Yes, DeepSeek R1's release is impressive. But the real story is what happened in just 7 days after:
- Original release: 8 models, 540K downloads. Just the beginning...
- The community turned those open-weight models into +550 NEW models on Hugging Face. Total downloads? 2.5M—nearly 5X the originals.
The reason? DeepSeek models are open-weight, letting anyone build on top of them. Interesting to note that the community focused on quantized versions for better efficiency & accessibility. They want models that use less memory, run faster, and are more energy-efficient.
When you empower builders, innovation explodes. For everyone. 🚀
The most popular community model? @bartowski's DeepSeek-R1-Distill-Qwen-32B-GGUF version — 1M downloads alone.
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llama.cpp is 26.8% faster than ollama. I have upgraded both, and using the same settings, I am running the same DeepSeek R1 Distill 1.5B on the same hardware. It's an Apples to Apples comparison.
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
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)
✨ MiniMax-text-01: - 456B with 45.9B activated per token - Combines Lightning Attention, Softmax Attention, and MoE for optimal performance - Training context up to 1M tokens, inference handles 4M tokens
✨ MiniMax-VL-01: - ViT-MLP-LLM framework ( non-transformer👀) - Handles image inputs from 336×336 to 2016×2016 - 694M image-caption pairs + 512B tokens processed across 4 stages
🖖 Let me introduce the work I've done over the past three months: 𝗟𝗹𝗮𝗺𝗮-𝟯.𝟮-𝗧𝗮𝗶𝘄𝗮𝗻-𝟯𝗕 and 𝗟𝗹𝗮𝗺𝗮-𝟯.𝟮-𝗧𝗮𝗶𝘄𝗮𝗻-𝟯𝗕-𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁, now open-sourced on 🤗 Hugging Face.
𝗹𝗶𝗮𝗻𝗴𝗵𝘀𝘂𝗻/𝗟𝗹𝗮𝗺𝗮-𝟯.𝟮-𝗧𝗮𝗶𝘄𝗮𝗻-𝟯𝗕: This model is built on top of 𝗺𝗲𝘁𝗮-𝗹𝗹𝗮𝗺𝗮/𝗟𝗹𝗮𝗺𝗮-𝟯.𝟮-𝟯𝗕 with continual pretraining. The training dataset consists of a mixture of Traditional Chinese and multilingual texts in specific proportions, including 20B tokens of Traditional Chinese text.
𝗹𝗶𝗮𝗻𝗴𝗵𝘀𝘂𝗻/𝗟𝗹𝗮𝗺𝗮-𝟯.𝟮-𝗧𝗮𝗶𝘄𝗮𝗻-𝟯𝗕-𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁: This is a fine-tuned conversational model based on the foundation model.
This Llama-3.2-Taiwan open-source project is currently a one-person effort (yes, I did everything from text preparation — so exhausting!). If you're interested, feel free to join the Discord server for discussions.
🅱🅴🅽🅲🅷🅼🅰🆁🅺🅸🅽🅶
The evaluation was conducted using ikala/tmmluplus, though the README page does not yet reflect the latest results. The performance is close to the previous versions, indicating that further improvements might require adding more specialized knowledge in the datasets.
🅰 🅲🅰🅻🅻 🅵🅾🆁 🆂🆄🅿🅿🅾🆁🆃
If anyone is willing to provide compute resources, it would be greatly appreciated to help this project continue and grow. 💪
InternLM3-8B-instruct🔥 Trained on just 4T tokens, it outperforms Llama3.1-8B and Qwen2.5-7B in reasoning tasks, at 75% lower cost! internlm/internlm3-67875827c377690c01a9131d
Microsoft's rStar-Math paper claims that 🤏 ~7B models can match the math skills of o1 using clever train- and test-time techniques. You can now download their prompt templates from Hugging Face ! 📏 The paper introduces rStar-Math, which claims to rival OpenAI o1's math reasoning capabilities by integrating Monte Carlo Tree Search (MCTS) with step-by-step verified reasoning trajectories. 🤖 A Process Preference Model (PPM) enables fine-grained evaluation of intermediate steps, improving training data quality. 🧪 The system underwent four rounds of self-evolution, progressively refining both the policy and reward models to tackle Olympiad-level math problems—without GPT-4-based data distillation. 💾 While we wait for the release of code and datasets, you can already download the prompts they used from the HF Hub! Details and links here 👇 Prompt-templates docs: https://moritzlaurer.github.io/prompt_templates/ Templates on the hub: MoritzLaurer/rstar-math-prompts Prompt-templates collection: MoritzLaurer/prompt-templates-6776aa0b0b8a923957920bb4 Paper: https://arxiv.org/pdf/2501.04519
@meg, one of the best researchers in AI ethics, makes a critical point about autonomy: fully autonomous systems carry unknowable risks because they operate on computer logic rather than human logic.
