Llama can now see and run on your device - welcome Llama 3.2
•
169
Omar Sanseviero
osanseviero
AI & ML interests
Llamas, model merging, massive ASR for data collection, 3D ML, on-device ML, quantization, model judging, ML in browser, healthcare applications, education, intersection of art and ML.🦙
Recent Activity
liked
a Space
3 days ago
Qwen/Qwen2.5-Turbo-1M-Demo
liked
a model
3 days ago
mistralai/Pixtral-Large-Instruct-2411
Articles
Organizations
osanseviero's activity
reacted to
Xenova's
post with 🔥
3 days ago
Post
3581
Have you tried out 🤗 Transformers.js v3? Here are the new features:
⚡ WebGPU support (up to 100x faster than WASM)
🔢 New quantization formats (dtypes)
🏛 120 supported architectures in total
📂 25 new example projects and templates
🤖 Over 1200 pre-converted models
🌐 Node.js (ESM + CJS), Deno, and Bun compatibility
🏡 A new home on GitHub and NPM
Get started with
Learn more in our blog post: https://huggingface.co/blog/transformersjs-v3
⚡ WebGPU support (up to 100x faster than WASM)
🔢 New quantization formats (dtypes)
🏛 120 supported architectures in total
📂 25 new example projects and templates
🤖 Over 1200 pre-converted models
🌐 Node.js (ESM + CJS), Deno, and Bun compatibility
🏡 A new home on GitHub and NPM
Get started with
npm i @huggingface/transformers.Learn more in our blog post: https://huggingface.co/blog/transformersjs-v3
- 2 replies
reacted to
maxiw's
post with 🤗🚀👍🔥❤️
8 days ago
Post
4491
I was curious to see what people post here on HF so I created a dataset with all HF Posts:
maxiw/hf-posts
Some interesting stats:
Top 5 Authors by Total Impressions:
-----------------------------------
@merve : 171,783 impressions (68 posts)
@fdaudens : 135,253 impressions (81 posts)
@singhsidhukuldeep : 122,591 impressions (81 posts)
@akhaliq : 119,526 impressions (78 posts)
@MonsterMMORPG : 112,500 impressions (45 posts)
Top 5 Users by Number of Reactions Given:
----------------------------------------
@osanseviero : 1278 reactions
@clem : 910 reactions
@John6666 : 899 reactions
@victor : 674 reactions
@samusenps : 655 reactions
Top 5 Most Used Reactions:
-------------------------
❤️: 7048 times
🔥: 5921 times
👍: 4856 times
🚀: 2549 times
🤗: 2065 times
Some interesting stats:
Top 5 Authors by Total Impressions:
-----------------------------------
@merve : 171,783 impressions (68 posts)
@fdaudens : 135,253 impressions (81 posts)
@singhsidhukuldeep : 122,591 impressions (81 posts)
@akhaliq : 119,526 impressions (78 posts)
@MonsterMMORPG : 112,500 impressions (45 posts)
Top 5 Users by Number of Reactions Given:
----------------------------------------
@osanseviero : 1278 reactions
@clem : 910 reactions
@John6666 : 899 reactions
@victor : 674 reactions
@samusenps : 655 reactions
Top 5 Most Used Reactions:
-------------------------
❤️: 7048 times
🔥: 5921 times
👍: 4856 times
🚀: 2549 times
🤗: 2065 times
- 9 replies
reacted to
tomaarsen's
post with 🚀🔥
about 1 month ago
Post
6313
📣 Sentence Transformers v3.2.0 is out, marking the biggest release for inference in 2 years! 2 new backends for embedding models: ONNX (+ optimization & quantization) and OpenVINO, allowing for speedups up to 2x-3x AND Static Embeddings for 500x speedups at 10-20% accuracy cost.
1️⃣ ONNX Backend: This backend uses the ONNX Runtime to accelerate model inference on both CPU and GPU, reaching up to 1.4x-3x speedup depending on the precision. We also introduce 2 helper methods for optimizing and quantizing models for (much) faster inference.
2️⃣ OpenVINO Backend: This backend uses Intel their OpenVINO instead, outperforming ONNX in some situations on CPU.
Usage is as simple as
🔒 Another major new feature is Static Embeddings: think word embeddings like GLoVe and word2vec, but modernized. Static Embeddings are bags of token embeddings that are summed together to create text embeddings, allowing for lightning-fast embeddings that don't require any neural networks. They're initialized in one of 2 ways:
1️⃣ via Model2Vec, a new technique for distilling any Sentence Transformer models into static embeddings. Either via a pre-distilled model with
2️⃣ Random initialization. This requires finetuning, but finetuning is extremely quick (e.g. I trained with 3 million pairs in 7 minutes). My final model was 6.6% worse than bge-base-en-v1.5, but 500x faster on CPU.
