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] | Since new TTS (Text-to-Speech) systems are coming out what feels like every day, and it's currently hard to compare them, my latest project has focused on doing just that.
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https://huggingface.co/spaces/ttsds/benchmark
Anyone can submit a new TTS model, and I hope this can provide a way to get some information on which areas models perform well or poorly in.
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] | Thrilled to share some AI insights for journalism! ๐๐ค
Just wrote a guest post on @ndiakopoulos's blog about Hugging Face on Sheets tool.
Why it matters:
๐ Brings AI power directly to spreadsheets
๐ Huge potential for data journalism
๐ซ๐ป No coding required!
If you've read Nicholas' "Automating the News" (a must-read!), you'll appreciate how this tool fits into the evolving landscape of AI in journalism.
Read here: https://generative-ai-newsroom.com/bringing-open-source-models-to-spreadsheets-c440fc4818b4
#AIJournalism #DataJournalism | {
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] | ๐ท๐ธ New Benchmark for Serbian Language ๐ท๐ธ
@DjMel and I recently released a new benchmark for Serbian language that measures General Knowledge of LLMs. We had to parse over 20 years of university entrance exams for University of Belgrade, so the dataset is of high quality.
๐ฅ OAI models still hold the podium places with a significant gap compared to open-source models
๐ค https://huggingface.co/Qwen/Qwen2-7B-Instruct and https://huggingface.co/VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct models show promising results considering they weren't trained on Serbian language
๐ Best open-source model seems to be https://huggingface.co/Stopwolf/Mustra-7B-Instruct-v0.2, a merge between https://huggingface.co/gordicaleksa/YugoGPT and https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2
๐ Some models like https://huggingface.co/google/gemma-2-9b-it turned out to be a disappointment with random guessing-like accuracy
Take a look at the whole results at the dataset page:
https://huggingface.co/datasets/DjMel/oz-eval
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โก InferenceClient is now a drop-in replacement for OpenAI's chat completion!
โจ Support for response_format, adapter_id , truncate, and more in InferenceClient
๐พ Serialization module with a save_torch_model helper that handles shared layers, sharding, naming convention, and safe serialization. Basically a condensed version of logic scattered across safetensors, transformers , accelerate
๐ Optimized HfFileSystem to avoid getting rate limited when browsing https://huggingface.co/datasets/HuggingFaceFW/fineweb
๐จ HfApi & CLI improvements: prevent empty commits, create repo inside resource group, webhooks API, more options in the Search API, etc.
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https://huggingface.co/spaces/Wauplin/huggingface_hub/discussions/7
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] | # Offensive Physical Security Reconnaissance Planning Automation with public facing RTSP streams and Moondream
After some late night casual hacking about on VLMs for criminal attack vector reconnaissance automaton experiments using Moondream (as usual) based image-text-text with pre defined text prompts that are tuned for extracting weakness or customer identity and monitory based theft physical red team engagement reconnaissance and vector of malicious or criminal activity Working on a space. Thanks again for such a wonderful blessing of super power image-text-to-text model with minimal computational power needed @vikhyatk
I have started actually implementing a custom little tool with both static html space sand python gradio spaces on the go which I shall share as hf spaces when done them.
---
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] | GraphRAG-Ollama-UI
I've been working on a local version of Microsoft's GraphRAG that uses Ollama for everything. It's got a new interactive UI built with Gradio that makes it easier to manage data, run queries, and visualize results. It's not fully featured or set up to harness the entire GraphRAG library yet but it allows you to run all the standard commands for Indexing/Processing and chatting with your graph. Some key features:
Uses local models via Ollama for LLM and embeddings
3D graph visualization of the knowledge graph using Plotly
File management through the UI (upload, view, edit, delete)
Settings management in the interface
Real-time logging for debugging
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] | #ICLM 2024 is almost there ๐ฅ๐ฅ๐ฅ PM if you will be in Vienna next week, Glad to catchup with the Hugging Face community there!
