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Hugging Face (Twitter)
RT @andimarafioti: 30x real-time speech-to-text in your browser.
No installs. No servers.
Just open the website.
RT @andimarafioti: 30x real-time speech-to-text in your browser.
No installs. No servers.
Just open the website.
Hugging Face (Twitter)
RT @vanstriendaniel: Data Designer from @nvidia now has full @huggingface Hub integration:
- load any dataset as a seed
- generate synthetic data,
- push results straight to the Hub
RT @vanstriendaniel: Data Designer from @nvidia now has full @huggingface Hub integration:
- load any dataset as a seed
- generate synthetic data,
- push results straight to the Hub
Hugging Face (Twitter)
RT @HuggingPapers: StepFun's Step 3.5 Flash
A sparse MoE model with 196B parameters, 11B active per token.
Achieves frontier-level reasoning comparable to GPT-5.2 xHigh and Gemini 3.0 Pro at 1/6th the decoding cost.
Ranks #1 on MathArena with 97.3% on AIME 2025.
RT @HuggingPapers: StepFun's Step 3.5 Flash
A sparse MoE model with 196B parameters, 11B active per token.
Achieves frontier-level reasoning comparable to GPT-5.2 xHigh and Gemini 3.0 Pro at 1/6th the decoding cost.
Ranks #1 on MathArena with 97.3% on AIME 2025.
Hugging Face (Twitter)
RT @turingcom: OpenEnv treats environments as first class infrastructure.
Through our collaboration with @AIatMeta and @huggingface, Turing is helping labs run tool-using agents against RL Environments that share:
-A standard step and reset API
-WebSocket sessions with per-client state
-MCP-style tool discovery and calling
-Observability hooks for rewards, errors, and drift
The payoff is simple. You can reuse the same evaluation pattern across domains and see where agents actually fail in long, tool-heavy workflows. Learn more below.
RT @turingcom: OpenEnv treats environments as first class infrastructure.
Through our collaboration with @AIatMeta and @huggingface, Turing is helping labs run tool-using agents against RL Environments that share:
-A standard step and reset API
-WebSocket sessions with per-client state
-MCP-style tool discovery and calling
-Observability hooks for rewards, errors, and drift
The payoff is simple. You can reuse the same evaluation pattern across domains and see where agents actually fail in long, tool-heavy workflows. Learn more below.
Hugging Face (Twitter)
RT @RisingSayak: An ambitious project today π₯
We got an agent to write custom kernels that actually work for a given model, hardware instruction set, and other relevant model-dependent constraints.
Benchmarks are our rewards here π€ͺ
We got these kernels to work with Diffusers and `torch.compile` and they delivered ACTUAL SPEEDUP without messing up the quality π
Despite the competitive landscape, we don't like to keep things private. Read all of it in the blog post below:
https://huggingface.co/blog/custom-cuda-kernels-agent-skills
RT @RisingSayak: An ambitious project today π₯
We got an agent to write custom kernels that actually work for a given model, hardware instruction set, and other relevant model-dependent constraints.
Benchmarks are our rewards here π€ͺ
We got these kernels to work with Diffusers and `torch.compile` and they delivered ACTUAL SPEEDUP without messing up the quality π
Despite the competitive landscape, we don't like to keep things private. Read all of it in the blog post below:
https://huggingface.co/blog/custom-cuda-kernels-agent-skills
Hugging Face (Twitter)
RT @_lewtun: We trained a tiny 4B model to reason for millions of tokens through IMO-level problems.
Heaps excited to share our new blog post covering the full pipeline, from distilling the π³ to augmenting RL with a reasoning cache that unlocks extreme inference-time scaling for theorem proving.
https://huggingface.co/spaces/lm-provers/qed-nano-blogpost
RT @_lewtun: We trained a tiny 4B model to reason for millions of tokens through IMO-level problems.
Heaps excited to share our new blog post covering the full pipeline, from distilling the π³ to augmenting RL with a reasoning cache that unlocks extreme inference-time scaling for theorem proving.
https://huggingface.co/spaces/lm-provers/qed-nano-blogpost
π1
Hugging Face (Twitter)
RT @j_dekoninck: Introducing QED-Nano: a 4B model for mathematical proof writing, competitive with larger models like GPT-OSS-120B.
We open-source our entire pipeline, including data, code, and a blog post, hoping that the community can build on these artifacts to create more specialized models.
RT @j_dekoninck: Introducing QED-Nano: a 4B model for mathematical proof writing, competitive with larger models like GPT-OSS-120B.
We open-source our entire pipeline, including data, code, and a blog post, hoping that the community can build on these artifacts to create more specialized models.
Hugging Face (Twitter)
RT @RisingSayak: Will be there at the @OfficialINDIAai Impact Summit in Delhi from 17-19.
Will also present ReflectionFlow at the symposium on the 18th.
Kinda surprised thereβs no real discussion about open science and open source, given the stellar speakers.
Anyway, looking forward to it!
