsoft/autogen ๐
```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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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
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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.
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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
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Introducing the 435M model that outperforms Llama-Guard-3-8B while slashing 75% of the computation cost! ๐ป๐ฅ
๐ Check it out: https://huggingface.co/hbseong/HarmAug-Guard (Yes, INFERENCE CODE INCLUDED! ๐ก)
More details in our paper: https://arxiv.org/abs/2410.01524 ๐
#HarmAug #LLM # Safety #EfficiencyBoost #Research #AI #MachineLearning | {
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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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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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"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).",
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"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.",
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"raw": "- Build collaborative training sets with your community (surprisingly underused in AI journalism)",
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"value": "- Turn your website chatbot logs into high-quality fine-tuning data",
"raw": "- Turn your website chatbot logs into high-quality fine-tuning data",
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"value": "- Compare generated vs published content (great for SEO headlines)",
"raw": "- Compare generated vs published content (great for SEO headlines)",
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"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.",
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"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.",
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] | 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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] | 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!
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] | 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 Faceโs 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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"value": "Tรผrkรงe gรถrsel dil iลleme sฤฑnฤฑrlarฤฑnฤฑ birlikte zorlayalฤฑm!",
"raw": "Tรผrkรงe gรถrsel dil iลleme sฤฑnฤฑrlarฤฑnฤฑ birlikte zorlayalฤฑm!",
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] | ๐ Introducing TraVisionLM: Turkish Visual Language Model - The First of Its Kind! ๐น๐ท๐ผ๏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, itโs easy to load, fine-tune, and useโno 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.
๐ 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
Letโs push Turkish visual language processing forward!
---
๐ TraVisionLM: Tรผrรผnรผn ฤฐlk รrneฤi Tรผrkรงe Gรถrsel Dil Modelini Sunuyorum! ๐น๐ท๐ผ๏ธ
TraVisionLM modelini Hugging Face'te yayฤฑnladฤฑm! 875M parametre ile bu hafif ve verimli model, gรถrรผntรผye dayalฤฑ Tรผrkรงe talimatlarฤฑ iลlemek iรงin tasarlandฤฑ. Transformers kรผtรผphanesiyle tamamen uyumlu, yรผklemesi, eฤitmesi ve kullanmasฤฑ รงok kolayโdฤฑล kรผtรผphane gerekmez!
Tek baลฤฑma geliลtirdiฤim TraVisionLM, dรผลรผk kaynaklฤฑ dillerde araลtฤฑrmalar iรงin saฤlam bir temel sunuyor. Geliลtirmeye devam ederken geri bildirimlerinizi bekliyorum.
๐ Hemen keลfedin:
- 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
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] | What is the best LLM for RAG systems? ๐ค
In a business setting, it will be the one that gives the best performance at a great price! ๐ผ๐ฐ
And maybe it should be easy to fine-tune, cheap to fine-tune... FREE to fine-tune? ๐ฒโจ
That's @Google Gemini 1.5 Flash! ๐๐
It now supports fine-tuning, and the inference cost is the same as the base model! <coughs LORA adopters> ๐คญ๐ค
So the base model must be expensive? ๐ธ
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. ๐๐ต
But is it any good? ๐คทโโ๏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. ๐๐ฏ
Google has finally given a model that is free to tune and offers an excellent balance between performance and cost. โ๏ธ๐
Happy tuning... ๐ถ๐ง
Gemini 1.5 Flash: https://developers.googleblog.com/en/gemini-15-flash-updates-google-ai-studio-gemini-api/ ๐
LLM Hallucination Index: https://www.rungalileo.io/hallucinationindex ๐ | {
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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.
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Most likely image generation ",
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Most likely image generation ",
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Flexible to design what is needed (keyword-centric) ",
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"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```",
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] | โจThe STABLE IMAGINE !!โจ
๐บSpace: https://huggingface.co/spaces/prithivMLmods/STABLE-IMAGINE
โ๏ธThe specific LoRA in the space that requires appropriate trigger words brings good results.