I would like to contribute ๐ by releasing the sixth Knowledge Vault, with 100 lectures visualized from the last 10 years of ICML from 2014 to 2024, (10 from 2024 will be included after the conference) including knowledge graphs for all the Invited Lectures and some extras, with almost 3000 topics represented using AI.
You can explore it here:
๐ https://theendofknowledge.com/Vaults/6/ICML-2015-2024.html
And you can learn more about the Vaults here:
๐https://www.linkedin.com/pulse/knowledge-vaults-david-vivancos-lbjef/
And previous Vaults relevant to the #huggingface community are:
๐ [ @lexfridman 2018-2024 Interviews] https://theendofknowledge.com/Vaults/1/Lex100-2024.html
๐ [ICLR 2014-2023] https://theendofknowledge.com/Vaults/2/ICLR2014-2023.html
๐ [AIForGood 2017-2024] https://theendofknowledge.com/Vaults/4/AIForGood2017-2024.html
๐ [CVPR 2015-2024] https://theendofknowledge.com/Vaults/5/CVPR-2015-2024.html
Hope you like them!
And great to see you all at #icml2024 @clem @thomwolf @julien-c and team
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] | I've made a creative version of Tile Upscaler
- https://huggingface.co/spaces/gokaygokay/TileUpscalerV2
- https://github.com/gokayfem/Tile-Upscaler
- New tiling strategy
- Now it's closer to Clarity Upscaler
- It has more parameters to play and it has more room to fail because of that
- You should try different resolutions, strength and controlnet strength
Original Tile Upscaler
- https://huggingface.co/spaces/gokaygokay/Tile-Upscaler
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Mistral released Codestral Mamba ๐
> Beats DeepSeek QwenCode, best model < 10B, competitive with Codestral 22B
> Mamba 2 architecture - supports up to 256K context
> Apache 2.0 licensed, perfect for local code assistant
> Transformers & llama.cpp integration upcoming!
Model checkpoint: https://huggingface.co/mistralai/mamba-codestral-7B-v0.1
Hugging Face dropped SmolLM ๐ค
> Beats MobileLLM, Qwen 0.5B, Phi 1.5B and more!
> 135M, 360M, and 1.7B param model checkpoints
> Trained on 600B high-quality synthetic + FineWeb Edu tokens
> Architecture: Llama + GQA + 2048 ctx length
> Ripe for fine-tuning and on-device deployments.
> Works out of the box with Transformers!
Model checkpoints: https://huggingface.co/collections/HuggingFaceTB/smollm-6695016cad7167254ce15966
Mistral released Mathstral 7B โ
> 56.6% on MATH and 63.47% on MMLU
> Same architecture as Mistral 7B
> Works out of the box with Transformers & llama.cpp
> Released under Apache 2.0 license
Model checkpoint: https://huggingface.co/mistralai/mathstral-7B-v0.1
Pretty dope day for open source ML. Can't wait to see what the community builds with it and to support them further! ๐ค
What's your favourite from the release today? | {
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] | Small models, BIG impact: SmolLM is here! ๐๐ฌ
We're launching a series of small but mighty language models:
๐๏ธ Super fast - runs on laptops, phones, you name it!
๐ 3 sizes: 130M, 350M, and 1.5B parameters
๐ฅ Outperforms same size models from Meta, Microsoft, and Qwen
๐ Fully open-source: datasets, training code, models
๐๐๐ฒ ๐๐๐๐ญ๐ฎ๐ซ๐๐ฌ
- Trained on FineWeb-Edu and Cosmopedia v2 (largest synthetic pre-training dataset)
- No cloud needed - run locally for privacy and energy efficiency
- Everything is public, from data curation to training steps
๐๐จ๐ญ๐๐ง๐ญ๐ข๐๐ฅ ๐ฎ๐ฌ๐ ๐๐๐ฌ๐๐ฌ
- On-device autocomplete
- Local request parsing
- Custom fine-tuning for specific needs without the need for expensive GPUs
๐๐จ ๐๐๐๐ฉ๐๐ซ
๐ Check it out: https://huggingface.co/collections/HuggingFaceTB/smollm-models-6695016cad7167254ce15966
๐ Run the 360M model in your browser, 100 % private: https://huggingface.co/spaces/HuggingFaceTB/SmolLM-360M-Instruct-WebGPU
๐ Read the blog explaining everything in detail: huggingface.co/blog/smollm
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] | Cool things this week from @huggingface!
