The AI community building the future. https://teapawt.org/t.co/TpiXQMQ9rZ
NYC and Paris and 🌏
Joined September 2016
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- Following227
- Followers841K
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Hugging Face retweeted
Sharing my slides on "Getting started with Local AI", presented at a recent hackathon in Qatar!
Learn more about:
* benefits of local AI
* where to find the best models
* quantization
* dense vs. MoE models
* using @Llama_app
Hugging Face retweeted
Hugging Face is now the best place to host and share 3D assets 🧊🤗
Drop in .glb, .ply, .stl, or splat files and get a 3D gallery automatically. There are thousands of 3D models already in public datasets!
350+ datasets to explore: huggingface.co/collections/c…
Docs: huggingface.co/docs/hub/data…
Hugging Face retweeted
Someone just dropped 1.4 million pixel art sprites on Hugging Face 😱👾
free to use, captioned, 16×16
huggingface.co/datasets/Anga…
Hugging Face retweeted
This week we were blessed by:
1. @Nouamanetazi dropped MoE training handbook: huggingface.co/spaces/huggin…
2. @yb2698 started training harness adapters: huggingface.co/blog/harness-…
3. @joelniklaus released the harness leaderboard: huggingface.co/spaces/joelni…
Have a lovely weekend y'all. 🤗
Hugging Face retweeted
I see more and more AI for Science releases on @huggingface, but I was not sure how much they had really increased (and I thought I might be biased) 😇
Then, I found this space which shows how much science releases have increased over the last year on the Hub. So far in 2026, there have been 18k new science models against 6.1k in the same period of 2025! I thought I might be biased, but in fact, I was underestimating it 🚀
🤗 Science on the Hub space: huggingface.co/spaces/evijit…
Thanks @evijit for making this space available and @cgeorgiaw for creating such an amazing AI for Science community!
Hugging Face retweeted
Harness adapters training has now finished!
@liquidai LFM2.5-24B, LFM3 + @thewebAI Twil-8B optimized for pi, hermes and opencode (up to 43% tool efficiency + 15% accuracy gain on our held-out data-analysis test set for LFM3 🤯 / up to 37% tool call efficiency on Twil-8B model).
More than 1900 RL steps trained using @huggingface jobs. 9 total adapters with GGUFs which are ready to use!
Reward hacking attempts blocked for all models with safeagents (and we opensource the attempts).
Let's explore together the outcome of this open experiment 👇
huggingface.co/collections/h…
huggingface.co/collections/h…
huggingface.co/collections/h…
Hugging Face retweeted
Today we release Ripple-65B Preview, our 65B dense LLM we pretrained from scratch.
Ripple sets a new state-of-the-art for U.S. open-weight models in agentic coding tasks, surpassing significantly larger models including Muse Spark Open-816B, Beam-501B, Inkling-975B, and Nemotron 3 Ultra-550B.
Learn more: ricursive.com/ripple
Hugging Face retweeted
The @huggingface crew is here in Doha till 7th for a grand Hackathon in collaboration with QAI.
We're excited to be in this part of the globe and build something cool!
Hugging Face retweeted
By the way you should follow Augustin on X - I’m trying to get him to post more - it’s one of the most cracked roboticist I’ve ever met
We were at the @seeedstudio Shenzhen factory today with @antoinepirrone and @matth_lapeyre and seeing the Microduck production line run for the first time was something special.
We even had our own assembly stations 🙂
Hugging Face retweeted
We spent $2m so you don't have to.
And so you can learn how to train MoEs.
Mixture-of-Experts LLMs are what powers the AIs you use everyday, and few people in the world know how to train them at scale (a handful at each top AI lab).
This handbook distills everything we learned in meticulously crafted chapters and figures.
Disclaimer: it may take days to read.
We hope to see 1000x more people able to train their own frontier models all over the world! 🤗
Congrats @Nouamanetazi @lvwerra @Thom_Wolf
Hugging Face retweeted
Open-source is critical for robotics AI. It means a student, a researcher or a startup can build on years of progress and contribute something the whole community can use rather than concentrating everything in a few companies building a black-box robot.
That’s why we’re building @huggingface into the platform for open-source robotics AI! Amazing to see the progress we've made in the six months and it's accelerating!
