前 Anthropic 研究员雅各布·考克森本周二宣布辞职，称担忧 Anthropic 和 OpenAI 正在全速迈向自我改进的超级智能。Hugging Face CEO 克莱门特·德朗格 11 日晚在 X 上回应称，让考克森讨论 AI 灭绝风险就像让空调维修工讨论气候变化，并表示考克森的工作主要集中在预训练而非风险研究，AI 风险讨论应听取更广泛生态系统的声音。

Anthropic Hugging Face 大佬观点

另有 4 家信源报道

9月11日9月11日周五

星期五 · 1 条

00:09

Thomas Wolf@Thom_Wolf

AI 评分 52/100

Thomas Wolf 在 FT 发文谈 OpenAI/HF 事件，并宣布 Hugging Face 组建 Open Alignment 团队Two big updates1. I published an @FT op-ed on the OpenAI/HF incident & follow-ups2. We’re starting an Open Alignment team at @huggingface to work on safety & alignment for open models, incl cybersecurityNeed 100x more transparency & research on thishttps://www.ft.com/content/9faf688d-9192-418e-b7d3-c2202526e85e译Hugging Face 联创 Thomas Wolf 宣布在金融时报发表关于 OpenAI/HF 事件及后续的评论文章，并将在 Hugging Face 组建 Open Alignment 团队，负责开源模型的安全与对齐研究，包括网络安全方向。他认为该领域需要 100 倍的透明度和研究投入，文章链接见 ft.com。

Hugging Face OpenAI 行业动态

9月10日9月10日周四

星期四 · 8 条

19:59

Hugging Face：Blog（RSS）精选

AI 评分 65/100

Hugging Face 用 Gradio Workflow 重建 Workflow1111，复刻 AUTOMATIC1111 主要功能

Hugging Face 发布 Workflow1111，用 gr.Workflow 以 73 个节点、11 条媒体管线重建 AUTOMATIC1111 的大部分功能，覆盖文本生成图像、高清修复、图生图、prompt matrix、VLM 反推提示词、检测生成 inpaint 蒙版、ControlNet 式预处理器、背景移除、PNG Info 和图生视频。

Hugging Face 产品更新图像生成多模态

推荐理由：原文展示用 Gradio Workflow 重建 AUTOMATIC1111 主要功能的具体做法，并给出 REST 与 MCP 入口，读者可评估它与 ComfyUI 的取舍。

17:39

Thomas Wolf@Thom_Wolf

AI 评分 68/100

DeepSeek V4.1 Flash 发布，重回开源模型榜首The new DeepSeek V4.1 Flash model is mindblowing - back on top of the open-source model leaderboard and extremely cheap.It has a lot of very smart ways to be efficient and highly capable so I made a video of the forward pass to give you a view of what going on inside the model during inference.Read more at https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/DeepSeek_V41_Tech_Report.pdfAnd find the weights at https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash译Thomas Wolf 称 DeepSeek V4.1 Flash 重回开源模型榜首且价格极低，并制作视频展示模型推理时前向传播的内部过程。技术报告见 https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/DeepSeek_V41_Tech_Report.pdf，权重已发布在 https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash。

DeepSeek Hugging Face 开源生态推理

另有 8 家信源报道

17:07

OpenBMB@OpenBMB

AI 评分 56/100

面壁智能 MiniCPM5-2B 登顶 Hugging Face 趋势榜并开源全套训练资源🚀MiniCPM5-2B hits #1 on @huggingface Trending! 🏆 Huge thanks to the community for the incredible support!💗Ranked #1 among open-weight models under 4B parameters worldwide in the @ArtificialAnlys Intelligence Index.🤖 Built for Agentic AI Tool calling, deep search, code generation, and more—bringing capable Agents to phones, PCs, and vehicles.🧠 More than model weights We’re opening up training code, Agent SFT/RL data, UltraX, Meshy, and JustRL II, enabling deeper research and easier reproduction.📱 Ready for the edge Day 0 support for Intel, AMD & Arm, plus mainstream inference and fine-tuning frameworks.Try the model here: 🤗 Hugging Face: http://huggingface.co/openbmb/MiniCPM5-2B 💻 GitHub: http://github.com/OpenBMB/MiniCPM译面壁智能 OpenBMB 宣布 MiniCPM5-2B 登上 Hugging Face 趋势榜第一，并在 Artificial Analysis Intelligence Index 中位列全球 4B 参数以下开放权重模型第一名。

