搜索“Hugging Face”

用のText Generation APIを新たに提供する。Windows MLがモデル形式に応じて推論エンジンを自動で選び、GGUFモデルにはllama.cpp、ONNXモデルにはONNX Runtimeを使う。テキストをモデルが扱う単位（トークン）に分割する処理から、文章の生成、生成途中のテキストを順次返す処理までをAPI側が担う。これによって開発者は、Hugging Faceで公開されたGGUFモデルを取得し、Windows MLを通じてローカルで試せる。 Windows MLへの統合に加え、Microsoftは、NVIDIAやコミュニティと協力してllama.cpp本体の性能も改善したと説明している。NVIDIA GPUでの計算に使うCUDAの処理を最適化したほか、CPUとGPUの間での処理の割り振りも見直した。 Windows MLサーバーは、PC上で動くローカルサーバーで、アプリからの要求を受けてモデルを実行する。OpenAI互換のAPIを備えており、開発用ライブラリのOpenAI SDKの接続先をこのサーバーに変更すれば、ローカルモデルを利用できる。 Microsoftは

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66，有比 ESP32 更强的本地能力，不少做机器人的都用了瑞芯微的芯片，说实话，我们自己在预研探索的产品用的也是 RK 系列的芯片，不过他们的芯片和 ESP32 系列不同的是做不小：我们现在已经遇到了想做小但散热解决不了的问题，甚至才用到它 CPU 的 20% 左右就烫得不行。再看看做 MicroDuck 的 Pollen Robotics，去年 4月被 Hugging Face 收了，今年 9月2日英伟达又宣布 129 亿美元收购 Hugging Face，LeRobot 和这些机器人背后站着的是英伟达，英伟达的模型和算力已经很牛逼，但虚拟世界再怎么模拟计算，再怎么解决 SIM2REAL 的 GAP，GAP 就是会存在，不如真实世界来的好。做这些产品，做不出来什么有用的东西，但能让团队/人了解开发具身智能这套工具链，就像我做这个没用的 Muse Gadget 一样，我知道他是怎么回事了，后面真想做有用的，或者一直跟他的技术进步，真能做出来有用的东西。 9月国内的安克也做了一件事情，搞首届黑客松（9月7日报名，10月中旬在深圳决赛），把自己公司成熟硬件产品的 SDK 开出来，邀请各位

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nd, including repeat observations. Once notified, a large number of these partners immediately revoke the token: OpenAI API keys, Google Cloud account credentials, Slack webhooks, Hugging Face user tokens, SendGrid keys, etc. The owner may still need to replace the token, but revocation can happen without waiting for a developer to find and process a GitHub alert. Push protection intervenes earlier. It stops recognizable credentials before it enters repository history, giving the developer or ag

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faster than n log n in OpenAI’s math repo (3.1K); OpenAI Decisions API public beta (2.8K) /r/LocalLlama + /r/localLLM Recap 1. Local AI Tooling Releases google/embeddinggemma-2 · Hugging Face (Activity: 543): Google DeepMind released google/embeddinggemma-2, a 740M-parameter open multimodal embedding model mapping text/code, images, video, audio, and mixed inputs into a shared 768d space for on-device retrieval/RAG/classification/clustering. It uses modular encoders—270M text, 170M vision, 300M

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from a pydantic schema. We install the released package, laya 0.3.27, and load the English checkpoint. Two choices here keep the run reproducible. By default laya.load follows the Hugging Face main branch, so we pin the revision the library’s own authors reviewed, which it exposes as laya.PINNED REVISIONS. And on CUDA Laya autocasts to half precision, so we switch that off to keep every device in fp32 and let a GPU run reproduce the CPU numbers shown here. Printing the checkpoint’s shipped tempe

