搜索“Hugging Face”

–100%（看 non-blocking）。轨迹若被扫到去拉上游解（PR、merge commit、镜像等）→ 也记 0。 2. SWE-Atlas-QnA → SWE-Atlas-QnA-AA。 124 题不变；重写了 1,306 条 rubric 里的 127 条；裁判改为 GPT-6 Luna（xhigh），每条准则投 5 票；数据集与裁判配置已发到 Hugging Face。 关键结果 综合指数前四： · Claude Code + Opus 5.5（max）61.7 · Claude Code + Sonnet 5.5（max）59.3 · Muse Code + Muse Wonder Full（max）58.9 · Claude Code + Fable 5.1（max + fallback）57.6 前四相差仅约 4.1 分，其中三席是 Claude Code。首个 OpenAI 组合是 Codex + GPT-6 Astra，54.7。 FrontierCode 单项（Opus 拉开差距处）： · Opus 5.5：56.0% · Codex + DeepSeek V

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的许可证。撰写本文时最强的开源图像模型 Qwen-Image-2.1 以研究许可证发布。 有些仓库甚至在宽松许可证的原版旁边还有一个非商业版本，因此缓存错误的仓库 ID 会改变你的许可证。 这套栈遵循的规则： 只允许 Apache 2.0、MIT、CC-BY-4.0、OpenMDW 和 NVIDIA Open Model License。 将每个模型固定到 Hugging Face 的某个提交，这样上游的许可证变更就无法悄无声息地生效。 在文档中注明 CC-BY-4.0 模型的署名。 这不是法律建议。在销售或为客户部署之前，请找人审查每一个已固定的许可证。 需要注意的事项 新的检查点可能需要比你固定的版本更新的 vLLM，而错误信息不会说明这一点。请先检查镜像内的版本。 faster-whisper 没有适用于 arm64 的 CUDA 构建，因此基于它构建的工具会在不告知你的情况下在 CPU 上运行。 这套栈使用 vLLM 而不是入门套件中自带的 Ollama。Ollama 适合一个人在笔记本电脑上使用。对于团队来说，vLLM 会把来自许多用户的请求批处理到每次对权重的遍历中，多用户

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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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de with Fable 5 access to the paper and the MetaInfer Repository, as well as access to an NVIDIA B200 workstation via SSH. I told it the target hardware and provided a link to the Hugging Face repo for both the NVFP4 quantized weights I wanted to use for Qwen-3.6-35B-A3B, as well as the original full-precision weights to be used as an accuracy oracle. The agent was also able to deploy engine candidates to Baseten, and to run AIPerf profiles against the deployed endpoint. We have internal agent s

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de with Fable 5 access to the paper and the MetaInfer Repository, as well as access to an NVIDIA B200 workstation via SSH. I told it the target hardware and provided a link to the Hugging Face repo for both the NVFP4 quantized weights I wanted to use for Qwen-3.6-35B-A3B, as well as the original full-precision weights to be used as an accuracy oracle. The agent was also able to deploy engine candidates to Baseten, and to run AIPerf profiles against the deployed endpoint. We have internal agent s

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test the time it takes to install common packages like PyTorch and vLLM via pip and uv, as well as download containers from Docker Hub, ghcr.io, and nvcr.io, and pull a model from Hugging Face and ModelScope. We then redo the pip and uv install tests with fresh package caches on each available storage tier: home, shared filesystem, local NVMe scratch, and any additional mounts (referred to as /home , /data , /scratch , and other). We then run import torch from each location to see how long that

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plies the output. So, it’s also borrowing the adaptive halting idea from Universal Transformer, which Nanbeige didn’t use. (However, there is a practical caveat here. The released Hugging Face implementation computes all configured passes before selecting an output, so it seems like the number of loops is effectively hard-coded to 4). 2.4 Routing flexible loop counts Another approach is Mixture-of-Recursions, a paper from 2025 that is essentially a more sophisticated version of the Universal Tra

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epSeek-V4-Flash MXFP4 GGUF blob from the Ollama model page and use ollama show --modelfile to obtain its local file path. The corresponding model card is DeepSeek-V4-Flash-0731 on Hugging Face: The recorded blob SHA-256 is 947ac34c08c0e5c5752ac76398f934b3b6b4075cfe915ba43dd5ac754900a4cd and the Ollama manifest SHA-256 is 882b1398c0ca4e7ec8ca0a501fd8c4372f780f690536a3ec17ffc75306569ed3; Install the SGLang checkout and build the DeepSeek Expert Pack manually. Use a matching DeepSeek model configur