智能体 Hugging Face 开源生态模型发布

另有 3 家信源报道

16:37

OpenBMB@OpenBMB

AI 评分 61/100

面壁 MiniCPM5-2B 登顶 Hugging Face 趋势榜，并列 4B 以下开源模型智能指数第一🚀MiniCPM5-2B hits #1 on @huggingface Trending! 🏆 Huge thanks to the community for the incredible support!💗Ranked #1 among open-weight models under 4B parameters worldwide in the @ArtificialAnlys Intelligence Index.🤖 Built for Agentic AI Tool calling, deep search, code generation, and more—bringing capable Agents to phones, PCs, and vehicles.🧠 More than model weights We’re opening up training code, Agent SFT/RL data, UltraX, Meshu, and JustRL II, enabling deeper research and easier reproduction.📱 Ready for the edge Day 0 support for Intel, AMD & Arm, plus mainstream inference and fine-tuning frameworks.Try the model here: 🤗 Hugging Face: http://huggingface.co/openbmb/MiniCPM5-2B 💻 GitHub: http://github.com/OpenBMB/MiniCPM译面壁智能 OpenBMB 发布 MiniCPM5-2B，登顶 Hugging Face 趋势榜，并在 Artificial Analysis Intelligence Index 4B 参数以下开源权重模型中排名第一。

智能体 Hugging Face 开源生态模型发布

另有 1 家信源报道

14:29

🚨 AI News | TestingCatalog@testingcatalog

AI 评分 75/100

DeepSeek-V4.1-Flash 上架 Hugging Face：552B 参数 MoE 模型，新架构家族最小款DeepSeek V4.1 Flash is now available on Huggingface!552B parameters MoE model with a new Encoder-Decoder structure.V4.1 Flash adopted a new pre-training method and underwent larger-scale reinforcement learning post-training.The smallest model in DeepSeek new architecture family, with native visual understanding.译DeepSeek 发布 DeepSeek-V4.1-Flash 并上架 Hugging Face，为 552B 参数 MoE 模型，采用新的 Encoder-Decoder 结构、新预训练方法和更大规模强化学习后训练。

DeepSeek: 🚀 Introducing DeepSeek-V4.1-Flash: smarter, faster, more efficient. 🔹 Introducing the smallest model in our new archit...

DeepSeek Hugging Face 多模态推理

另有 8 家信源报道

08:00

Hugging Face：Blog（RSS）精选

AI 评分 61/100

Hugging Face 用 LoRA 与 Storage Bucket 在 HF Jobs 上跑异步 GRPO，免 NCCL 并提速 3.9 倍

Hugging Face 在 TRL v1.14 的 AsyncGRPOTrainer 中支持只训练和同步 LoRA adapter，配合 Storage Bucket 挂载与代理路由，让训练 Job 和 vLLM 推理 Job 在不同机器上运行而无需 NCCL。

Hugging Face 推理教程/实践部署/工程

推荐理由：Hugging Face 官方用五个实验展示了如何在 Jobs 上分离训练与推理跑异步 GRPO，并通过逐项定位瓶颈把运行提速 3.9 倍，方法可迁移。

01:57

Nathan Lambert@natolambert精选

AI 评分 85/100

Nathan Lambert 评 Nvidia 收购 HuggingFace：软实力每年值约 100 亿美元I never got to comment on Nvidia-HF because I was OOO, but something that struck me is how HuggingFace is a steal for Nvidia. HuggingFace's core ability is understanding how to influence the discussion and direction of AI, relative to their resources.For Nvidia, who is so rich and powerful, this ability is worth easily the ~$10B they paid, but every year. There are many sources of capital other than just hard cash -- HuggingFace is rich in those sources of soft power. It'll be a challenge for Nvidia to keep that culture and to foster it, but that challenge will be valuable growth for the company.Nvidia is a better buyer than one of the three clouds, as the clouds would feel more pressure to make money on it, to compete directly with github, etc. HuggingFace should now be given the directive of Jensens ambition, to be free from having to think of themselves as a "functioning business unit" and to be the most powerful comm's army in the AI discourse of all time.HuggingFace should be free to win the hearts and minds of the next 100 million AI developers, and I'm excited to see it happen. Having worked at HuggingFace, and stared down the barrel of making the company work as an independent public entity, this outcome should've been seen as somewhat inevitable.Congrats again to @Thom_Wolf @julien_c, @ClementDelangue, and all my friends at @huggingface for making something with such incredible value. Much of the culture was about creating value for the whole community, rather than capturing it for HuggingFace. This is now in line with Nvidia's "all-in" bet on open models (so intelligence isn't monopolized), and this next part of the chapter will be fun.My first day of work at HuggingFace, fresh off a red eye in Paris, still feels like yesterday (2022). When I posted the prediction below, last year, I actually got hate for it. lol.译Nathan Lambert 评价 Nvidia 收购 HuggingFace，认为 HuggingFace 影响 AI 讨论方向的能力对 Nvidia 值得每年付出约 100 亿美元。他认为 Nvidia 比三大云厂商更适合做买方，HuggingFace 应摆脱盈利单位定位，去争取下一代 1 亿 AI 开发者；作者曾在 HuggingFace 工作并于去年预言过这一收购。

