๐๐ญ๐ New Research Alert - WACV 2025 (Avatars Collection)! ๐๐ญ๐ ๐ Title: EmoVOCA: Speech-Driven Emotional 3D Talking Heads ๐
๐ Description: EmoVOCA is a data-driven method for generating emotional 3D talking heads by combining speech-driven lip movements with expressive facial dynamics. This method has been developed to overcome the limitations of corpora and to achieve state-of-the-art animation quality.
๐ฅ Authors: @FedeNoce, Claudio Ferrari, and Stefano Berretti
๐ Conference: WACV, 28 Feb โ 4 Mar, 2025 | Arizona, USA ๐บ๐ธ
Another impressive model that joined the ranking today is ALLaM-AI/ALLaM-7B-Instruct-preview. After a long wait finally ALLaM is here and it is IMPRESSIVE given its size !
Google just released PaliGemma 2 Mix: new versatile instruction vision language models ๐ฅ
> Three new models: 3B, 10B, 28B with res 224, 448 ๐ > Can do vision language tasks with open-ended prompts, understand documents, and segment or detect anything ๐คฏ
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.
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
Will we soon all have our own personalized AI news agents? And what does it mean for journalism?
Just built a simple prototype based on the Hugging Face course. It lets you get customized news updates on any topic.
Not perfect yet, but you can see where things could go: we'll all be able to build personalized AI agents that curate & analyze news for each of us. And users who could decide to build custom news products for their needs, such as truly personalized newsletters or podcasts.
The implications for both readers & news organizations are significant. To name a few: - Will news articles remain the best format for informing people? - What monetization model will work for news organizations? - How do you create an effective conversion funnel?
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
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
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.
โญ๏ธ The AI Energy Score project just launched - this is a game-changer for making informed decisions about AI deployment.
You can now see exactly how much energy your chosen model will consume, with a simple 5-star rating system. Think appliance energy labels, but for AI.
Looking at transcription models on the leaderboard is fascinating: choosing between whisper-tiny or whisper-large-v3 can make a 7x difference. Real-time data on these tradeoffs changes everything.
166 models already evaluated across 10 different tasks, from text generation to image classification. The whole thing is public and you can submit your own models to test.
Why this matters: - Teams can pick efficient models that still get the job done - Developers can optimize for energy use from day one - Organizations can finally predict their AI environmental impact
If you're building with AI at any scale, definitely worth checking out.
"๐ฎ๐ฌ๐ฎ๐ฑ ๐๐ถ๐น๐น ๐ฏ๐ฒ ๐๐ต๐ฒ ๐๐ฒ๐ฎ๐ฟ ๐ผ๐ณ ๐๐ ๐ฎ๐ด๐ฒ๐ป๐๐": this statement has often been made, here are numbers to support it.
I've plotted the progress of AI agents on GAIA test set, and it seems they're headed to catch up with the human baseline in early 2026.
And that progress is still driven mostly by the improvement of base LLMs: progress would be even faster with fine-tuned agentic models.
๐ฅ Video AI is taking over! Out of 17 papers dropped on Hugging Face today, 6 are video-focused - from Sliding Tile Attention to On-device Sora. The race for next-gen video tech is heating up! ๐ฌ๐
๐ค Robotics > Pi0, first open-source foundation vision-language action model was released in Le Robot (Apache 2.0)
๐ฌ LLMs > Groundbreaking: s1 is simpler approach to test-time scaling, the release comes with small s1K dataset of 1k question-reasoning trace pairs (from Gemini-Thinking Exp) they fine-tune Qwen2.5-32B-Instruct to get s1-32B, outperforming o1-preview on math ๐คฏ s1-32B and s1K is out! > Adyen released DABstep, a new benchmark along with it's leaderboard demo for agents doing data analysis > Krutrim released Krutrim-2 instruct, new 12B model based on NeMo12B trained and aligned on Indic languages, a new multilingual sentence embedding model (based on STSB-XLM-R), and a translation model for Indic languages
๐ Multimodal > PKU released Align-DS-V, a model aligned using their new technique called LLF for all modalities (image-text-audio), along with the dataset Align Anything > OLA-7B is a new any-to-any model by Tencent that can take text, image, video, audio data with context window of 32k tokens and output text and speech in English and Chinese > Krutrim released Chitrarth, a new vision language model for Indic languages and English
๐ผ๏ธ Vision > BiRefNet_HR is a new higher resolution BiRefNet for background removal
๐ฃ๏ธ Audio > kyutai released Hibiki, it's a real-time speech-to-speech translation model ๐คฏ it's available for French-English translation > Krutrim released Dhwani, a new STT model for Indic languages > They also release a new dataset for STT-TTS
๐ผ๏ธ Image Generation > Lumina released Lumina-Image-2.0, a 2B parameter-flow based DiT for text to image generation > Tencent released Hunyuan3D-2, a 3D asset generation model based on DiT and Hunyuan3D-Paint > boreal-hl-v1 is a new boring photorealistic image generation LoRA based on Hunyuan
โก๏ธ How well do reasoning models perform on agentic tasks? Until now, all indicators seemed to show that they worked really well. On our recent reproduction of Deep Search, OpenAI's o1 was by far the best model to power an agentic system.
So when our partner Adyen built a huge benchmark of 450 data science tasks, and built data agents with smolagents to test different models, I expected reasoning models like o1 or DeepSeek-R1 to destroy the tasks at hand.
๐ But they really missed the mark. DeepSeek-R1 only got 1 or 2 out of 10 questions correct. Similarly, o1 was only at ~13% correct answers.
๐ง These results really surprised us. We thoroughly checked them, we even thought our APIs for DeepSeek were broken and colleagues Leandro Anton helped me start custom instances of R1 on our own H100s to make sure it worked well. But there seemed to be no mistake. Reasoning LLMs actually did not seem that smart. Often, these models made basic mistakes, like forgetting the content of a folder that they had just explored, misspelling file names, or hallucinating data. Even though they do great at exploring webpages through several steps, the same level of multi-step planning seemed much harder to achieve when reasoning over files and data.
It seems like there's still lots of work to do in the Agents x Data space. Congrats to Adyen for this great benchmark, looking forward to see people proposing better agents! ๐
๐ข SmolLM2 paper released! Learn how the ๐ค team built one of the best small language models: from data choices to training insights. Check out our findings and share your thoughts! ๐ค๐ก
