Hugging Face

The AI community building the future.

[Hugging Face @huggingface]

Great way to break into AI/ML research!

[Gradio @Gradio]

Something’s cooking in the lab 🧪🤗

NeurIPS 2026 × ML Intern

Reproduction hackathon. Coming soon. We are very excited!!

[NVIDIA AI @NVIDIAAI]

Meet NVIDIA Nemotron 3.5 Transcribe 🎙️

An open-weight, promptable 30B MoE model with ~4B active parameters that brings transcription in 40 languages, speaker attribution, translation, contextual prompting and audio understanding into one checkpoint.

Watch it keep track of who said what when everyone’s talking at once.

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[Liquid AI @liquidai]

Introducing 𝙻𝙵𝙼𝟹: our new generation of Liquid Foundation Models. With a new tri-hybrid architecture and new multimodal encoders, the LFM3 family is the ideal model suite for specialized intelligence.

> 6 model instances, across text, audio, vision, and omni modalities, generative and decision models.
> 𝙻𝙵𝙼𝟹-𝟷.𝟽𝙱: text, 256k context window.
> 𝙻𝙵𝙼𝟹-𝚅𝙻-𝟸𝙱: vision-language model with video-input support for spatial understanding
> 𝙻𝙵𝙼𝟹-𝙰𝚞𝚍𝚒𝚘-𝟸𝙱: tool calls, voice chat, reasoning in 18 languages.
> 𝙻𝙵𝙼𝟹-𝙾𝚖𝚗𝚒-𝟸𝙱: an omni model combines vision, audio, and language.
> 𝚍𝟷-𝟸𝙱 and 𝚍𝟷-𝙾𝚖𝚗𝚒-𝟸𝙱: decision models with typed outputs and calibrated probabilities in a single pass. Omni adds audio input.
> fine-tune on 1 GPU.
> a new tri-hybrid architecture
> architecture optimized for 14 processor classes across GPUs, CPUs, and NPUs.
> 𝙻𝙵𝙼𝟹-𝚅𝙻-𝟸𝙱 is a beast. It performs competitively with Gemma 4 12B (6x larger), and Gemini 3.5 Flash Lite, and GPT-5.6 Luna, with 1.9B parameters.
> 𝙻𝙵𝙼𝟹-𝟷.𝟽𝙱 is #1 on the Pareto frontier of device intelligence 𝚙𝚒𝚙𝚎𝚝𝚝𝚎.𝚕𝚒𝚚𝚞𝚒𝚍.𝚊𝚒

Every modality for specialized intelligence. Pick the one that fits your use case, customize it, and run it on-device.

1/ 🧵

[Sergio Paniego @SergioPaniego]

last week we closed the Training Agents series IRL at the GitHub offices in SF

4 talks, a panel and audience Q&A. almost 2h on post-training agents with open models

> Learnings from enterprise RL (@t2govind Microsoft)
> Environments and benchmarks in industry (@VBarres86633 Mercor)
> How to train your agents with Miles (@nblintao RadixArk)
> Why we built OpenEnv Arena (@xdotli BenchFlow)

full recording + slides

https://t.co/cIkm20wEdV

[Hugging Face @huggingface]

@ben_burtenshaw @reflection_ai First 400 subs on us!

[Ben Burtenshaw @ben_burtenshaw]

You can win $20K in cash prizes by building a demo, training a model, or creating a dataset with Beam by @reflection_ai!

Prizes are split across 3 winners that we'll choose after the challenge ends on Monday, November 2nd.

Plus, we're giving $25 hub credits to the first 400 to take part, just find the link in the page below.

Rules: Build something cool with beam and drop it on the discussion! Anything that might be helpful to the world or a cool demo is allowed:

- You could make a demo that uses the Beam model on Spaces
- You could quantize it for your favorite hardware
- You could generate a synthetic dataset for your favorite problem
- You could build an RL environment with or for Beam
- You could benchmark Beam and share the results.
- You could surprise us!

If you build something with Beam, then share it on the Hub to enter the challenge. We’ll collect all the links, select the best, and reach out to winners.

https://t.co/u1D4pBhbRp

[Emanuil Rusev @erusev]

I've been building Llama with the llama.cpp team — a tiny menu bar app that runs open models on your Mac.

Here it is in 35 seconds 🦙

[Pollen Robotics @pollenrobotics]

New on MicroDuck Arena: Tug of War 🦆🪢🦆

Two robot ducks, one rope, a pit in the middle. Drag your rival in and you win.

Train yours in simulation, publish it, and it's in the first cup, live Friday, Nov 6 at 9 AM PT.

https://t.co/ZjmNwv89cV

[Ben Burtenshaw @ben_burtenshaw]

We closed out the Training Agents series in person in San Francisco with four talks and a panel on post-training agents with open models in industry.

Enterprise RL (@t2govind Microsoft), environment design (Victor Barres Mercor), scaling agent RL with Miles (@nblintao RadixArk), and OpenEnv Arena (@xdotli BenchFlow).

Full recording, slides and links: https://t.co/fLAuoy4Vmd

[Niels Rogge @NielsRogge]

Papers with Code is now open-source!

Apache 2.0-license.

Let's collaboratively make the best paper interface!

https://t.co/hwh6CEApyK

[Jensen Huang @JensenHuang]

I was the opening act for a family of ducks.

Thank you, Berlin. What a GTC.

Europe is building its AI ecosystem and economy, from AI factories to agents to robots.

DSX POD: Vera Rubin modular factories built, cabled and tested in the factory, then delivered ready to produce intelligence.