Nathan Lambert: 英伟达应该收购 HuggingFace，以此作为一种低成本方式，在 CUDA 与开源生态系统之间建立更深层的整合。这几乎与自行训练领先的开源模型一样便宜，并且符合英伟达在 2025 年日益开放的趋势。

Hugging Face 大佬观点开源生态

推荐理由：曾在 HuggingFace 工作的作者分析了这起收购的软实力逻辑，指出其每年值约 100 亿美元的原因。

01:16

Ant Ling@AntLingAGI

AI 评分 52/100

蚂蚁百灵 Ling-3.0-flash-VL 联合 Novita 上线 Hugging Face，免费体验 14 天Now you can play with the VL model of Ling on Hugging Face with our day0 partner @novita_labs译蚂蚁百灵宣布与 day0 合作伙伴 Novita 合作，Ling-3.0-flash-VL 现已可在 Hugging Face 上体验，免费 14 天。该模型总参数 124B、每 token 激活 5.5B，原生支持图像和视频理解，面向多模态推理和智能体工作流。

Novita AI: 🤗 Novita now supports Ling-3.0-flash-VL on @huggingface. 🎁 Free for 14 days. • 124B total parameters · 5.5B active par...

Hugging Face 多模态开源生态模型发布

9月9日9月9日周三

星期三 · 1 条

18:57

The Decoder：AI News（RSS）

AI 评分 69/100

Hugging Face 推出 ML Intern，通过聊天即可运行机器学习实验

Hugging Face 推出内置于聊天机器人的 AI 助手 ML Intern，让没有 ML 专长的用户通过对话运行机器学习实验。它会搜索 Hugging Face Hub、GitHub 和网络寻找模型、数据集和工具，先估算算力成本并给出预算，获批后不超过限额；可自主创建数据集、训练模型、监控任务、上传结果、写报告和构建 demo，每个训练任务有独立仪表盘。

智能体 Hugging Face 产品更新

9月7日9月7日周一

星期一 · 2 条

21:37

OpenBMB@OpenBMB

AI 评分 53/100

面壁智能开源 MiniCPM5-2B，登顶 Artificial Analysis 4B 以下开源模型榜首🚀 Meet MiniCPM5-2B, a 2B-parameter language model bringing high intelligence density to the edge, now open source!It ranks #1 among open-source models under 4B parameters on the @ArtificialAnlys Intelligence Index, with a score of 23.It also scores 20 on the Agentic Index, bringing an early form of general-purpose agent capability to the edge.Across 34 benchmarks, MiniCPM5-2B achieves an average score of 53.9, covering coding, math, long-context understanding, tool use, and agentic tasks.And this release goes beyond the model itself.We’re opening up the data, training recipes, and RL stack behind MiniCPM5-2B.🤗 Hugging Face: http://huggingface.co/openbmb/MiniCPM5-2B 💻 GitHub: http://github.com/OpenBMB/MiniCPM译面壁智能（OpenBMB）开源 2B 参数端侧语言模型 MiniCPM5-2B，在 Artificial Analysis Intelligence Index 上以 23 分位列 4B 参数以下开源模型第一，Agentic Index 得分 20。

智能体 Hugging Face 开源生态模型发布

05:33

MarkTechPost（RSS）

AI 评分 52/100

H Company 发布 NeoMME 260M/800M 单塔多模态编码器，取消视觉塔和因果解码器

H Company 发布 NeoMME 系列多模态编码器，包含 260M 和 800M 两个双向 Transformer 模型，用单一塔处理多语言文本和 32×32 图像块，去掉了独立视觉塔和因果解码器，以离散掩码扩散方式预训练。