๐ Articles: https://huggingface.co/blog/prithivMLmods/lora-adp-01
**Description and Utility Functions **
โ
Most likely image generation
โ๏ธ Most accurate trigger words expected
โ
Each designed to capture different artistic elements
โ๏ธ Specialized styles and characteristics
โ
Flexible to design what is needed (keyword-centric)
โ๏ธ Increasing productivity
๐ซRepository: https://github.com/prithivsakthiur/gen-vision
๐Colab Link: https://colab.research.google.com/drive/1axA0pU--32t4a8AHiVlt6zyl8gRfiXKs
*๏ธโฃNotebook: 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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The algorithm shows dynamic neural network compression during training - with reduced size of weight, activation tensors and bits required to represent weights.
Itโs basically shrinking the neural network size (weights and activations) as itโs being trained without compromising performance - this helps reduce compute and inference cost.
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https://arxiv.org/html/2406.09308v1
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] | Remember when @mistralAI said large enough and casually dropped Mistral-Large-Instruct-2407? ๐คฏ๐
It's now on http://lmsys.org! ๐ It works amazing for instruction following, hard prompts, coding, and longer queries with only 123 billion parameters. ๐ก๐ป
It outperforms GPT4-Turbo and Claude 3 Opus on Coding, Hard Prompts, Math, and Longer Query categories. ๐๐ข
It also outperforms Llama 3.1 405B on Instruction Following while being 3x smaller. ๐๐
It also does exceedingly well on the Ai2 ZebraLogic logistic reasoning benchmark despite being much smaller than the other models. ๐ฆ๐ค
Mistral is not here to take part but to take over! ๐๐
Model: https://mistral.ai/news/mistral-large-2407/ | {
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ยท Making the momentum coefficient complex for adversarial games like GANs.
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ยท Differentiably finding bifurcations in optimization for diverse solutions.
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] | 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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- 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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] | ๐๏ธ Remember when last April, @Meta released Segment Anything Model (SAM) paper and it was too good to be true. ๐คฏ
They have now released Segment Anything Model 2 (SAM 2) and it's mind-blowingly great! ๐
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This dataset is truly huge, with 190.9K manual annotations and 451.7K automatic! ๐
๐ Paper: https://ai.meta.com/research/publications/sam-2-segment-anything-in-images-and-videos/
๐ Blog: https://ai.meta.com/sam2/
๐ Demo: https://sam2.metademolab.com/demo
๐พ Model Weights: https://github.com/facebookresearch/segment-anything-2/blob/main/checkpoints/download_ckpts.sh
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] | ๐ซ 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 exitsโeither pseudo-acquisitions or real onesโare 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
- Figureโs 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/
- Worldโs 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 worldโs leading-edge semiconductor manufacturers to construct chip plants in theUS with financial assistance from the administration
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] | ๐ 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.
๐ค Contributions, corrections, and feedback are very welcome!
The Open Language Models List: https://github.com/mmhamdy/open-language-models | {
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] | 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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] | Looks like @Google is still not satisfied with Gemini 1.5 Pro! ๐ฒ
Good folks at @GoogleDeepMind quietly updated the already good Gemini 1.5 Pro to Gemini-1.5-Pro-Experiment-0801 ๐
Unremarkable naming aside, the model itself outperforms GPT-4o, Claude-3.5, and LLama 3.1 on LMSYS and the Vision Leaderboard. ๐
Gemini-1.5-Pro-Experiment-0801 is great at almost everything, multi-lingual tasks, Maths, understanding, and coding. ๐๐๐ป
Although in my testing, I felt Claude-3.5 was slightly better at coding! ๐จโ๐ป๐ค
Also, still cannot find an LLM that can solve the "Strawberry prompt"! ๐โ
"""
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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] | 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.
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"raw": " ) appear to be arbitrary, reflecting probable brute-force exploration. This doesn't need to be the case.",
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"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.",
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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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"value": "- TorToiSe does very well, falling into second place after StyleTTS 2, which is also ranked first in the human evaluation at ",
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"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. ",
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"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.",
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"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.",
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"value": ". I'll also write an article on these and all the other results soon, since our paper, ",
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] | 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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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!