๐AI math olympiad winner NuminaMath is here!
๐คAnnouncing New Hugging Face and Keras NLP integration
โจUI overhaul to HF tokens!
๐ง Embed our dataset viewer on any webpage!
https://huggingface.co/blog/winning-aimo-progress-prize
https://huggingface.co/blog/keras-nlp-integration
https://huggingface.co/settings/tokens
https://x.com/julien_c/status/1812099420726456457
Check out the full list on our discord! ๐
https://discord.com/invite/JfAtkvEtRb
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Compose Image Grid",
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Apply styles",
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Apply styles",
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Set up Image tones",
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Set up Image tones",
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Apply filters & adjust quality",
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Apply filters & adjust quality",
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] | ๐ดโญ New addition to the existing concept space! ๐ดโญ
๐๏ธ Space: https://huggingface.co/spaces/prithivMLmods/IMAGINEO-4K
๐ Tried the Duotone Canvas with the image generator. Unlike the duotone filter in the Canva app, which applies hue and tints in RGBA, this feature applies duotones based purely on the provided prompt to personalize the generated image.
๐ These tones also work with the gridding option, which already exists in the space.
๐ The application of tones depends on the quality and detail of the prompt given. The palette may be distorted in some cases.
๐It doesn't apply like a hue or tint in RGBA (as shown in canva app below); it is purely based on the prompts passed.
๐๏ธ Check out the space: https://huggingface.co/spaces/prithivMLmods/IMAGINEO-4K
๐๏ธCollection: https://huggingface.co/collections/prithivMLmods/collection-zero-65e48a7dd8212873836ceca2
```
huggingface.co/spaces/prithivMLmods/IMAGINEO-4K
```
๐๏ธWhat you can do with this space:
โ
Compose Image Grid
๐๐ป "2x1", "1x2", "2x2", "2x3", "3x2", "1x1"
โ
Apply styles
โ
Set up Image tones
โ
Apply filters & adjust quality
.
.
.
Thanks for reading!
- @prithivMLmods | {
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626990036588926 | [
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] | Introducing Plugins in NiansuhAI (on July 20, 2024)
Plugin Names:
1. WebSearch: Tool for searching the web using search engines.
2. Calculator: Helps evaluate mathematical expressions; extends the base Tool class.
3. WebBrowser: Interacts with web pages to extract information or summarize content.
4. Wikipedia: Retrieves data from Wikipedia using its API.
5. Arxiv: Searches and fetches article information from Arxiv.
6. WolframAlphaTool: Answers questions on Math, Science, Technology, Culture, Society, and Everyday Life.
Similar to https://hf.co/chat | {
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"value": "๐ Title: IntrinsicAvatar: Physically Based Inverse Rendering of Dynamic Humans from Monocular Videos via Explicit Ray Tracing ๐",
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"raw": "๐ Description: IntrinsicAvatar is a method for extracting high-quality geometry, albedo, material, and lighting properties of clothed human avatars from monocular videos using explicit ray tracing and volumetric scattering, enabling realistic animations under varying lighting conditions.",
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Conference: CVPR, Jun 17-21, 2024 | Seattle WA, USA ๐บ๐ธ",
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Conference: CVPR, Jun 17-21, 2024 | Seattle WA, USA ๐บ๐ธ",
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] | ๐๐บ๐ New Research Alert - CVPR 2024 (Avatars Collection)! ๐๐๐
๐ Title: IntrinsicAvatar: Physically Based Inverse Rendering of Dynamic Humans from Monocular Videos via Explicit Ray Tracing ๐
๐ Description: IntrinsicAvatar is a method for extracting high-quality geometry, albedo, material, and lighting properties of clothed human avatars from monocular videos using explicit ray tracing and volumetric scattering, enabling realistic animations under varying lighting conditions.
