The AI community building the future. https://xitter.cf/t.co/TpiXQMQ9rZ
NYC and Paris and 🌏
Joined September 2016
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Hugging Face retweeted
Introducing Carbon-X: The best open DNA model.
We midtrained a 14B model on DNA and text, and it beats every open language model and matches GPT-6.1 Sol at 1,000x lower cost.
Ask it about a sequence, what a mutation does, or to generate new DNA.
huggingface.co/HuggingFaceBi…
Hugging Face retweeted
NVIDIA CEO Jensen Huang asked 4 tiny robots to sit, stand, nap, and do somersaults.
And they actually did it.
NVIDIA is bringing AI agents from virtual world to real world with these robotic ducks.
Hugging Face retweeted
Jensen Huang having some fun demonstrating MicroDuck robots trained using NVIDIA RTX Spark and Omniverse on stage at GTC Berlin.
Hugging Face retweeted
Frontier labs are advancing the capabilities in AI for biology but preventing the good guys (scientists, academia, non-profits, health agencies,...) to use, study and understand it with widespread but shallow guardrails that the bad guys are bypassing easily (blocked me to proofread this tweet for example 😅).
This will automatically create massive assymetry and risks in the coming years, just like in cybersecurity, especially now that frontier labs are building wet labs (HF cybersecurity scenario for biology when?).
This is why we created Carbon-X, the best open-source DNA model by midtraining Qwen3-14B on 300B tokens of DNA, English and interleaved data. We also used a hybrid tokenizer that tokenizes English with BPE and DNA with a 6-mer tokenizer.
As a result, Carbon-X outperforms all open language models we evaluated on five benchmarks of DNA understanding and generation. This is a 27-point improvement over Qwen3-14B, showing how much a language model can gain from DNA training. It is also API frontier level at 1,800x less cost than GPT 6.1 Sol!
We hope Carbon-X helps researchers explore DNA in new ways. Releasing it openly means anyone can study its capabilities, limitations, and risks. Let's make the AI biology frontier open-source to make it safe and impactful for the whole world (not just a few companies).
Apache 2.0 Model on @huggingface: huggingface.co/HuggingFaceBi…
Hugging Face retweeted
just updated the Decision Index to 0.4 🧭
what's new?
🛡️ curbing benchmaxxing: score now exclusively made from private & in-house benchmarks
🔒 stronger private benchmarks on new domains
🏆 224 models evaluated, new top models in all sizes
come play 👉huggingface.co/spaces/multim…
Hugging Face retweeted
Today we're releasing Carbon-X, a 14B model that brings DNA and natural language together for the first time 🧬
1. Beats every open language model we tested across DNA benchmarks. Within a point of GPT-6.1 Sol at >1,000× lower inference cost.
2. Mid-trained on 300B tokens of interleaved DNA and text, followed by SFT (without distillation!) and RL.
3. A new hybrid tokenizer encodes DNA using ~3× fewer tokens than BPE.
4. Gives other models a DNA specialist subagent: Kimi K3 gains 11.1 points when it consults Carbon-X.
5. Runs locally on a laptop with llama.cpp. Apache 2.0. Your DNA stays on your hardware.
Model: huggingface.co/HuggingFaceBi…
Blog: huggingface.co/spaces/Huggin…
Introducing Carbon-X: The best open DNA model.
We midtrained a 14B model on DNA and text, and it beats every open language model and matches GPT-6.1 Sol at 1,000x lower cost.
Ask it about a sequence, what a mutation does, or to generate new DNA.
huggingface.co/HuggingFaceBi…
Hugging Face retweeted
We just merged the first protein sequence design model RFD4-Proteina in Diffusers 🔥
I hope this pushes the state of open BioML further.
Kudos to @NVIDIAAI and Baker Lab for shipping this!
Check out the docs to know more:
huggingface.co/docs/diffuser…
Hugging Face retweeted
Introducing SimReady Central Library. 👋
This library brings 2,000+ simulation-ready 3D assets to @huggingface, open source for developers building robotics simulations and AI factory digital twins.
Now, developers can spend less time preparing assets while equipment manufacturers can help customers evaluate equipment in simulation before real-world deployment.
Get started 👉 nvda.ws/3Wl6LCi
Come and build models, datasets, and spaces on the hub, to win $20k in cash and credits!
We teamed up with @huggingface to give away $20,000 to anyone who's building with Beam 🤗
Use Beam for any project - make a demo or a dataset, benchmark the model, create an RL environment - and comment your link in the model's repo page by Monday, November 2.
We choose the winners. Winners choose cash or credits. Claim $25 in Hugging Face credits to build with Beam and enter the competition 👇
huggingface.co/reflection/Be…
Hugging Face retweeted
Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine.
• Best-in-class fast local coding
• Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark
• Built from scratch in under 4 months
Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.
Hugging Face retweeted
When @jensenhuang asks for a somersault, you deliver. 🦆
See how AI agents on NVIDIA RTX Spark help bring @huggingface's Microduck from simulation to the real world.
Hugging Face retweeted
Today we’re releasing two decision models, elephant-1 (9B) and mammoth-1 (27B), trained by the @baselabs team, available for free for one week on our Model APIs.
mammoth-1 is #1 on the Jev Decision Index and elephant-1 is tied for #1 on the Artificial Analysis Decision Index.
It’s a new model, but more importantly, this is an open-source model you can access, deploy, and post-train on Baseten (or wherever you do your best work).
The free week ends 10/27/26 at 11:59 PM PT. Try it and let us know what you think.
Try mammoth-1 here: baseten.co/library/mammoth-1…
Try elephant-1 here:
baseten.co/library/elephant-…
Hugging Face retweeted
It feels pretty fitting for @JensenHuang & @nvidia to end their European tour with Microducks on stage.
Microduck was designed and engineered in Europe (more precisely Bordeaux, France) by the @pollenrobotics and @huggingface teams. With American capital and ambition + Chinese manufacturing genius, it became the most popular robot of 2026 with over 15,000 sold all over the world!
I believe Europe has more opportunity to shine in AI than in any previous tech wave. We intend to prove it with @huggingface @nvidia and the whole open-source AI community in the coming years!
Let's go European AI 🇪🇺🇪🇺🇪🇺
Hugging Face retweeted
Here is the Microduck up close
You can program it work DGX spark
This baby duck represents the future of physical Ai
it relies on reinforcement learning to transition AI-trained movements from digital simulations into the real world
Small duck now large robots later 🐥🤖💚
Hugging Face retweeted
So many SO-ARMs! 💪
Here is the robotics workshop organized at @NVIDIAGTC Berlin that uses SO-ARM101 and LeRobot to control real and simulated robotic arms. The light box makes it easier to get policies working in any location and light conditions.
Hugging Face retweeted
Microducks on stage for Jensen Huang’s famous keynote!
We couldn’t be prouder of our little ones. 🥹
Hugging Face retweeted
I've made some big improvements to the PDF reader on Papers with Code!
> The AI assistant now cites its sources, full RAG-style
> This is powered by Chandra-OCR 2 by @datalabto which is openly available on @huggingface
> This model returns bounding boxes besides markdown
> Allows you to click on citations and highlight sections
Try it out on any PDF, like paperswithcode.co/pdf/2610.1…
Hugging Face retweeted
🧵 I've just released Sentence Transformers v6.2.0!
Featuring model merging with @arcee_ai mergekit, load models faster, & evaluators share work across GPUs automatically.
Plus lower-memory losses, multi-vector improvements & plenty of fixes.
Thread with highlights 👇
