Research in business taxation and development, University Dauphine-PSL ๐ | Backed by the Microsoft for Startups Hub program and Google Cloud Platform for startups program | Hugging Face for Legal ๐ค
๐๐ฟ๐ฒ๐ฎ๐ ๐ณ๐ฒ๐ฎ๐๐๐ฟ๐ฒ ๐ฎ๐น๐ฒ๐ฟ๐: you can now share agents to the Hub! ๐ฅณ๐ฅณ
And any agent pushed to Hub get a cool Space interface to directly chat with it.
This was a real technical challenge: for instance, serializing tools to export them meant that you needed to get all the source code for a tool, verify that it was standalone (not relying on external variables), and gathering all the packages required to make it run.
๐ Multimodal > OpenGVLab released InternVideo 2.5 Chat models, new video LMs with long context > AIDC released Ovis2 model family along with Ovis dataset, new vision LMs in different sizes (1B, 2B, 4B, 8B, 16B, 34B), with video and OCR support > ColQwenStella-2b is a multilingual visual retrieval model that is sota in it's size > Hoags-2B-Exp is a new multilingual vision LM with contextual reasoning, long context video understanding
๐ฌ LLMs A lot of math models! > Open-R1 team released OpenR1-Math-220k large scale math reasoning dataset, along with Qwen2.5-220K-Math fine-tuned on the dataset, OpenR1-Qwen-7B > Nomic AI released new Nomic Embed multilingual retrieval model, a MoE with 500 params with 305M active params, outperforming other models > DeepScaleR-1.5B-Preview is a new DeepSeek-R1-Distill fine-tune using distributed RL on math > LIMO is a new fine-tune of Qwen2.5-32B-Instruct on Math
๐ฃ๏ธ Audio > Zonos-v0.1 is a new family of speech recognition models, which contains the model itself and embeddings
๐ผ๏ธ Vision and Image Generation > We have ported DepthPro of Apple to transformers for your convenience! > illustrious-xl-v1.0 is a new illustration generation model
Less is More for Reasoning (LIMO): a 32B model fine-tuned with 817 examples can beat o1-preview on math reasoning! ๐คฏ
Do we really need o1's huge RL procedure to see reasoning emerge? It seems not. Researchers from Shanghai Jiaotong University just demonstrated that carefully selected examples can boost math performance in large language models using SFT โno huge datasets or RL procedures needed.
Their procedure allows Qwen2.5-32B-Instruct to jump from 6.5% to 57% on AIME and from 59% to 95% on MATH, while using only 1% of the data in previous approaches.
โก The Less-is-More Reasoning Hypothesis: โฃ Minimal but precise examples that showcase optimal reasoning patterns matter more than sheer quantity โฃ Pre-training knowledge plus sufficient computational resources at inference levels up math skills
โก๏ธ Core techniques: โฃ High-quality reasoning chains with self-verification steps โฃ 817 handpicked problems that encourage deeper reasoning โฃ Enough inference-time computation to allow extended reasoning
๐ช Efficiency gains: โฃ Only 817 examples instead of 100k+ โฃ 40.5% absolute improvement across 10 diverse benchmarks, outperforming models trained on 100x more data
This really challenges the notion that SFT leads to memorization rather than generalization! And opens up reasoning to GPU-poor researchers ๐
๐ฏ Perplexity drops their FIRST open-weight model on Hugging Face: A decensored DeepSeek-R1 with full reasoning capabilities. Tested on 1000+ examples for unbiased responses.
I am pleased to introduce my first project built upon Hugging Faceโs smolagents framework, integrated with Alpaca for financial market analysis automation ๐ฆ๐ค
The project implements technical indicators such as the Relative Strength Index (RSI) and Bollinger Bands to provide momentum and volatility analysis. Market data is retrieved through the Alpaca API, enabling access to historical price information across various timeframes.
AI-powered insights are generated using Hugging Faceโs inference API, facilitating the analysis of market trends through natural language processing with DuckDuckGo search integration for real-time sentiment analysis based on financial news ๐ฆ
Hugging Face just launched the AI Agents Course โ a free journey from beginner to expert in AI agents!
- Learn AI Agent fundamentals, use cases and frameworks - Use top libraries like LangChain & LlamaIndex - Compete in challenges & earn a certificate - Hands-on projects & real-world applications
๐ช๐ฒ'๐๐ฒ ๐ท๐๐๐ ๐ฟ๐ฒ๐น๐ฒ๐ฎ๐๐ฒ๐ฑ ๐๐บ๐ผ๐น๐ฎ๐ด๐ฒ๐ป๐๐ ๐๐ญ.๐ฏ.๐ฌ ๐, and it comes with a major feature: you can now log agent runs using OpenTelemetry to inspect them afterwards! ๐
This interactive format is IMO much easier to inspect big multi-step runs than endless console logs.
It is now possible to generate 16 Megapixel (4096x4096) raw images with SANA 4K model using under 8GB VRAM, 4 Megapixel (2048x2048) images using under 6GB VRAM, and 1 Megapixel (1024x1024) images using under 4GB VRAM thanks to new optimizations
Implements compute-efficient DeepPCR algorithm which parallelizes sequential operations thus speeding up inference and training of neural networks. DeepPCR can significantly reduce the time complexity in operations such as denoising in latent diffusion space from O(L) to O(log2 L).
I've been in Brazil for 10 days now ๐ง๐ท๐ง๐ท๐ง๐ท
I've been surprised by the gap between the massive number of people interested in AI (chatgpt adoption is crazy here) and the relatively low number of real AI builders - aka people and companies building their own AI models, datasets and apps.
Lots of efforts needed across the world for everyone to participate, control and benefit this foundational technology, starting with open-source & multi-lingual AI, more access to GPUs & AI builder training for all!
Iโve published a new dataset to simplify model merging ๐ค
This dataset facilitates the search for compatible architectures for model merging with @arcee_aiโs mergekit, streamlining the automation of high-performance merge searches ๐
๐ช It's the first time Open-Source coding model of this size class that clearly matches GPT-4o's coding capabilities!
โจ Completes the previous two Qwen 2.5 Coder release with 4 new size: 0.5B, 3B, 14B, 32B ๐ Support long context up to 128K (for the 14B and 32B models) โ Drop-in replacement to GPT-4o as a coding assistant on Cursor or for Artifacts! ๐ค Models available right now on the Hub, under Apache 2.0 license!
A non-Instruct LLM assistant is mostly useless. ๐ง
Since it's mostly a model trained to complete text, when you ask it a question like "What to do during a stopover in Paris?", it can just go on and on adding more details to your question instead of answering, which would be valid to complete text from its training corpus, but not to answer questions.
โก๏ธ So the post-training stage includes an important Instruction tuning step where you teach your model how to be useful : answer questions, be concise, be polite... RLHF is a well known technique for this.
For people interested to understand how this step works, the folks at Adaptive ML have made a great guide!