๐ฅ Authors: Shaofei Wang, Boลพidar Antiฤ, Andreas Geiger, and Siyu Tang
๐
Conference: CVPR, Jun 17-21, 2024 | Seattle WA, USA ๐บ๐ธ
๐ Paper: https://huggingface.co/papers/2312.05210
๐ Github Page: https://neuralbodies.github.io/IntrinsicAvatar/
๐ Repository: https://github.com/taconite/IntrinsicAvatar
๐บ Video: https://www.youtube.com/watch?v=aS8AIxgVXzI
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The Emilia dataset dropped last week, and it's a cool one:
- 101k+ hours of high-quality audio
- 6 languages: ๐จ๐ณ ๐บ๐ธ ๐ฏ๐ต ๐ฐ๐ท ๐ฉ๐ช ๐ซ๐ท
- Diverse content: talk shows, interviews, debates, sports commentary, audiobooks
This dataset could improve multilingual speech generation and recognition. Opens up many possibilities for global media, language learning, and accessibility!
Explore it: https://huggingface.co/datasets/amphion/Emilia
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Excited to announce WizardLM new Paper: Auto Evol-Instruct!
๐ฆ Twitter: https://x.com/WizardLM_AI/status/1812857977122202087
๐ Paper: https://arxiv.org/pdf/2406.00770
๐ค 1. Fully AI-Powered Pipeline
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๐2. Scaling Evol-Instruct with Arena Learning
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] | @seyonec It's a great help to experiment with ChemBERTa.
BTW, There are several models that handle SMILES in the model repository. Can you kindly recommend the one with the best performance in handling hERG dataset?
https://paperswithcode.com/dataset/herg
Best,
Joo-Haeng Lee, Pebblous Inc.
http://pebblous.ai | {
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] | New model drop...๐ฅ
FROSTING LANE REDUX
The v1 of this model was released during a big model push, so I think it got lost in the shuffle. I revisited it for a project and realized it wasn't inventive enough around certain concepts, so I decided to retrain.
https://huggingface.co/alvdansen/frosting_lane_redux
I think the original model was really strong on it's own, but because it was trained on fewer images I found that it was producing a very lackluster range of facial expressions, so I wanted to improve that.
The hardest part of creating models like this, I find, is maintaining the detailed linework without without overfitting. It takes a really balanced dataset and I repeat the data 12 times during the process, stopping at the last 10-20 epochs.
It is very difficult to predict the exact amount of time needed, so for me it is crucial to do epoch stops. Every model has a different threshold for ideal success.
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Conference: ECCV, 29 Sep โ 4 Oct, 2024 | Milano, Italy ๐ฎ๐น",
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Conference: ECCV, 29 Sep โ 4 Oct, 2024 | Milano, Italy ๐ฎ๐น",
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] | ๐ฅ๐ญ๐ New Research Alert - ECCV 2024 (Avatars Collection)! ๐๐ญ๐ฅ
๐ Title: RodinHD: High-Fidelity 3D Avatar Generation with Diffusion Models ๐
๐ Description: RodinHD generates high-fidelity 3D avatars from portrait images using a novel data scheduling strategy and weight consolidation regularization to capture intricate details such as hairstyles.
