Hugging Face (Twitter)
RT @abhinadduri: We updated the State Embedding 600M checkpoint on the @ArcInstitute Hugging Face
This model was trained with 4x FLOPs compared to the preprint model. It achieves significantly lower val/loss and does better on internal evals - would recommend using this over the 4 epoch one for single-cell embeddings!
Preprint: https://www.biorxiv.org/content/10.1101/2025.06.26.661135v1
Hugging Face: https://huggingface.co/arcinstitute/SE-600M
RT @abhinadduri: We updated the State Embedding 600M checkpoint on the @ArcInstitute Hugging Face
This model was trained with 4x FLOPs compared to the preprint model. It achieves significantly lower val/loss and does better on internal evals - would recommend using this over the 4 epoch one for single-cell embeddings!
Preprint: https://www.biorxiv.org/content/10.1101/2025.06.26.661135v1
Hugging Face: https://huggingface.co/arcinstitute/SE-600M
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Hugging Face (Twitter)
RT @saurabh_ai_news: The GitHub for AI just dropped their first robot. 🤖
Hugging Face (@huggingface) & @pollenrobotics are launching Reachy Mini.
An affordable, hackable, open-source robot for everyone, powered by the community.
This is huge.
RT @saurabh_ai_news: The GitHub for AI just dropped their first robot. 🤖
Hugging Face (@huggingface) & @pollenrobotics are launching Reachy Mini.
An affordable, hackable, open-source robot for everyone, powered by the community.
This is huge.
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Hugging Face (Twitter)
RT @dylan_ebert_: Which Generative 3D produces the best topology?
⚔️ 3D Arena now has Topology-only voting/rankings
RT @dylan_ebert_: Which Generative 3D produces the best topology?
⚔️ 3D Arena now has Topology-only voting/rankings
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Hugging Face (Twitter)
RT @ClementDelangue: Crossed quarter of a million $ of orders https://twitter.com/ClementDelangue/status/1942960661371216195#m
RT @ClementDelangue: Crossed quarter of a million $ of orders https://twitter.com/ClementDelangue/status/1942960661371216195#m
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Hugging Face (Twitter)
RT @BrianRoemmele: New Open Source Robot!
Meet Reachy.
The Reachy Mini from Hugging Face, and it’s an impressive open-source robot for AI and robotics enthusiasts.
Priced at $299, this 11-inch Python-programmable kit, with JavaScript and Scratch support coming soon, is perfect for developers, educators, and hobbyists. Its wide-angle camera, microphones, and 6DOF head movement enable seamless human-robot interaction and AI experimentation.
The Lite version is $299 or the Wireless at $449 with Raspberry Pi 5, and connect with a dynamic community to code and innovate. With fully open-source hardware, software, and Hugging Face AI model integration, this will be the ultimate testing ground.
Details: hf.co/blog/reachy-mini
Community: https://discord.com/channels/519098054377340948/1377671369893875783
Video: https://youtube.com/watch?v=JvdBJZ-qR18
A must-try for anyone exploring the future of robotics!
RT @BrianRoemmele: New Open Source Robot!
Meet Reachy.
The Reachy Mini from Hugging Face, and it’s an impressive open-source robot for AI and robotics enthusiasts.
Priced at $299, this 11-inch Python-programmable kit, with JavaScript and Scratch support coming soon, is perfect for developers, educators, and hobbyists. Its wide-angle camera, microphones, and 6DOF head movement enable seamless human-robot interaction and AI experimentation.
The Lite version is $299 or the Wireless at $449 with Raspberry Pi 5, and connect with a dynamic community to code and innovate. With fully open-source hardware, software, and Hugging Face AI model integration, this will be the ultimate testing ground.
Details: hf.co/blog/reachy-mini
Community: https://discord.com/channels/519098054377340948/1377671369893875783
Video: https://youtube.com/watch?v=JvdBJZ-qR18
A must-try for anyone exploring the future of robotics!
Hugging Face (Twitter)
RT @casper_hansen_: Step 2 of many: Last week, I released a biomedical dataset of 521k samples.
This week, I released full-text embeddings (32k) with 2048 dimension from Qwen3 4B embedding model.
RT @casper_hansen_: Step 2 of many: Last week, I released a biomedical dataset of 521k samples.
This week, I released full-text embeddings (32k) with 2048 dimension from Qwen3 4B embedding model.
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Hugging Face (Twitter)
RT @amir_mahla: Deploy full-stack desktop agents in seconds with ScreenEnv! ✨
> Fully Sandboxed Desktop, isolated & reproducible.
> AI-native with MCP support
> Agents can see, click, type, browse, manage apps & files and more
> Runs in Docker, no VMs, no boilerplate
👇 Link in comments
🙏 Huge thank you to my teammate @AymericRoucher for their ideas, collaboration, and incredible energy during this release.