Full release notes: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.2.0
Documentation on Speeding up Inference: https://sbert.net/docs/sentence_transformer/usage/efficiency.html
1️⃣ ONNX Backend: This backend uses the ONNX Runtime to accelerate model inference on both CPU and GPU, reaching up to 1.4x-3x speedup depending on the precision. We also introduce 2 helper methods for optimizing and quantizing models for (much) faster inference.
2️⃣ OpenVINO Backend: This backend uses Intel their OpenVINO instead, outperforming ONNX in some situations on CPU.
Usage is as simple as
SentenceTransformer("all-MiniLM-L6-v2", backend="onnx"). Does your model not have an ONNX or OpenVINO file yet? No worries - it'll be autoexported for you. Thank me later 😉🔒 Another major new feature is Static Embeddings: think word embeddings like GLoVe and word2vec, but modernized. Static Embeddings are bags of token embeddings that are summed together to create text embeddings, allowing for lightning-fast embeddings that don't require any neural networks. They're initialized in one of 2 ways:
1️⃣ via Model2Vec, a new technique for distilling any Sentence Transformer models into static embeddings. Either via a pre-distilled model with
from_model2vec or with from_distillation where you do the distillation yourself. It'll only take 5 seconds on GPU & 2 minutes on CPU, no dataset needed.2️⃣ Random initialization. This requires finetuning, but finetuning is extremely quick (e.g. I trained with 3 million pairs in 7 minutes). My final model was 6.6% worse than bge-base-en-v1.5, but 500x faster on CPU.
Full release notes: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.2.0
Documentation on Speeding up Inference: https://sbert.net/docs/sentence_transformer/usage/efficiency.html
- 1 reply
reacted to
nyuuzyou's
post with 👀
about 1 month ago
Post
1941
🎓 Introducing Doc4web.ru Documents Dataset -
nyuuzyou/doc4web
Dataset highlights:
- 223,739 documents from doc4web.ru, a document hosting platform for students and teachers
- Primarily in Russian, with some English and potentially other languages
- Each entry includes: URL, title, download link, file path, and content (where available)
- Contains original document files in addition to metadata
- Data reflects a wide range of educational topics and materials
- Licensed under Creative Commons Zero (CC0) for unrestricted use
The dataset can be used for analyzing educational content in Russian, text classification tasks, and information retrieval systems. It's also valuable for examining trends in educational materials and document sharing practices in the Russian-speaking academic community. The inclusion of original files allows for in-depth analysis of various document formats and structures.
Dataset highlights:
- 223,739 documents from doc4web.ru, a document hosting platform for students and teachers
- Primarily in Russian, with some English and potentially other languages
- Each entry includes: URL, title, download link, file path, and content (where available)
- Contains original document files in addition to metadata
- Data reflects a wide range of educational topics and materials
- Licensed under Creative Commons Zero (CC0) for unrestricted use
The dataset can be used for analyzing educational content in Russian, text classification tasks, and information retrieval systems. It's also valuable for examining trends in educational materials and document sharing practices in the Russian-speaking academic community. The inclusion of original files allows for in-depth analysis of various document formats and structures.
reacted to
merve's
post with 🔥
about 1 month ago
Post
3740
Meta AI vision has been cooking
@facebook
They shipped multiple models and demos for their papers at @ECCV 🤗
Here's a compilation of my top picks:
- Sapiens is family of foundation models for human-centric depth estimation, segmentation and more, all models have open weights and demos 👏
All models have their demos and even torchscript checkpoints!
A collection of models and demos: facebook/sapiens-66d22047daa6402d565cb2fc
- VFusion3D is state-of-the-art consistent 3D generation model from images
Model: facebook/vfusion3d
Demo: facebook/VFusion3D
- CoTracker is the state-of-the-art point (pixel) tracking model
Demo: facebook/cotracker
Model: facebook/cotracker
They shipped multiple models and demos for their papers at @ECCV 🤗
Here's a compilation of my top picks:
- Sapiens is family of foundation models for human-centric depth estimation, segmentation and more, all models have open weights and demos 👏
All models have their demos and even torchscript checkpoints!
