The AI community building the future. hf.co/careers
NYC and Paris and 🌏
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- Following 227
- Followers 822,944
- Likes 9,501
Hugging Face retweeted
Muse Spark open weights (even in NVFP4) is an amazing computer use agent
I shipped an e2e tutorial on building an env for a browser agent
(with OpenEnv, Helium and @huggingface Sandboxes)
enjoy 🤗 here's a small visualized run ⤵️
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Local AI 202: Get Massive Speed-ups with Speculative Decoding nitter.siddz.com/i/broadcasts/1NGaroZve…
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Hugging Face retweeted
Fun and crazy napkin math: Meta, NVIDIA, Alibaba, DeepSeek, Moonshot, Mistral and ~30 other labs have shared close to $1B worth of training compute on the Hugging Face Hub
Free to download
End of 2023 it was under $40M
Wikipedia was written in volunteer hours. This AI age commons is written in GPU-hours and data
(it's a floor btw: about half comes from official compute numbers rest is educated guess and it still miss the ~900k community fine-tunes)
Space with data to explore: thomwolf-hub-dollars.static.…
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Hugging Face retweeted
Curious to keep up to date with auto-research papers?
Just added a category for it on Papers with Code!
Find it here: paperswithcode.co/tasks/auto…
@NielsRogge an idea for paperswithcode, can you add category of auto-research papers ?
I see them a lot more these days, especially on X and I think that would be awesome 😊
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Hugging Face retweeted
friendly reminder: this is on Monday 🤝
→ learn about speculative decoding to speed-up inference
→ if you're a beginner and not interested, just use llama.app that does this for you automatically 🦙
Learn about how to get multipliers in local LLM speed-ups using speculative decoding in this livestream with @mervenoyann, @MaartenGr and @_EldarKurtic
piped.video/watch?v=H4azmWcq…
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Hugging Face retweeted
Beautiful work from @perplexity_ai @AravSrinivas with weights on @huggingface of course!
huggingface.co/perplexity-ai…
huggingface.co/perplexity-ai…
Our next generation of open weights multimodal decision models comes in two sizes, pplx-decider-v1.2 and pplx-decider-flash-v1.2.
Text and vision scores improve. Inference is up to 5x faster: all questions in a request run in one pass.
Flash is the top 4B on the Decision Index.
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Hugging Face retweeted
Tonight is the night. Today is the day AI Builders unite.
I hear MEGADUCK is coming. Will you?
Open Together, 6pm on 10/16 at The Midway, SF. Free.
luma.com/OpenTogether
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Hugging Face retweeted
it's time to invent math 2.0
(sounds on)
The math community deserves its own AI system.
Introducing Open Math OS: the infrastructure for Math 2.0.
Open models and AI agents. 24/7 cloud compute with 100+ tools. The community controls its data.
Compute is free for approved teams. Apply now:
openmathmodel.org/
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LIVE STREAM: New Open Weight Model nitter.siddz.com/i/broadcasts/1DxleVaPL…
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LIVE STREAM in minutes! Hear from the Reflection team about how they built Beam.
Stream: piped.video/watch?v=dGhUFqeI…
Beam is now available across platforms, with open weights under the Apache 2.0 license - in collaboration with an ecosystem of 40+ partners.
We're releasing:
- Weights, tokenizer, and reference implementation
- BF16, FP8 and NVFP4 weights and a DFlash2 drafter
- The open source harness we built and trained Beam on: Mirror CLI
Weights and model card on Hugging Face: huggingface.co/reflection
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Hugging Face retweeted
Beam is now available across platforms, with open weights under the Apache 2.0 license - in collaboration with an ecosystem of 40+ partners.
We're releasing:
- Weights, tokenizer, and reference implementation
- BF16, FP8 and NVFP4 weights and a DFlash2 drafter
- The open source harness we built and trained Beam on: Mirror CLI
Weights and model card on Hugging Face: huggingface.co/reflection
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Today, we’re releasing Bonsai 2 2B, 4B, and 9B.
Based on Qwen3.5, these new ternary models bring stronger reasoning, visual understanding, and tool calling to compact footprints designed for everyday devices:
• 2B: 0.43GB
• 4B: 0.95GB
• 9B: 2.0GB
For this release, we focused on what makes small models useful as local assistants: following instructions, avoiding repetitive loops, calling tools on mobile devices, and giving useful answers without unnecessary output. The 9B model uses roughly 40% fewer tokens on standard instruction-following and everyday math tasks. Solving the Riemann hypothesis can wait.
These models are successors to our first-generation Bonsai 1.7B, 4B, and 8B, and provide a foundation for bringing practical agentic functionality to local applications on personal computers, phones, and wearables.
The Bonsai 2 family now includes four sizes spanning 2B to 27B parameters. Developers can choose the balance of capability, memory use, and responsiveness that fits their application and hardware.
Available today under Apache 2.0.
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Hugging Face retweeted
Meta is releasing Muse Spark Open as the most-capable US open-weight model.. 🥁 and it's an agent focused MoE that takes all modalities 🔥
Welcoming the model to Hub with day-0 support in transformers and llama.cpp 🤗
huggingface.co/blog/meta-mus…
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Hugging Face retweeted
llama serve -hf ggml-org/Muse-Spark-Open-816B-A42B-GGUF:Q2_K (on two mac studios).
Well done @finkd & @johnternus!
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Hugging Face retweeted
It's time. It's fucking time!
Flux.3 Image Klein is out and we bring you full support for it in Diffusers.
Catch all the details in our post:
huggingface.co/blog/flux-3
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