๐ฅ Authors: Bowen Zhang, @yiji, @chunyuwang, Ting Zhang, @jiaolong, Yansong Tang, Feng Zhao, Dong Chen, and Baining Guo
๐
Conference: ECCV, 29 Sep โ 4 Oct, 2024 | Milano, Italy ๐ฎ๐น
๐ Paper: https://huggingface.co/papers/2407.06938
๐ Github Page: https://rodinhd.github.io/
๐ Repository: https://github.com/RodinHD/RodinHD
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๐ Added to the Avatars Collection: https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
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I asked 8 LLMs to "Tell me a bedtime story about bears and waffles."
Claude 3.5 Sonnet and GPT-4o gave me the worst stories: no conflict, no moral, zero creativity.
In contrast, smaller models were quite creative and wrote stories involving talking waffle trees and bears ostracized for their love of waffles.
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I mapped it to the hero's journey to have some kind of framework. Prompt engineering can definitely help here, but it's still disappointing that the larger models don't create better stories right off the bat.
Do you know why smaller models outperform the frontier models here? | {
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] | ๐ The Ghost 8B Beta model outperforms prominent models such as Llama 3 8B Instruct, GPT 3.5 Turbo in the lc_winrate score. In addition, it also outperforms Claude 3 Opus, Claude 3 Sonnet, GPT-4, and Mistral Large when comparing the winrate score of AlpacaEval 2.0.
Ghost 8B Beta is a large language model developed with goals that include excellent multilingual support, superior knowledge capabilities, and cost-effectiveness. The model comes in two context length versions, 8k and 128k, along with multilingual function tools support by default.
The languages supported are ๐บ๐ธ English, ๐ซ๐ท French, ๐ฎ๐น Italian, ๐ช๐ธ Spanish, ๐ต๐น Portuguese, ๐ฉ๐ช German, ๐ป๐ณ Vietnamese, ๐ฐ๐ท Korean and ๐จ๐ณ Chinese.
Explore the Potential:
To learn more about this groundbreaking language model, visit the official website or explore the online demo platforms:
- Ghost 8B Beta (ฮฒ, 8k) on Spaces: https://huggingface.co/spaces/lamhieu/ghost-8b-beta-8k.
- Ghost 8B Beta (ฮฒ, 128k) on Spaces: https://huggingface.co/spaces/lamhieu/ghost-8b-beta-128k
- Official website: https://ghost-x.org/docs/models/ghost-8b-beta
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I've been in the lab synthesizing captions, with my trusty sidekick Blip, and along the way I had an interesting idea. I thought of designing an incredibly simple model that accepts simple instruction pairs, adjective noun pairs specifically, and outputs 2d vertices.
The current implementation has been implemented by myself then ran over with Claude, not because I am incompetent, but because I recognize tools written by experts may have more technique than my newbie self.
As with all projects, this will be updated with proportion to the feedback received, if someone's using it and wants to keep using it, i'm happy to keep working on anything. Thanks, all! ๐ค
`-<3`
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] | ๐ I'm excited to announce that huggingface_hub's InferenceClient now supports OpenAI's Python client syntax! For developers integrating AI into their codebases, this means you can switch to open-source models with just three lines of code. Here's a quick example of how easy it is.
Why use the InferenceClient?
๐ Seamless transition: keep your existing code structure while leveraging LLMs hosted on the Hugging Face Hub.
๐ค Direct integration: easily launch a model to run inference using our Inference Endpoint service.
๐ Stay Updated: always be in sync with the latest Text-Generation-Inference (TGI) updates.
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Gradio-Lite, the in-browser ver. of Gradio, gives it a rich interface using only Python code, even for such an in-browser AI app!
Try out a chat app that runs completely inside your browser ๐
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] | Still following your human intuition to mix corpora from different sources for pre-training ๐ง ? Everyone says that data mixture has a big impact on model performance, but how - and why๐ต๏ธ? Did you know that web corpora are actually highly impactful for downstream tasks ๐?