RT @amir_mahla: Deploy full-stack desktop agents in seconds with ScreenEnv! ✨
> Fully Sandboxed Desktop, isolated & reproducible.
> AI-native with MCP support
> Agents can see, click, type, browse, manage apps & files and more
> Runs in Docker, no VMs, no boilerplate
👇 Link in comments
🙏 Huge thank you to my teammate @AymericRoucher for their ideas, collaboration, and incredible energy during this release.
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Hugging Face (Twitter)
RT @UFBots: Reachy Mini from @LeRobotHF @huggingface training up for UFB.
Wait till this little bugger get his arms/legs. He might just be the Ali of UFB 🤖🥊👑
RT @UFBots: Reachy Mini from @LeRobotHF @huggingface training up for UFB.
Wait till this little bugger get his arms/legs. He might just be the Ali of UFB 🤖🥊👑
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Hugging Face (Twitter)
RT @_fracapuano: Today, we're releasing an open-source async inference stack for all models currently hosted on @huggingface, powering the world's cutest robots, built with love by the team at @LeRobotHF
Details in 🧵
RT @_fracapuano: Today, we're releasing an open-source async inference stack for all models currently hosted on @huggingface, powering the world's cutest robots, built with love by the team at @LeRobotHF
Details in 🧵
Hugging Face (Twitter)
RT @RisingSayak: Users of `torch.compile`. Some small performance tips:
1. Default to `fullgraph=True` to catch graph breaks as early as possible.
2. Check for recompilation triggers. Put your code under `torch._dynamo.config.patch(error_on_recompile=True)` context.
3. Use regional compilation almost always to cut down cold-start timing significantly.
Graph-breaks and frequent recompilations can easily come in the way of performance. Eliminate them as much as possible.
In Diffusers, we have a dedicated test suite for checking these things. Reference:
https://github.com/huggingface/diffusers/blob/941b7fc0843139e52419a65b7fa850169fde0360/tests/models/test_modeling_common.py#L1952
Immense thanks to @anijain2305 for always helping out!
RT @RisingSayak: Users of `torch.compile`. Some small performance tips:
1. Default to `fullgraph=True` to catch graph breaks as early as possible.
2. Check for recompilation triggers. Put your code under `torch._dynamo.config.patch(error_on_recompile=True)` context.
3. Use regional compilation almost always to cut down cold-start timing significantly.
Graph-breaks and frequent recompilations can easily come in the way of performance. Eliminate them as much as possible.
In Diffusers, we have a dedicated test suite for checking these things. Reference:
https://github.com/huggingface/diffusers/blob/941b7fc0843139e52419a65b7fa850169fde0360/tests/models/test_modeling_common.py#L1952
Immense thanks to @anijain2305 for always helping out!
GitHub
diffusers/tests/models/test_modeling_common.py at 941b7fc0843139e52419a65b7fa850169fde0360 · huggingface/diffusers
🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch and FLAX. - huggingface/diffusers
Hugging Face (Twitter)
RT @Xianbao_QIAN: Skywork-R1V 3.0: an open source model that beats close source models on multi-modal reasoning.
Link on @huggingface
https://huggingface.co/Skywork/Skywork-R1V3-38B
RT @Xianbao_QIAN: Skywork-R1V 3.0: an open source model that beats close source models on multi-modal reasoning.
Link on @huggingface
https://huggingface.co/Skywork/Skywork-R1V3-38B
Hugging Face (Twitter)
RT @apples_jimmy: I think I’m more excited for the openai opensource model than Gpt 5
RT @apples_jimmy: I think I’m more excited for the openai opensource model than Gpt 5
Hugging Face (Twitter)
RT @Xianbao_QIAN: Kimi K2 is open sourced on @huggingface
- 1T MoE, 32B active params
- Excellent coding & Tool use & Math
- Not a thinking model
- Both BASE and Instruct is released, friendly for fine-tunes!!!
https://huggingface.co/moonshotai/Kimi-K2-Base https://twitter.com/Xianbao_QIAN/status/1943621126652821617#m
RT @Xianbao_QIAN: Kimi K2 is open sourced on @huggingface
- 1T MoE, 32B active params
- Excellent coding & Tool use & Math
- Not a thinking model
- Both BASE and Instruct is released, friendly for fine-tunes!!!
https://huggingface.co/moonshotai/Kimi-K2-Base https://twitter.com/Xianbao_QIAN/status/1943621126652821617#m
Hugging Face (Twitter)
RT @ClementDelangue: 1T parameters, open-weights, just released on @huggingface!
RT @ClementDelangue: 1T parameters, open-weights, just released on @huggingface!