The solution? Build systems that support & assist rather than override human decisions.
I highly recommend reading the blog post written by Meg, @evijit@sasha and @giadap. They define different levels of agent autonomy & provide a values-based analysis of risks, benefits, and uses of AI agents to help you make better decisions.
We’re launching a FREE and CERTIFIED course on Agents!
We're thrilled to announce the launch of the Hugging Face Agents course on Learn! This interactive, certified course will guide you through building and deploying your own AI agents.
Here's what you'll learn:
- Understanding Agents: We'll break down the fundamentals of AI agents, showing you how they use LLMs to perceive their environment (observations), reason about it (thoughts), and take actions. Think of a smart assistant that can book appointments, answer emails, or even write code based on your instructions. - Building with Frameworks: You'll dive into popular agent frameworks like LangChain, LlamaIndex and smolagents. These tools provide the building blocks for creating complex agent behaviors. - Real-World Applications: See how agents are used in practice, from automating SQL queries to generating code and summarizing complex documents. - Certification: Earn a certification by completing the course modules, implementing a use case, and passing a benchmark assessment. This proves your skills in building and deploying AI agents. Audience
This course is designed for anyone interested in the future of AI. Whether you're a developer, data scientist, or simply curious about AI, this course will equip you with the knowledge and skills to build your own intelligent agents.
Enroll today and start building the next generation of AI agent applications!
🏎️ Today I'm introducing a method to train static embedding models that run 100x to 400x faster on CPU than common embedding models, while retaining 85%+ of the quality! Including 2 fully open models: training scripts, datasets, metrics.
We apply our recipe to train 2 Static Embedding models that we release today! We release: 2️⃣ an English Retrieval model and a general-purpose Multilingual similarity model (e.g. classification, clustering, etc.), both Apache 2.0 🧠 my modern training strategy: ideation -> dataset choice -> implementation -> evaluation 📜 my training scripts, using the Sentence Transformers library 📊 my Weights & Biases reports with losses & metrics 📕 my list of 30 training and 13 evaluation datasets
The 2 Static Embedding models have the following properties: 🏎️ Extremely fast, e.g. 107500 sentences per second on a consumer CPU, compared to 270 for 'all-mpnet-base-v2' and 56 for 'gte-large-en-v1.5' 0️⃣ Zero active parameters: No Transformer blocks, no attention, not even a matrix multiplication. Super speed! 📏 No maximum sequence length! Embed texts at any length (note: longer texts may embed worse) 📐 Linear instead of exponential complexity: 2x longer text takes 2x longer, instead of 2.5x or more. 🪆 Matryoshka support: allow you to truncate embeddings with minimal performance loss (e.g. 4x smaller with a 0.56% perf. decrease for English Similarity tasks)
The blogpost contains a lengthy list of possible advancements; I'm very confident that our 2 models are only the tip of the iceberg, and we may be able to get even better performance.
Major update on the Talking to Chatbots dataset! Expanded the 'wrapped' dataset (one row per chat) to 2.86k records, and the 'unwrapped' version (one row per conversation turn) to 11k records. The main source is my ChatGPT archive with nearly 2 years of chats. It is still a work in progress as I incorporate chats from other sources and qualitative metrics (SCBN) for responses.