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下来，AReno 还会持续补充更多面向小模型、Agentic RL、开发者实践和高校教学的案例，帮助开发者从更小、更直观的任务出发，逐步把后训练方法迁移到自己的真实场景中。 07 欢迎下载使用 Tiny，也欢迎参与贡献 AReno Ling-3.0-tiny 已正式开源，提供 BF16、FP8 和 INT4 版本。欢迎根据自己的设备与任务需求下载使用： 🤗 Hugging Face 也欢迎访问 AReno GitHub 仓库（https://github.com/inclusionAI/AReno），复现这次 Spark 后训练实验，提交 Issue、参与讨论，或者贡献 Tiny 个性化训练 Case。 如果你对以下方向感兴趣，欢迎参与 AReno 开源社区： 模型进入本地只是第一步，能否根据业务反馈持续适配，才决定它能否真正进入流程。 Ling-3.0-tiny × ASystem AReno 此次验证的，不是一个已经完成的行业解决方案，而是一条轻量、透明、可复现的任务适配路径：从跑起来，到训起来，再到回到任务中验证是否真的变好。 我们希望开放的不只是模型权重和训练工具，更是一种可被

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lowerCount":818,"isUserFollowing":false,"primaryOrg":{"avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1583856921041-5dd96eb166059660ed1ee413.png","fullname":"Hugging Face","name":"huggingface","type":"org","isHf":true,"details":"The AI community building the future.","plan":"team"}},"createdAt":"2026-09-08T06:42:11.000Z","type":"comment","data":{"edited":false,"hidden":false,"latest":{"raw":"How did you like the series? Anything that we could have done better?","html":" How

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thing with PRX, or find a bug in our pipeline? PRX is Apache 2.0 and the model code lives at github.com/Photoroom/PRX . We integrated PRX into the diffusers library and there is a Hugging Face Space to try out our latest version PRX Pixel . Come talk diffusion models and data with us on Discord . Qwen2.5-VL-7B-Captioner-Relaxed CMMD, "Rethinking FID" (Jayasumana et al., 2024) DINOv2 (Oquab et al., 2023), the features behind DINO-MMD Models mentioned in this article 7 Ertugrul/Qwen2.5-VL-7B-Capti

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arge v3 are available on Together. Reach out if you’re scaling voice AI in production. Parakeet v3 is the successor to v2, which was an English-only model that set the pace on the Hugging Face Open ASR Leaderboard for single-language throughput. v3 extends that foundation significantly, expanding language support from English to 25 European languages, adding automatic language detection without requiring a language prompt, and was trained on 1.7 million hours of audio data — including NVIDIA's G

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ntion pattern is not unique to Laguna XS.2 and is used by many other architectures (including Gemma 4). But what’s new is the use of per-layer query-head counts. For instance, the Hugging Face model hub config.json includes a num attention heads per layer setting, so layers can have different numbers of query heads while keeping the KV cache shape compatible. So Laguna XS.2 gives more query heads to sliding-window layers and fewer query heads to global layers, while keeping the KV heads fixed at

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x: After identifying the root cause, I collaborated with the Kimi team. They were incredibly responsive and, based on my findings, updated the model's tokenizer config.json on the Hugging Face Hub. The fix was to explicitly declare add generation prompt as a supported parameter for the chat template. This allowed vLLM to pass the argument correctly, fixing the primary source of failed tool calls. Additionally, I submitted this PR, where I whitelist standard chat-template parameters when tokenize

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throughput; Efficient and intelligent memory management; Robust support for batched requests; Deeply optimized backend performance Model Download You can download the models from Hugging Face: Deployment Below is a quick guide to deploying MiniMax-M1 with vLLM and Docker: MiniMax-M1 Hybrid Architecture Highlights Mixture-of-Experts (MoE) MiniMax-M1 utilizes a Mixture-of-Experts (MoE) architecture with 456 billion total parameters. During inference, a dynamic routing algorithm activates a sparse

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ly 2025); Transformers and vLLM: Inference in Action; vLLM’s Deployment Superpower: OpenAI Compatibility; Why do we need the Transformers modeling backend?; Case Study: Helium The Hugging Face Transformers library offers a flexible, unified interface to a vast ecosystem of model architectures. From research to fine-tuning on custom dataset, Transformers is the go-to toolkit for all. But when it comes to deploying these models at scale, inference speed and efficiency often take center stage. Ente

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