Check out our preprint "RegMix: Data Mixture as Regression for Language Model Pre-training" ๐
๐ฌ In this paper, we've proposed an automatic data mixture method RegMix that achieves a 6.3% improvement over human selection on the widely used HellaSwag benchmark - and it only needs a 2% extra training FLOPs! ๐
๐ Paper: https://huggingface.co/papers/2407.01492
๐ป Code: https://github.com/sail-sg/regmix
๐ Collection: https://huggingface.co/collections/sail/regmix-data-mixture-as-regression-6682b6caab37b9442877f0ce
๐ฎ Demo: https://huggingface.co/spaces/sail/RegMix
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] | Hi everyone ๐ค
You can easily use the new Model Explorer from Google (https://github.com/google-ai-edge/model-explorer) here: https://huggingface.co/spaces/1aurent/model-explorer
Unfortunately, it doesn't look like it can easily be preloaded with some models, so you'll have to bring your own. Here is a quick selection of models you can use:
- https://huggingface.co/openai-community/gpt2/blob/main/64-8bits.tflite
- https://huggingface.co/qualcomm/ResNet50/blob/main/ResNet50.tflite
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Hey folks!
We built Korvus, an open-source RAG (Retrieval-Augmented Generation) pipeline that consolidates the entire RAG workflow - from embedding generation to text generation - into a single SQL query, significantly reducing architectural complexity and latency.
https://github.com/postgresml/korvus
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- Full RAG pipeline (embedding generation, vector search, reranking, and text generation) in one SQL query
- SDKs for Python, JavaScript, and Rust (more languages planned)
- Built on PostgreSQL, leveraging pgvector and pgml
- Open-source, with support for open models
- Designed for high performance and scalability
We're eager to get feedback from the community and welcome contributions. Check out our GitHub repo for more details:
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"raw": "Learn More: ๐ผ๏ธ",
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Key Findings:๐
VLMs, including GPT-4o, Gemini-1.5 Pro, Claude-3 Sonnet, and Claude-3.5 Sonnet, struggle with basic visual tasks.
Tasks such as identifying where lines intersect or counting basic shapes are challenging for these models.
The authors noted, "The shockingly poor performance of four state-of-the-art VLMs suggests their vision is, at best, like of a person with myopia seeing fine details as blurry, and at worst, like an intelligent person that is blind making educated guesses"โ(Vision Language Models Are Blind; 2024)โ.
Human-like Myopia? ๐
VLMs may have a blind spot similar to human myopia.
This limitation makes it difficult for VLMs to perceive details.
Suggests a potential parallel between human and machine vision limitations.
Technical Details: ๐ง
The researchers created a new benchmark called BlindTest.
BlindTest consists of simple visual tasks to evaluate VLMs low-level vision capabilities.
Four VLMs were assessed using BlindTest.
Many shortcomings were revealed in the models ability to process basic visual information.
Learn More: ๐ผ๏ธ
For a deeper dive into this research, check out the project page: https://vlmsareblind.github.io/
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"value": "This unlocks a world of possibilities for in-browser video editing! ๐คฏ What will you build? ๐",
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] | Introducing Whisper Timestamped: Multilingual speech recognition with word-level timestamps, running 100% locally in your browser thanks to ๐ค Transformers.js! Check it out!
๐ https://huggingface.co/spaces/Xenova/whisper-word-level-timestamps ๐
This unlocks a world of possibilities for in-browser video editing! ๐คฏ What will you build? ๐
Source code: https://github.com/xenova/transformers.js/tree/v3/examples/whisper-word-timestamps | {
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] | Ghost 8B Beta is a large language model developed with goals that include excellent multilingual support, superior knowledge capabilities, and cost-effectiveness. The model comes in two context length versions, 8k and 128k, along with multilingual function tools support by default.