Agents: models, harnesses, sandboxes, knowledge and tools. NVIDIA Agent Toolkit brings the pieces together so companies control what their agents know, access and do.  NVIDIA OpenShell enhances safety.

Physical AI: new customers across AI-RAN, robotaxis and robotics.

Personal AI: after four years building with Microsoft, RTX Spark brings personal agents to the Windows PC.

Thank you to President @EmmanuelMacron, Prime Minister @andyburnham and Chancellor @bundeskanzler for hosting me. Great to celebrate Sferical One with Sweden’s technology leaders. And thank you to President @TokayevKZ for the warm welcome.

And to our partners and NVIDIA teams: thank you for making it happen.

The keynote is here: https://t.co/s9GISZ1Swi

[Emm | scenario.com @emmanuel_2m]

We just open-sourced 1,300+ 3D models.

All 39 GB are free to download on Hugging Face.

https://t.co/LVAmH3NNTM

> made with Hitem3D, Meshy, Rodin, Tripo, Claude Opus 5.5 3D and GPT-6 Astra 3D, which all run on Scenario

> includes the input images, prompts and settings that made them

> GLBs up to 8K textures, native Blender files from Opus and Astra, CC BY 4.0

[Remi Fabre @RemiFabreRobot]

Sorry for the wait, the episode is here!

Live action with real robots instead of animation (episode 4): "Anger"

Enjoy!

As always, the software of Microduck and Reachy Mini is open source:
https://t.co/RW1p4V2X6X
https://t.co/hA91qMLZ4d

[Lexsi Labs @Lexsi_labs]

A big announcement from Lexsi Labs:

Lexsi Labs (@Lexsi_labs) , Cohere Labs (@cohere ) and Hugging Face(@huggingface) are partnering on the Lexsi AI Safety and Alignment Hackathon, one of the biggest hackathon on post-training and AI Safety & Alignment.

Nov 1 to 21, 2026. Online, free, open to builders worldwide.

Teams build real post-training pipelines:
- Models: open-weights Tiny Aya and Aya Expanse from Cohere Labs
- Tooling: six open-source Lexsi Labs toolkits (AlignTune, CuratorKIT, AuditKIT, SafeTune, CircuitKIT, AgentTune)
- Artifacts: models, adapters, logs and datasets hosted on Hugging Face

Three tracks: fine-tuning and data curation, AI red teaming and safety, agentic fine-tuning.

Each submission chains at least two toolkits and reports before-and-after metrics.

$30,000+ in prizes and credits, GPU credits for up to 800 teams, Demo Day for the top 20, and interviews at Lexsi Labs for standout builders.

Kickoff: Bengaluru, Oct 31, with an online option.

Registration starts today at: https://t.co/mSMWw8poAx

Thank you to @huggingface and @cohere for building this with us.

[microagi @microagi]

As our customers scale to training robot models using millions of hours of our recordings, we needed to build a new kind of data infrastructure.

Terabytes of data delivered in minutes, and the capacity to deliver over a Petabyte per day to each of our customers, to multiple customers at the same time. That’s more than 10 Gigabytes a second, delivered into any cloud, wherever our customers need it.

Using our customer portal, customers select precisely the data they want and it starts arriving in minutes at their destination, including AWS, GCP, Cloudflare, Hugging Face and any cloud provider who can handle the ingest speed.

We’ve written about how we worked with @huggingface and @Cloudflare to build this secure, scalable infrastructure. Link in comments.

[Linoy Tsaban @linoy_tsaban]

Introducing Qwen Image 2.1 Realtime ⚡️
I'm releasing V1 of my *2-step* distilled LoRA, and it runs in *realtime* on ZeroGPU ⚡️⚡️
(live editing part of the video was not sped up!)

> DMD2, rank 32
> 2 steps, with quality competitive with Turbo (8-step) and Base (40-step)
> realtime on ZeroGPU (half of an RTX Pro 6000)

LoRA: https://t.co/dZQC6MOxbx
Space: https://t.co/6fHURqMoZ3

[NVIDIA AI @NVIDIAAI]

Available now: NVIDIA Nemotron 3.5 Bolt E4B 4GB ⚡️

Our newest small open model is built for local assistants, interactive characters and robots that can carry on a conversation and use tools to get things done.

Watch Reachy Mini chat and move, with Bolt handling conversation and NVIDIA Cosmos handling vision. Both models run locally on Jetson Orin Nano 8GB.

Learn more: https://t.co/B8ODaWMn0O

[Hugging Face @huggingface]

🎯

[alphaXiv @askalphaxiv]

OpenResearch now has @TrackioApp built in 📈

Your agent logs its experiments to Trackio, charts open right next to each run, and it reads the curves back to decide what to try next. Open source and local-first.

Running on HF Jobs, Modal, k8s or Slurm? Save a @huggingface Space and every run lands there, private Spaces included.

Check it out: https://t.co/IDi6bmr04h

[Scenario @Scenario_gg]

We just open-sourced 1,300+ 3D models.

That's 39 GB of 3D files, free to download on Hugging Face.

https://t.co/2ilZjaYXDx

> Hitem3D, Meshy, Rodin, Tripo, Claude Opus 5.5 3D and GPT-6 Astra 3D, all run on Scenario

> every example ships its input image, prompt and exact settings

> GLB up to 8K textures, native Blender files from Opus and Astra, CC BY 4.0

[Sergio Paniego @SergioPaniego]

Live From SF! We’re teaching these folk how to train their own open weight agents. Sorry, but we won’t live stream. Full recording coming tomorrow!