deepseek-ai/DeepSeek-OCR is out! π₯ my take β€΅οΈ > pretty insane it can parse and re-render charts in HTML > it uses CLIP and SAM features concatenated, so better grounding > very efficient per vision tokens/performance ratio > covers 100 languages
π New blog: Maintain the unmaintainable β 1M+ Python LOC, 400+ models
How do you stop a million-line library built by thousands of contributors from collapsing under its own weight? At π€ Transformers, we do it with explicit software-engineering tenets, principles that make the codebase hackable at scale.
π Inside the post: β One Model, One File: readability first β you can still open a modeling file and see the full logic, top to bottom. β Modular Transformers: visible inheritance that cuts maintenance cost by ~15Γ while keeping models readable. β Config-Driven Performance: FlashAttention, tensor parallelism, and attention scheduling are config-level features, not rewrites.
Written with @lysandre,@pcuenq and @yonigozlan, this is a deep dive into how Transformers stays fast, open, and maintainable.
IBM just released small swiss army knife for the document models: granite-docling-258M on Hugging Face π₯
> not only a document converter but also can do document question answering, understand multiple languages π€― > best part: released with Apache 2.0 license π use it with your commercial projects! > it supports transformers, vLLM and MLX from the get-go! π€ > built on SigLIP2 & granite-165M
Smol course has a distinctive approach to teaching post-training, so I'm posting about how itβs different to other post-training courses, including the llm course thatβs already available.
In short, the smol course is just more direct that any of the other course, and intended for semi-pro post trainers.
- Itβs a minimal set of instructions on the core parts. - Itβs intended to bootstrap real projects you're working on. - The material handsover to existing documentation for details - Likewise, it handsover to the LLM course for basics. - Assessment is based on a leaderboard, without reading all the material.
The course builds on smol course v1 which was the fastest way to learn to train your custom AI models. It now has:
- A leaderboard for students to submit models to - Certification based on exams and leaderboards - Prizes based on Leaderboards - Up to date content on TRL and SmolLM3 - Deep integration with the Hubβs compute for model training and evaluation
We will release chapters every few weeks, so you can follow the org to stay updated.
The open source AI community is just made of people who are passionate and care about their work. So we thought it would be cool to share our favourite icons of the community with a fun award.
Winners get free Hugging Face Pro Subscriptions, Merchandise, or compute credits for the hub.
This is a new initiative to recognise and celebrate the incredible work being done by community members. It's all about inspiring more collaboration and innovation in the world of machine learning and AI.
They're highlighting contributors in four key areas: - model creators: building and sharing innovative and state-of-the-art models. - educators: sharing knowledge through posts, articles, demos, and events. - tool builders: creating the libraries, frameworks, and applications that we all use. - community champions: supporting and mentoring others in forums.
Know someone who deserves recognition? Nominate them by opening a post in the Hugging Face community forum.
first vision language model built off openai/gpt-oss-20b just dropped! π₯
InternVL3.5 comes with 32 models π€― pre-trained, fine-tuned, aligned in various sizes OpenGVLab/internvl35-68ac87bd52ebe953485927fb comes with gpt-oss or Qwen3 for LLM part ‡οΈ