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* ๐จโ๐ป Try on Spaces: https://huggingface.co/spaces/lamhieu/ghost-8b-beta-8k
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"value": "๐ก This gave them their key idea: During decoding, rather than picking the token with the highest logit, ๐๐ต๐ ๐ป๐ผ๐ ๐ฝ๐ถ๐ฐ๐ธ ๐๐ต๐ฒ ๐๐ผ๐ธ๐ฒ๐ป ๐๐ถ๐๐ต ๐๐ต๐ฒ ๐บ๐ผ๐๐ ๐ถ๐บ๐ฝ๐ฟ๐ฒ๐๐๐ถ๐๐ฒ ๐ถ๐ป๐ฐ๐ฟ๐ฒ๐ฎ๐๐ฒ ๐ถ๐ป ๐น๐ผ๐ด๐ถ๐ ๐ฎ๐ฐ๐ฟ๐ผ๐๐ ๐น๐ฎ๐๐ฒ๐ฟ๐?",
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"value": "๐ ๐ฑ% - ๐ฎ๐ฌ% ๐ฏ๐ฎ๐๐ฒ ๐ฝ๐ผ๐ถ๐ป๐๐ ๐ถ๐ป๐ฐ๐ฟ๐ฒ๐ฎ๐๐ฒ ๐ฎ๐ฐ๐ฟ๐ผ๐๐ ๐๐ต๐ฒ ๐ฏ๐ฒ๐ป๐ฐ๐ต๐บ๐ฎ๐ฟ๐ธ๐",
"raw": "๐ ๐ฑ% - ๐ฎ๐ฌ% ๐ฏ๐ฎ๐๐ฒ ๐ฝ๐ผ๐ถ๐ป๐๐ ๐ถ๐ป๐ฐ๐ฟ๐ฒ๐ฎ๐๐ฒ ๐ฎ๐ฐ๐ฟ๐ผ๐๐ ๐๐ต๐ฒ ๐ฏ๐ฒ๐ป๐ฐ๐ต๐บ๐ฎ๐ฟ๐ธ๐",
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"value": "๐ For instance on TruthfulQA / Open-ended, across all model sizes the increase in truthfulness is 14 base points, which is ๐ฎ๐ฟ๐ผ๐๐ป๐ฑ ๐ฐ๐ฌ% ๐ถ๐บ๐ฝ๐ฟ๐ผ๐๐ฒ๐บ๐ฒ๐ป๐ ๐ฐ๐ผ๐บ๐ฝ๐ฎ๐ฟ๐ฒ๐ฑ ๐๐ผ ๐๐๐ฎ๐ป๐ฑ๐ฎ๐ฟ๐ฑ ๐ฑ๐ฒ๐ฐ๐ผ๐ฑ๐ถ๐ป๐ด!",
"raw": "๐ For instance on TruthfulQA / Open-ended, across all model sizes the increase in truthfulness is 14 base points, which is ๐ฎ๐ฟ๐ผ๐๐ป๐ฑ ๐ฐ๐ฌ% ๐ถ๐บ๐ฝ๐ฟ๐ผ๐๐ฒ๐บ๐ฒ๐ป๐ ๐ฐ๐ผ๐บ๐ฝ๐ฎ๐ฟ๐ฒ๐ฑ ๐๐ผ ๐๐๐ฎ๐ป๐ฑ๐ฎ๐ฟ๐ฑ ๐ฑ๐ฒ๐ฐ๐ผ๐ฑ๐ถ๐ป๐ด!",
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"value": "๐ค Wouldn't decoding take longer because of this added contrasting step? ๐ ๐ง๐ต๐ฒ ๐ฟ๐๐ป๐๐ถ๐บ๐ฒ ๐ถ๐ป๐ฐ๐ฟ๐ฒ๐ฎ๐๐ฒ ๐ถ๐ ๐ป๐ฒ๐ด๐น๐ถ๐ด๐ถ๐ฏ๐น๐ฒ, ๐ญ ๐๐ผ ๐ด% ๐ผ๐ป๐น๐.",
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"value": "Paper added to my collection ๐ ",
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] | ๐๐๐ฐ ๐๐๐๐จ๐๐ข๐ง๐ ๐ญ๐๐๐ก๐ง๐ข๐ช๐ฎ๐ ๐ข๐ง ๐ญ๐ซ๐๐ง๐ฌ๐๐จ๐ซ๐ฆ๐๐ซ๐ฌ ๐ฌ๐ข๐ ๐ง๐ข๐๐ข๐๐๐ง๐ญ๐ฅ๐ฒ ๐ซ๐๐๐ฎ๐๐๐ฌ ๐ก๐๐ฅ๐ฅ๐ฎ๐๐ข๐ง๐๐ญ๐ข๐จ๐ง๐ฌ ๐
DoLa decoding, which made a conference paper at ICLR '24, has just been merged in Transformers by @joaogante and Yung-Sung Chuang.