Hugging Face 多模态开源生态搜索

9月5日9月5日周六

星期六 · 1 条

01:36

ViggleAI@ViggleAI

AI 评分 57/100

Viggle 发布首个开放权重模型 Viggle-AnimateIntroducing Viggle-Animate, our first open-weight model.Built on MiniMax-H3, it replaces a character in a video from one repainted frame. No pose skeleton, mask or text prompt at render time.3 forward passes. 5s of video in 26s. Try now: https://huggingface.co/viggle/viggle-animate译Viggle 发布首个开放权重模型 Viggle-Animate，基于 MiniMax-H3 构建，可从一帧重绘帧替换视频中的人物，渲染时无需姿势骨架、遮罩或文本提示。模型经 3 次前向传播可在 26 秒内生成 5 秒视频，已在 Hugging Face 开放（https://huggingface.co/viggle/viggle-animate）。

Hugging Face 图像生成开源生态模型发布

9月4日9月4日周五

星期五 · 6 条

09:30

IT之家（RSS）已收录

AI 评分 86/100

英伟达拟以 129.3 亿美元收购 Hugging Face，黄仁勋承诺维持开放平台

英伟达 9 月 3 日宣布同意以 129.303 亿美元（约合 870.92 亿元人民币）收购 Hugging Face，黄仁勋随后发文解释收购原因。

Hugging Face 开源生态行业动态

09:04

Peter Steinberger 🦞@steipete已收录

AI 评分 93/100

NVIDIA 宣布收购 Hugging Face，黄仁勋称开放模型将受益于这桩联姻brilliant fit.译黄仁勋宣布 NVIDIA 将收购 Hugging Face，称开放模型能强化安全与网络安全、加速创新与扩散、支持主权 AI，让开发者、初创、大学和各国都能构建并定制 AI。他表示 NVIDIA 将成为 Hugging Face 及其社区和开放模型未来的好归宿，作者 Peter Steinberger 转发并评论这是绝佳组合。

Jensen Huang: 对 NVIDIA 和 @huggingface 来说是激动人心的一天。开放模型能增强安全性与网络安全，加速创新与扩散，并实现主权自主。它们让每一位开发者、初创公司、大学、行业和国家都能基于 AI 进行构建、定制并从中受益。感谢 @Cle...

Hugging Face 开源生态行业动态

08:34

AK@_akhaliq

AI 评分 26/100

Repo-To-Skill 论文提出将 GitHub 仓库蒸馏为 AI 技能Repo-To-SkillDistilling GitHub Repositories Into AI4AI Skillspaper: https://huggingface.co/papers/2609.02749译论文 Repo-To-Skill 研究如何将 GitHub 仓库蒸馏（Distilling）为 AI4AI Skills。论文链接：https://huggingface.co/papers/2609.02749。

智能体 Hugging Face 开源生态论文/研究

04:40

Rohan Paul@rohanpaul_ai

AI 评分 34/100

Hojo-ASR-Multi-V1 上线 Open ASR Leaderboard，开源多语言语音识别平均 WER 3.54%My criteria for buying tech have changed a lot over the years.The biggest limitation of the smartphone might be that it requires your attention.Almost every useful action begins the same way: pull it out, unlock it, find something, interact with a screen.AI hardware has a chance to break that loop. If a device can understand enough context to know when to listen, capture, surface, or act, the screen stops being the center of the product.Also, a device doesn't need hundreds of apps if it understands the one environment it was built for.That makes specialization much more economically interesting than it was ten years ago.Instead of asking, “How many things can this device do?” I want to ask, “How little interaction does this one job require?”That’s also why something like HojoAI/Hojo-ASR-Multi-V1 from @hojoHQ is worth looking at through a hardware lens.An open multilingual speech layer potentially changes what you can build around a very narrow use case.译HojoAI 的 Hojo-ASR-Multi-V1 正式上线 Open ASR Leaderboard，全球排名第 7、开源多语言 ASR 第 1，五语言平均 WER 3.54%。

HojoAI: #7 globally. #1 open multilingual ASR model. Hojo-ASR-Multi-V1 is officially live on the Open ASR Leaderboard. With an a...