- Almost 100,000 public robotics datasets on hf.co/datasets, up 48%
- Over 30,000 public robotics models, up 41%
- Microduck and Reachy mini, our open-source robots became the most popular robot of 2026 (probably ending the year with 50,000+ sold and 20,000+ shipped)
- We’re a founding partner of @RoboColosseum, evaluating robots across 140+ real-world scenes. Every run is published publicly on HF, including failures, so everyone can learn from what actually happens.
And @LeRobotHF got downloaded over 1M times and v0.7.0 just landed with new world action models, interactive robot control and remote GPU inference: huggingface.co/blog/lerobot/…!
Huge thanks to everyone making this happen 🤗🦾
Stream live training runs on Hugging Face!
Harness adapters training is now live!
We are training LoRA adapters on LFM2.5 / LFM3 from @liquidai and the recent Twil-9B model from @thewebAI using RL to make them more efficient and accurate on agentic tasks!
You can follow the training going live here: harness-hub-harness-adapters…
huggingface.co/blog/harness-…
Hugging Face retweeted
18 of the 20 best open models today are Mixture-of-Experts. Dense is basically over.
And yet the know-how to train a good MoE still lives mostly in the heads of a handful of teams.
So we spent 800k GPU hours running the experiments ourselves and wrote everything down. Super happy to finally release "The Ultimate MoE Training Handbook", free and open, organized around the questions training teams hit:
- how many experts, how big, shared or not?
- why is half my step spent in all-to-all, and how do I hide it?
- why is my run 25x slower with no error in the logs?
- softmax, sigmoid or sqrt(softplus) router, and why keep it in fp32?
- aux loss or DeepSeek's loss-free bias?
- upcycle a dense checkpoint or train from scratch?
- can I serve with fewer experts per token than I trained with?
⚠️ Warning: it's long (~70k words, budget a few days, sorry) but with 55 interactive figures, code you can run and numbers from our own runs.
Read it here: huggingface.co/spaces/huggin…
Our open-source ML Intern is now available as a Slack bot you can install in your own workspace!
Evaluate models, build datasets, and fine-tune models on the fly 🚀
YOUR data deserves YOUR own, private model.
Now, let @HuggingFace's ML Intern build it for you.
Connect your data, files, mcps. And call it from Slack.
Join us in this livestream next Tuesday, learn how to shrink models to fit in your laptop and make them go brrr 💨
We will have @mervenoyann @maximelabonne @TadimetiAditya @bartowski1182 and @mgoin_ giving a primer and know-hows on quantization with llama.cpp and vLLM ⚡️
Live on X at the scheduled time, as well as YouTube 🔔 youtube.com/watch?v=il0dgxjp…
Evaluating robot models in the real world is hard, but solvable. That's why we're a founding partner of RoboColosseum 🤗
100+ real robots, 140+ in-the-wild scenes, and every rollout, failures included, open on the Hub.
Introducing RoboColosseum
An open robot eval initiative by @huggingface @BitRobotNetwork @eaic_sg @DJiafei
Access:
- 100+ robots (@UnitreeRobotics G1s, @LeRobotHF SO-101s, YAMs, Franka and more)
- 140+ in-the-wild scenes
- Open rollout data
- 10,000 FREE eval hrs
Thread ↓
Hugging Face retweeted
Llama ❤️ @huggingface Hub 🤗
Get started with thousands of LLMs with a few clicks, running lightning-fast on your computer 🦙
Private, local, open-source 🙌🏻
Hugging Face retweeted
We @huggingface reproduced this paper!!
It's built with ML Intern, an open-source harness to do AI research, train models, and more
Model: huggingface.co/ML-Intern-lab…
Demo: huggingface.co/spaces/ML-Int…
We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math.
It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training.
We call this Weight-Space Programming: frontier LLMs can now natively read/write neural network weights! 🧵
Blog: rikiyat.github.io/blog/weigh…
Readers added context they thought people might want to know
LLM was given weights explicitly as text in prompt and used code/forward passes (per authors' logs) to ablate Qwen looping direction, a known thinking-mode issue per model card. Primarily fixed repetition in that mode, not general 'natively' improving weights/math.
rikiyat.github.io/blog/weight-sp…
github.com/RikiyaT/lm_und…
huggingface.co/Qwen/Qwen3.5-0…
huggingface.co/RikiyaT/Qwen3.…