Hugging Face (Twitter)
RT @reach_vb: Pretty wild that @Kimi_Moonshot dropped a 1T parameter (32B active) MoE trained on 15.5 Trillion tokens - MIT licensed 🔥
Beats all other open weights models across coding, agentic and reasoning benchmarks
Ofcourse live on Hugging Face! 🤗
RT @reach_vb: Pretty wild that @Kimi_Moonshot dropped a 1T parameter (32B active) MoE trained on 15.5 Trillion tokens - MIT licensed 🔥
Beats all other open weights models across coding, agentic and reasoning benchmarks
Ofcourse live on Hugging Face! 🤗
Hugging Face (Twitter)
RT @rohanpaul_ai: 🇨🇳 INCREDIBLE. China just released 1tn parm top open source model for coding and agentic tool work.
Kimi K2 from Moonshot AI
Insane numbers on benchmarks.
On LiveCodeBench the model hits 53.7 Pass@1, beating DeepSeek‑V3 by almost 7 points and clearing Qwen‑235B by more than 16 points
Scores 65.8% on single‑shot SWE‑bench agentic coding and 70.6 on Tau2 retail tool use, numbers that sit at or near the top of the open stack.
- 1 tn total parameters MoE, 32Bn active
- Trained with the Muon optimizer
- Very strong across frontier knowledge, reasoning, and coding tasks
- SOTA on SWE Bench Verified, Tau2 & AceBench among open models
- Pre-trained n 15.5T tokens with zero training instability.
- Agentic Intelligence: Specifically designed for tool use, reasoning, and autonomous problem-solving.
- API endpoints mirror OpenAI and Anthropic schemas, while self‑hosters can load weights through vLLM, SGLang, KTransformers, or...
Перейти на оригинальный пост
RT @rohanpaul_ai: 🇨🇳 INCREDIBLE. China just released 1tn parm top open source model for coding and agentic tool work.
Kimi K2 from Moonshot AI
Insane numbers on benchmarks.
On LiveCodeBench the model hits 53.7 Pass@1, beating DeepSeek‑V3 by almost 7 points and clearing Qwen‑235B by more than 16 points
Scores 65.8% on single‑shot SWE‑bench agentic coding and 70.6 on Tau2 retail tool use, numbers that sit at or near the top of the open stack.
- 1 tn total parameters MoE, 32Bn active
- Trained with the Muon optimizer
- Very strong across frontier knowledge, reasoning, and coding tasks
- SOTA on SWE Bench Verified, Tau2 & AceBench among open models
- Pre-trained n 15.5T tokens with zero training instability.
- Agentic Intelligence: Specifically designed for tool use, reasoning, and autonomous problem-solving.
- API endpoints mirror OpenAI and Anthropic schemas, while self‑hosters can load weights through vLLM, SGLang, KTransformers, or...
Перейти на оригинальный пост
Hugging Face (Twitter)
RT @AIatAMD: 🚀 We’re excited to partner with @HuggingFace to launch a new section of their MCP Course: Local Tiny Agents with AMD NPU and iGPU Acceleration — powered by Lemonade Server 🍋 https://github.com/lemonade-sdk/lemonade
In this hands-on module, you’ll learn how to:
✅ Accelerate end-to-end Tiny Agents applications using AMD’s Neural Processing Unit (NPU) and integrated GPU (iGPU)
✅ Enable local file access and build assistants that handle sensitive data entirely on-device — ensuring maximum privacy and performance
We’re proud to support developers building smarter, faster, and more private AI agents.
🔗 Dive into the course: https://huggingface.co/learn/mcp-course/unit2/lemonade-server
⭐ Star our Lemonade GitHub repo: https://github.com/lemonade-sdk/lemonade
#AMD #HuggingFace #TinyAgents #EdgeAI #NPUs #iGPU #LemonadeServer #MCP #AIAcceleration
RT @AIatAMD: 🚀 We’re excited to partner with @HuggingFace to launch a new section of their MCP Course: Local Tiny Agents with AMD NPU and iGPU Acceleration — powered by Lemonade Server 🍋 https://github.com/lemonade-sdk/lemonade
In this hands-on module, you’ll learn how to:
✅ Accelerate end-to-end Tiny Agents applications using AMD’s Neural Processing Unit (NPU) and integrated GPU (iGPU)
✅ Enable local file access and build assistants that handle sensitive data entirely on-device — ensuring maximum privacy and performance
We’re proud to support developers building smarter, faster, and more private AI agents.
🔗 Dive into the course: https://huggingface.co/learn/mcp-course/unit2/lemonade-server
⭐ Star our Lemonade GitHub repo: https://github.com/lemonade-sdk/lemonade
#AMD #HuggingFace #TinyAgents #EdgeAI #NPUs #iGPU #LemonadeServer #MCP #AIAcceleration