RT @RisingSayak: Will be there at the @OfficialINDIAai Impact Summit in Delhi from 17-19.
Will also present ReflectionFlow at the symposium on the 18th.
Kinda surprised thereβs no real discussion about open science and open source, given the stellar speakers.
Anyway, looking forward to it!
β‘1π1
Hugging Face (Twitter)
RT @Alibaba_Qwen: π Qwen3.5-397B-A17B is here: The first open-weight model in the Qwen3.5 series.
πΌοΈNative multimodal. Trained for real-world agents.
β¨Powered by hybrid linear attention + sparse MoE and large-scale RL environment scaling.
β‘8.6xβ19.0x decoding throughput vs Qwen3-Max
π201 languages & dialects
πApache2.0 licensed
πDive in:
GitHub: github.com/QwenLM/Qwen3.5
Chat: chat.qwen.ai
APIοΌhttps://modelstudio.console.alibabacloud.com/ap-southeast-1/?tab=doc#/doc/?type=model&url=2840914_2&modelId=group-qwen3.5-plus
Qwen Code: github.com/QwenLM/qwen-code
Hugging Face: https://huggingface.co/collections/Qwen/qwen35
ModelScope: https://modelscope.cn/collections/Qwen/Qwen35
blog: qwen.ai/blog?id=qwen3.5
RT @Alibaba_Qwen: π Qwen3.5-397B-A17B is here: The first open-weight model in the Qwen3.5 series.
πΌοΈNative multimodal. Trained for real-world agents.
β¨Powered by hybrid linear attention + sparse MoE and large-scale RL environment scaling.
β‘8.6xβ19.0x decoding throughput vs Qwen3-Max
π201 languages & dialects
πApache2.0 licensed
πDive in:
GitHub: github.com/QwenLM/Qwen3.5
Chat: chat.qwen.ai
APIοΌhttps://modelstudio.console.alibabacloud.com/ap-southeast-1/?tab=doc#/doc/?type=model&url=2840914_2&modelId=group-qwen3.5-plus
Qwen Code: github.com/QwenLM/qwen-code
Hugging Face: https://huggingface.co/collections/Qwen/qwen35
ModelScope: https://modelscope.cn/collections/Qwen/Qwen35
blog: qwen.ai/blog?id=qwen3.5
π1
βHugging Face (Twitter)
RT @NielsRogge: Dots.ocr is known for being among the SOTA for OCR
Looks like the new 1.5 model is SOTA on OlmOCRBench!
https://huggingface.co/rednote-hilab/dots.ocr-1.5
RT @NielsRogge: Dots.ocr is known for being among the SOTA for OCR
Looks like the new 1.5 model is SOTA on OlmOCRBench!
https://huggingface.co/rednote-hilab/dots.ocr-1.5
X (formerly Twitter)
Niels Rogge (@NielsRogge) on X
Dots.ocr is known for being among the SOTA for OCR
Looks like the new 1.5 model is SOTA on OlmOCRBench!
https://t.co/HwzeuKzzFD
Looks like the new 1.5 model is SOTA on OlmOCRBench!
https://t.co/HwzeuKzzFD
π1
Hugging Face (Twitter)
RT @AdinaYakup: Happy Spring Festivalπ§§π
Hereβs to another year of building and sharing!
ζ°ηδΈεΉ΄οΌη»§η»εΌζΊεθ‘ π€
RT @AdinaYakup: Happy Spring Festivalπ§§π
Hereβs to another year of building and sharing!
ζ°ηδΈεΉ΄οΌη»§η»εΌζΊεθ‘ π€
π₯°1
Hugging Face (Twitter)
RT @Cohere_Labs: Very special to work with our @huggingface friends to bring Tiny Aya, the most capable multilingual open-weight model at its scale to the world! π
Big thanks to @ngxson for the huge help merging the changes into llama.cpp.
RT @Cohere_Labs: Very special to work with our @huggingface friends to bring Tiny Aya, the most capable multilingual open-weight model at its scale to the world! π
Big thanks to @ngxson for the huge help merging the changes into llama.cpp.
π1
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Hugging Face (Twitter)
RT @Cohere_Labs: Introducing β¨Tiny Ayaβ¨, a family of massively multilingual small language models built to run where people actually are.
Tiny Aya delivers strong multilingual performance in 70+ global languages in a 3.35B parameter model, efficient enough to run locally, even on a phone.
RT @Cohere_Labs: Introducing β¨Tiny Ayaβ¨, a family of massively multilingual small language models built to run where people actually are.
Tiny Aya delivers strong multilingual performance in 70+ global languages in a 3.35B parameter model, efficient enough to run locally, even on a phone.
Hugging Face (Twitter)
RT @evijit: Today, @evaluatingevals is introducing Every Eval Ever, a unified, open data format and public dataset for AI evaluation results.
RT @evijit: Today, @evaluatingevals is introducing Every Eval Ever, a unified, open data format and public dataset for AI evaluation results.