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虑对大型语言模型能有多聪明加以限制。 上周末我参加了The Curve，这是一个年度会议，汇聚了顶尖AI实验室的高管、非营利组织的领导者、政府官员以及一小群记者。在伯克利一家由酒店改造而成的场地内，这群人花了几天时间热烈辩论经济、政治和安全问题。尽管该会议自创办三年以来每年都关注存在性风险，但今年的问题显得尤为紧迫。 我认为有两种解释。其一是 OpenAI与Hugging Face事件的持续余波，其影响已令整个行业的人感到警觉。其二是 OpenAI 以及 Anthropic 概述了他们在递归自我改进方面的进展：即能够研究并训练其继任者的AI系统，这将带来越来越快的发布节奏，以及系统脱离其控制的风险上升。 在The Curve大会上，我听到多位演讲者表示，我们可能希望限制未来系统被允许达到的智能水平。取决于实验室将任何限制推进到何种程度，其结果可能等同于事实上一项禁止系统达到超人智能的禁令。 这是否对你来说有意义，取决于你是否相信，鉴于当前模型的架构，递归自我改进或超级智能是否真的可能实现。而这在很大程度上取决于你如何在此定义和评估“智能”。但这是我在一场充满值得关注讨论的会议上听到的最

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ipts aren’t modified. But misaligned AI systems with superhuman hacking skills could subvert many or all of the systems companies rely on to detect AI misbehavior. In the OpenAI / Hugging Face incident, for example, models severely compromised a chunk of OpenAI’s internal infrastructure. If this incident happened with more capable and/or more misaligned models, they might cover up most or all of the evidence, hiding it from employees at the company. Speculatively in the worst case, AIs could cre

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一条双输的道路上，除非人们接受细微差别、取舍和可怕的现实。 关于这场辩论中各方的观点，已有大量论述，我总结如下： 开放权重模型对社会功能构成难以承受的风险——这一立场由前沿实验室领导层和美国国家安全界占据；西方有声音认为开放权重模型对防御是必要的，禁止它们会让世界更不安全——这一立场由AI风险温和派以不同程度占据。我把自己也归入这一类，还有OpenAI事件后Hugging Face的视角，以及Joshua Saxe等人；中国企业继续发布具有强大网络能力的开放权重模型，它们是基于其社会和政府的风险评估做出决策的 这份清单同样按信息量或清晰度在西方AI生态系统中进行组织。以风险为中心的讨论获得了不成比例的可见度，其中包括一些我认为对跨假定党派界限就这一问题展开接触的潜力极为有害的作品。也几乎没有实质性的接触去试图理解中国公司如何评估风险——相反，大多是近乎人身攻击的论点，比如“中国实验室不在乎安全”。 反开放权重联盟的妄想 “开放权重很危险”阵营最近发布的文章，是Anthropic关于GLM-5.3作为攻击性网络工具的风险报告。问题不在于他们所做的技术研究——这些研究大体上是合理的——而在

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ningful, efficient and interpretable to downstream users. To support further research on accessible, in-language reasoning, we release our model and multilingual reasoning data on Hugging Face. For the full analysis, read the paper here. If you use the model, the data, or find this work useful, please cite the paper: We would like to thank everyone who contributed to this work and shared their insights: Ananya Sahu, Alejandro R. Salamanca, Daniel D'souza, Alexandre Berard, Thomas Euyang, David S

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infers control signals from raw video. How Rho-1 Compares Data verified on October 5, 2026 from official sources. Genie 3 access per Project Genie; Emu3.5 parameter count per its Hugging Face model card. Key Takeaways Rho-1 is a 19B model that reads and writes text, image, video and robot actions; Two expert streams share attention and one KV cache in every block; Distillation cuts denoising from 99 to 8 steps, about 1 second per 5.3 s clip; Trained on 320 H100s for 3 months; video is capped at

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science, Gemini 4 Argon guesses wrong on 15% of questions it doesn’t know, versus 29% for the next best model. GPT-6 Astra has the highest accuracy at 61% (data) Multi-harness RL (Hugging Face): A capture proxy speaks the OpenAI Chat, OpenAI Responses, Anthropic and Gemini formats. It forwards calls to vLLM and records exact token IDs and logprobs for TRL, so 10 unmodified harnesses become RL environments (Delangue, explainer).Results: The same weights score 62% under Mini-SWE-Agent and 33% unde

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Core app rather than the box itself. Core comes with three models preinstalled: Qwen-3.8-Next, Qwen-3.8-27B, and Gemma-4-31B. Javaid said users can also install other models from Hugging Face, where developers share AI models, or load their own trained models. Asked how Core differs from a Mac Mini, Apple’s small desktop computer that some people set up to run AI tools, Javaid said Core’s hardware and software were built specifically for running AI agents, with models already installed, while t