Hugging Face 开源生态模型发布语音

00:39

Emad@EMostaque已收录

AI 评分 86/100

Hugging Face 宣布将以约 129.3 亿美元并入 NVIDIA，承诺保持平台开放独立Without Hugging Face the launch and scaling of stable diffusion & the models that followed would only have been a fraction of what they were.Great and super engaged team who have constantly flown the flag for open source 🤗译Hugging Face CEO Clement Delangue 宣布与 NVIDIA 达成约 $12，930，300，000 的收购意向，称开源 AI 处于拐点，需要更多算力与支持。

clem 🤗: 非常高兴地宣布，我们有意与 NVIDIA 达成一项 129.303 亿美元的收购合作 💛💚 自创办 Hugging Face 十年来，开源 AI 正处于一个关键转折点。感谢社区，我们已经证明开源 AI 可以成为闭源 API 的补充，甚至...

Hugging Face 开源生态行业动态

00:04

Artificial Intelligence News（网页）精选

AI 评分 83/100

NVIDIA 以 129.3 亿美元收购 Hugging Face

NVIDIA 已同意以 129.3 亿美元收购 Hugging Face，以扩展该开源模型仓库的平台与基础设施。交易目标是平台增长和基础设施投资，旨在为全球企业开发者、软件工程师和研究机构扩大 AI 访问机会。Hugging Face 由 Clem Delangue、Julien Chaumond、Thomas Wolf 等人在过去十年间打造。

Hugging Face 开源生态行业动态

推荐理由：原文报道 NVIDIA 收购 Hugging Face 的金额与用途，读者可以据此了解开源模型平台归属变化的方向。

9月3日9月3日周四

星期四 · 9 条

21:29

Hugging Face：Blog（RSS）

AI 评分 53/100

NeoMME 发布 260M 与 800M 多语言多模态编码器，权重以 Apache 2.0 开源

H Company 团队发布 NeoMME，含 260M 和 800M 两个尺寸的多语言多模态编码器，用单个双向 Transformer 从零处理文本 token 和 32×32 图像 patch，不使用预训练视觉塔或因果语言模型，训练采用掩码离散扩散目标，每个模型处理约 5240 亿 packed token。

Hugging Face 检索增强多模态开源生态

21:01

The Verge：AI（RSS）已收录

AI 评分 87/100

Nvidia 以约 129.3 亿美元收购 Hugging Face

Nvidia 宣布以 129.3 亿美元收购开源 AI 模型与数据集托管平台 Hugging Face，该平台 2023 年估值为 45 亿美元。

Hugging Face 开源生态行业动态

20:39

Thomas Wolf@Thom_Wolf已收录

AI 评分 90/100

Hugging Face 联创 Thomas Wolf 宣布以 $12，930，300，000 被 NVIDIA 收购So happy to finally share the news in personIt’s been a wild ride for Hugging Face. We certainly did not anticipate, back in 2016, as a tiny team of scrappy underdogs, that the field would grow so much or that the impact we could have on it would become so massive.I remember @julien_c joking that « code will be a subset of ML » several years ago. The joke turned out to be true, and the pleasure we’ve had being part of this transformation and pushing an alternative vision of AI as open, collaborative and distributed has been and still is immense.We’ve always built things seriously while not taking ourselves too seriously at Hugging Face (special congrats if you find the Hugging Face and Nvidia references hidden in our $12,930,300,000 acquisition price), and we plan to keep doing what we've been doing, just at a much bigger scale, backed by the resources, expertise and drive of Nvidia. And to be clear, nothing changes for our users today.No company in the world has been a more natural fit with our mission than Nvidia. From open-source, open-weights and open science to robotics and AI for science, they have been close partners across everything we care about. So when Jensen offered @ClementDelangue the opportunity to double down on building the Hub as an open, independent and compute agnostic platform, we decided the time was right to start the next 10 years of our journey together.We’re at an important inflection point for open-source AI, where scale and compute are becoming increasingly essential. We’re excited to have the resources to push further, build more ambitiously, and bring you even more projects and news in the coming months.译Hugging Face 联创 Thomas Wolf 宣布公司以 $12，930，300，000 被 NVIDIA 收购。他表示 Hub 将在 NVIDIA 支持下继续作为开放、独立、计算无关的平台发展，用户侧今天没有任何变化；团队认为开源 AI 正处于规模与算力日益关键的关键节点。