🤖 Robotics > Pi0, first open-source foundation vision-language action model was released in Le Robot (Apache 2.0)
💬 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 🤯 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
👀 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
🖼️ Vision > BiRefNet_HR is a new higher resolution BiRefNet for background removal
🗣️ Audio > kyutai released Hibiki, it's a real-time speech-to-speech translation model 🤯 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
🖼️ 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
➡️ 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.
👎 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.
🧐 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! 🚀
OpenAI's latest agentic app Deep Research seems really good... But it's closed, as usual.
⏱️ So with a team of cracked colleagues, we set ourselves a 24hours deadline to replicate and open-source Deep Research! ⏱️
➡️ We built open-Deep-Research, an entirely open agent that can: navigate the web autonomously, scroll and search through pages, download and manipulate files, run calculation on data...
We aimed for the best performance: are the agent's answers really rigorous?
On GAIA benchmark, Deep Research had 67% accuracy on the validation set. ➡️ open Deep Research is at 55% (powered by o1), it is: - the best pass@1 solution submitted - the best open solution 💪💪
And it's only getting started ! Please jump in, drop PRs, and let's bring it to the top !
This week in open AI was 🔥 Let's recap! 🤗 merve/january-31-releases-679a10669bd4030090c5de4d LLMs 💬 > Huge: AllenAI released new Tülu models that outperform DeepSeek R1 using Reinforcement Learning with Verifiable Reward (RLVR) based on Llama 3.1 405B 🔥 > Mistral AI is back to open-source with their "small" 24B models (base & SFT), with Apache 2.0 license 😱 > Alibaba Qwen released their 1M context length models Qwen2.5-Instruct-1M, great for agentic use with Apache 2.0 license 🔥 > Arcee AI released Virtuoso-medium, 32.8B LLMs distilled from DeepSeek V3 with dataset of 5B+ tokens > Velvet-14B is a new family of 14B Italian LLMs trained on 10T tokens in six languages > OpenThinker-7B is fine-tuned version of Qwen2.5-7B-Instruct on OpenThoughts dataset
VLMs & vision 👀 > Alibaba Qwen is back with Qwen2.5VL, amazing new capabilities ranging from agentic computer use to zero-shot localization 🔥 > NVIDIA released new series of Eagle2 models with 1B and 9B sizes > DeepSeek released Janus-Pro, new any-to-any model (image-text generation from image-text input) with MIT license > BEN2 is a new background removal model with MIT license!
Audio 🗣️ > YuE is a new open-source music generation foundation model, lyrics-to-song generation
Now you can launch a code agent directly from your terminal! ✨ 𝚜𝚖𝚘𝚕𝚊𝚐𝚎𝚗𝚝 "𝚈𝚘𝚞𝚛 𝚝𝚊𝚜𝚔" directly launches a CodeAgent ▶️ This also works with web agents (replace 𝚜𝚖𝚘𝚕𝚊𝚐𝚎𝚗𝚝 with 𝚠𝚎𝚋𝚊𝚐𝚎𝚗𝚝) thanks to @merve !
💾 Another treat from smolagents release 1.7.0: Now agents have a memory mechanism, enabling many possibilities like replaying the last run with 𝚊𝚐𝚎𝚗𝚝.𝚛𝚎𝚙𝚕𝚊𝚢(), thank you @clefourrier !
Datasets on the Hugging Face Hub rely on parquet files. We can interact with these files using DuckDB as a fast in-memory database system. One of DuckDB’s features is vector similarity search which can be used with or without an index.
✅ Hosting our own inference was not enough: now the Hub 4 new inference providers: fal, Replicate, SambaNova Systems, & Together AI.
Check model cards on the Hub: you can now, in 1 click, use inference from various providers (cf video demo)
Their inference can also be used through our Inference API client. There, you can use either your custom provider key, or your HF token, then billing will be handled directly on your HF account, as a way to centralize all expenses.
💸 Also, PRO users get 2$ inference credits per month!
Multimodal 💬 - We have released SmolVLM -- tiniest VLMs that come in 256M and 500M, with it's retrieval models ColSmol for multimodal RAG 💗 - UI-TARS are new models by ByteDance to unlock agentic GUI control 🤯 in 2B, 7B and 72B - Alibaba DAMO lab released VideoLlama3, new video LMs that come in 2B and 7B - MiniMaxAI released Minimax-VL-01, where decoder is based on MiniMax-Text-01 456B MoE model with long context - Dataset: Yale released a new benchmark called MMVU - Dataset: CAIS released Humanity's Last Exam (HLE) a new challenging MM benchmark
LLMs 📖 - DeepSeek-R1 & DeepSeek-R1-Zero: gigantic 660B reasoning models by DeepSeek, and six distilled dense models, on par with o1 with MIT license! 🤯 - Qwen2.5-Math-PRM: new math models by Qwen in 7B and 72B - NVIDIA released AceMath and AceInstruct, new family of models and their datasets (SFT and reward ones too!)