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oss-platform performance across a broad spectrum of edge devices and web (WASM). For a complete list of supported hardware platforms and the latest performance benchmarks, see our Hugging Face model card. You can try the LiteRT benchmark tool with more physical devices on Google Cloud or check out our zero-setup web demo for an instant, browser-based playground. EmbeddingGemma 2 and Google AI Edge provide the building blocks to bring fast, private, and offline multimodal retrieval directly into

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ma, available state/action features, and episode organization. We implement a byte-range Parquet reader that accesses only the required row groups and columns directly through the Hugging Face filesystem. We identify episode boundaries within a data shard and convert selected state and action fields into NumPy trajectories. We then visualize joint positions, gripper activity, Cartesian motion, action distributions, frequency spectra, and episode-duration statistics. We build a seek-based video p

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n model (FM) from the JumpStart catalog then provide the model ID. Example prompt: “Find the best instance for model huggingface-reasoning-qwen3-8b on SageMaker AI.”; Model on the Hugging Face Hub: You want to use a model hosted on Hugging Face. Provide the model name. For gated models (such as Llama variants), your agent surfaces the license terms and asks you to accept them and provide your Hugging Face token. Example prompt: “I want to deploy a Llama model from Hugging Face Hub. What’s the ch

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An OpenAI researcher called it “the most ‘feel the AGI’ moment that I had since reasoning models.” One of the most surprising aspects of July’s news that OpenAI agents attacked Hugging Face was how the agents had worked together. Hundreds of agents participated in the attack, and they did so in a pretty sophisticated way, organizing themselves unprompted into teams and dividing up their work. They referred to themselves as a “collective” or a “swarm”; some agents even made personal sacrifices to

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o, and a hospital. The research is ongoing and so few details are available, but the behavior shows how persistent AI agent activity has become on the internet. In the wake of the Hugging Face incident , many researchers are actively monitoring for rogue agent activity on the internet. Much of that activity has been easy to find because agents tend to use the same techniques and make little effort to conceal themselves. In this case, the agents don’t seem to have been doing anything more nefario

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o people is very clearly OpenAI speech. And then somewhere next to that is the kind of clarification law that AI companies are talking about in terms of agents and things like the Hugging Face attack, where they don’t know if a user directing an agent to do something bad is their fault. Whether that liability should pass through them if I’m the person who told my agent to go do some horrible hacking attack. I talked to the tech industry, and the thing they want from legislation is actual clarity

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8月底跟进，能够分析超过20分钟的视频。 Qwen2.5（9月）：2024年9月19日，在云栖大会上，阿里巴巴一次性发布了100多个开源模型。《Qwen2.5技术报告》称预训练数据从7万亿增长到18万亿个token。规模从0.5B到72B，具备128K上下文和8K token生成能力。 采用已经真实发生。阿里巴巴表示Qwen模型下载量已超过4000万次，并在Hugging Face上催生了超过5万个衍生模型。 编程与推理（11月至12月）：Qwen2.5-Coder于2024年11月11日发布全系列。随后是首个推理模型。QwQ-32B-Preview于11月下旬以Apache 2.0发布，作为OpenAI o1的开源挑战者。QVQ-72B-Preview，一个实验性视觉推理模型，于12月24日为这一年收官。 第三章——2025年初：回应DeepSeek 2025年1月属于DeepSeek-R1。Qwen的回应以周计，而非以月计。 1月26日，团队发布了Qwen2.5-VL，有3B、7B和72B三种规模。TechCrunch指出它可以控制PC和手机。三天后，即农历新年的第一天，阿里巴巴推

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ator like Intel XPU, AMD ROCm, Apple MPS, Qualcomm AI Engine and various accelerator integrated via PrivateUse1 mechanism, as well as ecosystem libraries such as vLLM, SGLang, and Hugging Face Transformers. While PyTorch’s own upstream CI is mature and extensive, it runs within pytorch/pytorch repo, leaving downstream repositories without a standard way to know when to test against upstream changes, report results back, or correlate failures across projects. This introduced a significant coordin

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