A collection of models and demos: facebook/sapiens-66d22047daa6402d565cb2fc
- VFusion3D is state-of-the-art consistent 3D generation model from images
Model: facebook/vfusion3d
Demo: facebook/VFusion3D
- CoTracker is the state-of-the-art point (pixel) tracking model
Demo: facebook/cotracker
Model: facebook/cotracker
reacted to
fdaudens's
post with 🧠🤗👀🔥
about 1 month ago
Post
3037
The Nobel Prize background for Hopfield and Hinton's work on neural networks is pure gold. It's a masterclass in explaining AI basics.
Key takeaways from the conclusion:
- ML applications are expanding rapidly. We're still figuring out which will stick.
- Ethical discussions are crucial as the tech develops.
- Physics 🤝 AI: A two-way street of innovation.
Some mind-blowing AI applications in physics:
- Discovering the Higgs particle
- Cleaning up gravitational wave data
- Hunting exoplanets
- Predicting molecular structures
- Designing better solar cells
We're just scratching the surface. The interplay between AI and physics is reshaping both fields.
Bonus: The illustrations accompanying the background document are really neat. (Credit: Johan Jarnestad/The Royal Swedish Academy of Sciences)
#AI #MachineLearning #Physics #Ethics #Innovation
Key takeaways from the conclusion:
- ML applications are expanding rapidly. We're still figuring out which will stick.
- Ethical discussions are crucial as the tech develops.
- Physics 🤝 AI: A two-way street of innovation.
Some mind-blowing AI applications in physics:
- Discovering the Higgs particle
- Cleaning up gravitational wave data
- Hunting exoplanets
- Predicting molecular structures
- Designing better solar cells
We're just scratching the surface. The interplay between AI and physics is reshaping both fields.
Bonus: The illustrations accompanying the background document are really neat. (Credit: Johan Jarnestad/The Royal Swedish Academy of Sciences)
#AI #MachineLearning #Physics #Ethics #Innovation
-
- 1 reply
reacted to
reach-vb's
post with 🔥👍
about 1 month ago
Post
2050
On-device AI framework ecosystem is blooming these days:
1. llama.cpp - All things Whisper, LLMs & VLMs - run across Metal, CUDA and other backends (AMD/ NPU etc)
https://github.com/ggerganov/llama.cpp
2. MLC - Deploy LLMs across platforms especially WebGPU (fastest WebGPU LLM implementation out there)
https://github.com/mlc-ai/web-llm
3. MLX - Arguably the fastest general purpose framework (Mac only) - Supports all major Image Generation (Flux, SDXL, etc), Transcription (Whisper), LLMs
https://github.com/ml-explore/mlx-examples
4. Candle - Cross-platform general purpose framework written in Rust - wide coverage across model categories
https://github.com/huggingface/candle
Honorable mentions:
1. Transformers.js - Javascript (WebGPU) implementation built on top of ONNXruntimeweb
https://github.com/xenova/transformers.js
2. Mistral rs - Rust implementation for LLMs & VLMs, built on top of Candle
https://github.com/EricLBuehler/mistral.rs
3. Ratchet - Cross platform, rust based WebGPU framework built for battle-tested deployments
https://github.com/huggingface/ratchet
4. Zml - Cross platform, Zig based ML framework
https://github.com/zml/zml
Looking forward to how the ecosystem would look 1 year from now - Quite bullish on the top 4 atm - but open source ecosystem changes quite a bit! 🤗
Also, which frameworks did I miss?
1. llama.cpp - All things Whisper, LLMs & VLMs - run across Metal, CUDA and other backends (AMD/ NPU etc)
https://github.com/ggerganov/llama.cpp
2. MLC - Deploy LLMs across platforms especially WebGPU (fastest WebGPU LLM implementation out there)
https://github.com/mlc-ai/web-llm
3. MLX - Arguably the fastest general purpose framework (Mac only) - Supports all major Image Generation (Flux, SDXL, etc), Transcription (Whisper), LLMs
https://github.com/ml-explore/mlx-examples
4. Candle - Cross-platform general purpose framework written in Rust - wide coverage across model categories
https://github.com/huggingface/candle
Honorable mentions:
1. Transformers.js - Javascript (WebGPU) implementation built on top of ONNXruntimeweb
https://github.com/xenova/transformers.js
2. Mistral rs - Rust implementation for LLMs & VLMs, built on top of Candle
https://github.com/EricLBuehler/mistral.rs
3. Ratchet - Cross platform, rust based WebGPU framework built for battle-tested deployments
https://github.com/huggingface/ratchet
4. Zml - Cross platform, Zig based ML framework
https://github.com/zml/zml
Looking forward to how the ecosystem would look 1 year from now - Quite bullish on the top 4 atm - but open source ecosystem changes quite a bit! 🤗
Also, which frameworks did I miss?