Audio 🗣️ - Llasa is a new speech synthesis model based on Llama that comes in 1B,3B, and 8B - TangoFlux is a new audio generation model trained from scratch and aligned with CRPO
Image/Video/3D Generation ⏯️ - Flex.1-alpha is a new 8B pre-trained diffusion model by ostris similar to Flux - tencent released Hunyuan3D-2, new 3D asset generation from images
smolagents can see 🔥 we just shipped vision support to smolagents 🤗 agentic computers FTW
you can now: 💻 let the agent get images dynamically (e.g. agentic web browser) 📑 pass images at the init of the agent (e.g. chatting with documents, filling forms automatically etc) with few LoC change! 🤯 you can use transformers models locally (like Qwen2VL) OR plug-in your favorite multimodal inference provider (gpt-4o, antrophic & co) 🤠
Today we make the biggest release in smolagents so far: 𝘄𝗲 𝗲𝗻𝗮𝗯𝗹𝗲 𝘃𝗶𝘀𝗶𝗼𝗻 𝗺𝗼𝗱𝗲𝗹𝘀, 𝘄𝗵𝗶𝗰𝗵 𝗮𝗹𝗹𝗼𝘄𝘀 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝘄𝗲𝗯 𝗯𝗿𝗼𝘄𝘀𝗶𝗻𝗴 𝗮𝗴𝗲𝗻𝘁𝘀! 🥳
Our agents can now casually open up a web browser, and navigate on it by scrolling, clicking elements on the webpage, going back, just like a user would.
The demo below shows Claude-3.5-Sonnet browsing GitHub for task: "Find how many commits the author of the current top trending repo did over last year." Hi @mlabonne !
Go try it out, it's the most cracked agentic stuff I've seen in a while 🤯 (well, along with OpenAI's Operator who beat us by one day)
You can now use the Synthetic Data Generator with your own domain-specific seed data to generate a dataset for fine-tuning retrieval or reranking models.
👀 Multimodal - MiniCPM-o 2.6 is a new sota any-to-any model by OpenBMB (vision, speech and text!) - VideoChat-Flash-Qwen2.5-2B is new video multimodal models by OpenGVLab that come in sizes 2B & 7B in resolutions 224 & 448 - ByteDance released larger SA2VA that comes in 26B parameters - Dataset: VRC-Bench is a new diverse benchmark for multimodal LLM reasoning performance
💬 LLMs - MiniMax-Text-01 is a new huge language model (456B passive 45.9B active params) by MiniMaxAI with context length of 4M tokens 🤯 - Dataset: Sky-T1-data-17k is a diverse dataset used to train Sky-T1-32B - kyutai released Helium-1-Preview-2B is a new small multilingual LM - Wayfarer-12B is a new LLM able to write D&D 🧙🏻♂️ - ReaderLM-v2 is a new HTML parsing model by Jina AI - Dria released, Dria-Agent-a-3B, new agentic coding model (Pythonic function calling) based on Qwen2.5 Coder - Unsloth released Phi-4, faster and memory efficient Llama 3.3
🖼️ Vision - MatchAnything is a new foundation model for matching - FitDit is a high-fidelity VTON model based on DiT architecture
🗣️ Audio - OuteTTS-0.3-1B is a new multilingual text-to-speech model with voice cloning and emotion control capabilities
📖 Retrieval - lightblue released a new reranker based on Qwen2.5 LB-reranker-0.5B-v1.0 that can handle 95+ languages - cde-small-v2 is a new sota small retrieval model by @jxm
You can now use the "Synthetic Data Generator" at a much larger scale with your preferred inference engine: Ollama, vLLM, TGI, and serverless inference! 🔥
This work from Chinese startup @MiniMax-AI introduces a novel architecture that achieves state-of-the-art performance while handling context windows up to 4 million tokens - roughly 20x longer than current models. The key was combining lightning attention, mixture of experts (MoE), and a careful hybrid approach.
𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀:
🏗️ MoE with novel hybrid attention: ‣ Mixture of Experts with 456B total parameters (45.9B activated per token) ‣ Combines Lightning attention (linear complexity) for most layers and traditional softmax attention every 8 layers
🏆 Outperforms leading models across benchmarks while offering vastly longer context: ‣ Competitive with GPT-4/Claude-3.5-Sonnet on most tasks ‣ Can efficiently handle 4M token contexts (vs 256K for most other LLMs)
🔬 Technical innovations enable efficient scaling: ‣ Novel expert parallel and tensor parallel strategies cut communication overhead in half ‣ Improved linear attention sequence parallelism, multi-level padding and other optimizations achieve 75% GPU utilization (that's really high, generally utilization is around 50%)
🎯 Thorough training strategy: ‣ Careful data curation and quality control by using a smaller preliminary version of their LLM as a judge!
Overall, not only is the model impressive, but the technical paper is also really interesting! 📝 It has lots of insights including a great comparison showing how a 2B MoE (24B total) far outperforms a 7B model for the same amount of FLOPs.