This new decoding method is simple yet extremely impressive!
Reminder: Decoder LLMs (the GPT kind of LLM, the most common one) generate their outputs one token at a time: at each step, given a current text, they compute a logit for each token in their vocabulary that should represent the probability of this token coming next.
Then they either pick the highest logit token (greedy decoding) or sample one with a probability defined by the logits (sampling).
The authors of DoLa wanted to improve that simple method.
They knew this established fact that transformer LMs encode low-level info (like base syntax) in early layers and more high-level info like knowledge in the later layers.
๐ก This gave them their key idea: During decoding, rather than picking the token with the highest logit, ๐๐ต๐ ๐ป๐ผ๐ ๐ฝ๐ถ๐ฐ๐ธ ๐๐ต๐ฒ ๐๐ผ๐ธ๐ฒ๐ป ๐๐ถ๐๐ต ๐๐ต๐ฒ ๐บ๐ผ๐๐ ๐ถ๐บ๐ฝ๐ฟ๐ฒ๐๐๐ถ๐๐ฒ ๐ถ๐ป๐ฐ๐ฟ๐ฒ๐ฎ๐๐ฒ ๐ถ๐ป ๐น๐ผ๐ด๐ถ๐ ๐ฎ๐ฐ๐ฟ๐ผ๐๐ ๐น๐ฎ๐๐ฒ๐ฟ๐?
This gives impressive results:
๐ ๐ฑ% - ๐ฎ๐ฌ% ๐ฏ๐ฎ๐๐ฒ ๐ฝ๐ผ๐ถ๐ป๐๐ ๐ถ๐ป๐ฐ๐ฟ๐ฒ๐ฎ๐๐ฒ ๐ฎ๐ฐ๐ฟ๐ผ๐๐ ๐๐ต๐ฒ ๐ฏ๐ฒ๐ป๐ฐ๐ต๐บ๐ฎ๐ฟ๐ธ๐
๐ For instance on TruthfulQA / Open-ended, across all model sizes the increase in truthfulness is 14 base points, which is ๐ฎ๐ฟ๐ผ๐๐ป๐ฑ ๐ฐ๐ฌ% ๐ถ๐บ๐ฝ๐ฟ๐ผ๐๐ฒ๐บ๐ฒ๐ป๐ ๐ฐ๐ผ๐บ๐ฝ๐ฎ๐ฟ๐ฒ๐ฑ ๐๐ผ ๐๐๐ฎ๐ป๐ฑ๐ฎ๐ฟ๐ฑ ๐ฑ๐ฒ๐ฐ๐ผ๐ฑ๐ถ๐ป๐ด!
๐ค Wouldn't decoding take longer because of this added contrasting step? ๐ ๐ง๐ต๐ฒ ๐ฟ๐๐ป๐๐ถ๐บ๐ฒ ๐ถ๐ป๐ฐ๐ฟ๐ฒ๐ฎ๐๐ฒ ๐ถ๐ ๐ป๐ฒ๐ด๐น๐ถ๐ด๐ถ๐ฏ๐น๐ฒ, ๐ญ ๐๐ผ ๐ด% ๐ผ๐ป๐น๐.
Paper added to my collection ๐ https://huggingface.co/collections/m-ric/optimization-mechanics-661d543a5fc6ca1dc84284a0 | {
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