- 1 reply
reacted to
alielfilali01's
post with 🔥
about 1 month ago
Post
1820
Why nobdoy is talking about the new training corpus released by MBZUAI today.
TxT360 is +15 Trillion tokens corpus outperforming FineWeb on several metrics. Ablation studies were done up to 1T tokens.
Read blog here : LLM360/TxT360
Dataset : LLM360/TxT360
TxT360 is +15 Trillion tokens corpus outperforming FineWeb on several metrics. Ablation studies were done up to 1T tokens.
Read blog here : LLM360/TxT360
Dataset : LLM360/TxT360
- 2 replies
reacted to
lucifertrj's
post with 👀
about 1 month ago
Post
1498
AI Agents LlamaIndex in 40 minutes
The video covers code and workflow explanations for:
- Function Calling
- Function Calling Agents + Agent Runner
- Agentic RAG
- REAcT Agent: Build your own Search Assistant Agent
Watch: https://youtu.be/bHn4dLJYIqE
The video covers code and workflow explanations for:
- Function Calling
- Function Calling Agents + Agent Runner
- Agentic RAG
- REAcT Agent: Build your own Search Assistant Agent
Watch: https://youtu.be/bHn4dLJYIqE
reacted to
fdaudens's
post with 🔥
about 1 month ago
Post
1970
This is how AI can be useful in journalism: Just tested DataTalk - a tool that lets you dig through campaign finance data with just your words.
It's transforming complex FEC filings and OpenSecrets datasets into actionable insights for journalists.
Key features for newsrooms:
- Natural language queries on FEC data
- Rapid insights on donors, spending, special interests
- SQL access for deep dives
Tested it out:
- Retrieved how much Harris and Trump raised
- Found top donors instantly (#1 is Timothy Mellon—have you heard about him?)
- Uncovered big self-funders like David Trone ($62M)
Pros:
- Saves hours of data wrangling
- Surfaces story leads quickly
- Transparent AI retrieving steps makes this tool auditable
Awesome work by Stanford University Open Virtual Assistant Lab, Big Local News, and Columbia University - Graduate School of Journalism. Expert-guided.
Remember: Always verify. Use for leads, not final copy. But this is gold for finding new leads.
How might this change campaign finance reporting? What other datasets need this treatment?
Try it out: https://www.datatalk.genie.stanford.edu/
#AIJournalism #campaignfinance #datajournalism #election2024
It's transforming complex FEC filings and OpenSecrets datasets into actionable insights for journalists.
Key features for newsrooms:
- Natural language queries on FEC data
- Rapid insights on donors, spending, special interests
- SQL access for deep dives
Tested it out:
- Retrieved how much Harris and Trump raised
- Found top donors instantly (#1 is Timothy Mellon—have you heard about him?)
- Uncovered big self-funders like David Trone ($62M)
Pros:
- Saves hours of data wrangling
- Surfaces story leads quickly
- Transparent AI retrieving steps makes this tool auditable
Awesome work by Stanford University Open Virtual Assistant Lab, Big Local News, and Columbia University - Graduate School of Journalism. Expert-guided.
Remember: Always verify. Use for leads, not final copy. But this is gold for finding new leads.
How might this change campaign finance reporting? What other datasets need this treatment?
Try it out: https://www.datatalk.genie.stanford.edu/
#AIJournalism #campaignfinance #datajournalism #election2024
reacted to
philipp-zettl's
post with 👀
about 2 months ago
Post
1393
🚀 Finishing up the prototype of my weekend project called ChessPT 🚀
- The game state is now being rendered. This simplifies coming up with own new moves
- The model space philipp-zettl/ChessPT was updated to provide an interactive mode.
- The space is currently running v0.4 of philipp-zettl/chessPT
- New updates will come this week.
- Training runs will be logged under https://wandb.ai/philipp-zettl/chessPT/
**Note**: The model is still not performing on a level that I want it to. It predicts too frequently invalid moves (according to the game state). In addition to that the post-processing step is a little faulty, so it might be possible that you end up in a state where the model didn't provide a next move.
- The game state is now being rendered. This simplifies coming up with own new moves
- The model space philipp-zettl/ChessPT was updated to provide an interactive mode.
- The space is currently running v0.4 of philipp-zettl/chessPT
- New updates will come this week.
- Training runs will be logged under https://wandb.ai/philipp-zettl/chessPT/
**Note**: The model is still not performing on a level that I want it to. It predicts too frequently invalid moves (according to the game state). In addition to that the post-processing step is a little faulty, so it might be possible that you end up in a state where the model didn't provide a next move.