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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, 1955โฆ
We look forward to replicate this approach very soon on other "hard" tasks commonly associated with generalist LLMs/SLMs: RAG, function calling, summarization, document segmentationโฆ
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
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Merging code (very simple), inference code, merged params: https://huggingface.co/sayakpaul/FLUX.1-merged
Enjoy the Monday ๐ค
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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
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"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. 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.",
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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
๐ Comprehensive Tutorial Video Link โถ๏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.
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โถ๏ธ https://www.patreon.com/posts/106135985
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โถ๏ธ https://youtu.be/XFUZof6Skkw
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โถ๏ธ https://blackforestlabs.ai/announcing-black-forest-labs/
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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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] | 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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] | 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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] | 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.
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] | Iโve always wondered why holography hasnโt 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 itโs 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 itโs 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
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Primarily in Russian language
Covers a wide range of topics from various discussion forums
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] | ๐ 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.
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] | Black Forest Labs, BASED! ๐
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. ๐
Update ๐คOfficial demo:
https://huggingface.co/spaces/black-forest-labs/FLUX.1-dev
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] | 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 ๐
https://huggingface.co/datasets/argilla/magpie-ultra-v0.1
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] | 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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] | OWLSAM2: text-promptable SAM2 ๐ฆ https://huggingface.co/spaces/merve/OWLSAM2
Marrying cutting-edge zero-shot object detector OWLv2 ๐ค mask generator SAM2 (small checkpoint)
Zero-shot segmentation with insane precision โต๏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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] | 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
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"value": "๐ก Key idea: researchers they decided to use a segmentation model to help them collect the dataset.",
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"value": "But then itโs a chicken and egg problem: you need the model to create the dataset and the opposite as well? ๐ค",
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"value": "โ To solve this, they build a data generation system that they scale up progressively in 3 successive manual annotations phases:",
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"value": "๐ฆ๐๐ฒ๐ฝ ๐ญ: Annotators use only SAM + manual editing tools on each frame โ Create 16k masklets across 1.4k videos",
"raw": "๐ฆ๐๐ฒ๐ฝ ๐ญ: Annotators use only SAM + manual editing tools on each frame โ Create 16k masklets across 1.4k videos",
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"value": "๐ฆ๐๐ฒ๐ฝ ๐ฎ: 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! ๐ Collect 60k masklets",
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"value": "๐ฆ๐๐ฒ๐ฝ ๐ฏ: Now SAM 2 is more powerful, it has the โsingle clickโ prompting option, thus annotators can use it with simple clicks to re-annotate data.",
"raw": "๐ฆ๐๐ฒ๐ฝ ๐ฏ: Now SAM 2 is more powerful, it has the โsingle clickโ prompting option, thus annotators can use it with simple clicks to re-annotate data.",
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] | ๐ฆ๐๐ ๐ฎ ๐ฟ๐ฒ๐น๐ฒ๐ฎ๐๐ฒ๐ฑ: ๐ก๐ฒ๐ ๐ฆ๐ข๐ง๐ ๐ผ๐ป ๐๐ฒ๐ด๐บ๐ฒ๐ป๐๐ฎ๐๐ถ๐ผ๐ป, ๐ฏ๐ ๐ฐ๐ผ๐บ๐ฏ๐ถ๐ป๐ถ๐ป๐ด ๐๐๐ป๐๐ต๐ฒ๐๐ถ๐ฐ ๐ฑ๐ฎ๐๐ฎ ๐๐ถ๐๐ต ๐ต๐๐บ๐ฎ๐ป ๐ณ๐ฒ๐ฒ๐ฑ๐ฏ๐ฎ๐ฐ๐ธ ๐
It's a model for Object segmentation, for both image and video:
๐ input = a text prompt, or a click on a specific object
๐ 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)
๐ช 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! โก๏ธ As a result, existing video segmentation datasets have a real lack of coverage: few examples, few masklets drawn.
๐ก Key idea: researchers they decided to use a segmentation model to help them collect the dataset.
But then itโs a chicken and egg problem: you need the model to create the dataset and the opposite as well? ๐ค
โ To solve this, they build a data generation system that they scale up progressively in 3 successive manual annotations phases:
๐ฆ๐๐ฒ๐ฝ ๐ญ: Annotators use only SAM + manual editing tools on each frame โ Create 16k masklets across 1.4k videos
๐ฆ๐๐ฒ๐ฝ ๐ฎ: 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! ๐ Collect 60k masklets
๐ฆ๐๐ฒ๐ฝ ๐ฏ: Now SAM 2 is more powerful, it has the โsingle 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 ๐ | {
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