AK@_akhaliq · 2天前49

open-fusion in claude code with hf-claude

译在 Claude Code 中使用 hf-claude 进行 open-fusion。

AK@_akhaliq · 2天前40

Ornith-1.0-35B is now available in claude code through hf-claude

译Ornith-1.0-35B 现在可以通过 hf-claude 在 Claude Code 中使用。

SenseTime@SenseTime_AI · 3天前35

As highlighted by YouTuber CAPITAL R, 𝗦𝗲𝗻𝘀𝗲𝗡𝗼𝘃𝗮-𝗨𝟭-𝟴𝗕-𝗠𝗼𝗧-𝗜𝗻𝗳𝗼𝗴𝗿𝗮𝗽𝗵𝗶𝗰 can generate 𝘀𝘁𝘂𝗱𝗶𝗼-𝗾𝘂𝗮𝗹𝗶𝘁𝘆, high-density infographics. This 𝘂𝗻𝗹𝗼𝗰𝗸𝘀 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝘁𝗵𝗮𝘁 𝘄𝗲𝗿𝗲 𝗽𝗿𝗲𝘃𝗶𝗼𝘂𝘀𝗹𝘆 𝘀𝗹𝗼𝘄 𝗮𝗻𝗱 𝗲𝘅𝗽𝗲𝗻𝘀𝗶𝘃𝗲. 📹 𝗪𝗮𝘁𝗰𝗵 𝘁𝗵𝗲 𝘃𝗶𝗱𝗲𝗼: https://www.youtube.com/watch?v=6MgQlox8jNA 🤗 https://huggingface.co/sensenova/SenseNova-U1-8B-MoT-Infographic 🖼️ Showcases: https://github.com/OpenSenseNova/SenseNova-U1/blob/main/docs/u1_infographic_showcases.md 👾 Discord: https://discord.com/

译商汤推出 SenseNova-U1-8B-MoT-Infographic 模型，能够生成工作室级别的高密度信息图，此前这类工作流程缓慢且昂贵。YouTuber CAPITAL R 制作了演示视频，模型已在 HuggingFace 上线，GitHub 页面展示示例图片，并开放 Discord 社区。

AK@_akhaliq · 4天前46

baidu/Unlimited-OCR is now number 1 model on huggingface

译baidu/Unlimited-OCR 现在在 HuggingFace 上排名第一

Nathan Lambert@natolambert · 4天前59

This is real and a horrible consequence of vibe regulation of frontier models.

译这是真实的，也是前沿模型氛围监管的可怕后果。

AK@_akhaliq · 5天前56

hf-claude lets you use over 100 open models in claude code including glm 5.2, minimax-m3, deepseek v4 pro

译hf-claude 让你在 Claude Code 中使用超过 100 个开源模型，包括 GLM 5.2、MiniMax-M3、DeepSeek V4 Pro。

AK@_akhaliq · 6天前40

ViQ Text-Aligned Visual Quantized Representations at Any Resolution

译ViQ 文本对齐的视觉量化表示，支持任意分辨率。

SenseTime@SenseTime_AI · 6天前60

𝗦𝗲𝗻𝘀𝗲𝗡𝗼𝘃𝗮 𝗨𝟭 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗰𝗼𝗱𝗲 𝗶𝘀 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲𝗱 — 𝗳𝘂𝗹𝗹 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝘀𝘁𝗮𝗰𝗸, 𝗶𝗻𝘀𝗽𝗲𝗰𝘁𝗮𝗯𝗹𝗲, 𝗺𝗼𝗱𝗶𝗳𝗶𝗮𝗯𝗹𝗲, 𝗿𝗲𝗯𝘂𝗶𝗹𝗱𝗮𝗯𝗹𝗲. Also released: a smoke-test dataset spanning all 7 task types — t2i · it2i · it2i (multi-img) · interleave_gen · multimodal understanding · video understanding · pure language continuation Use it to: 🔹Bring your own data in this schema to fine-tune U1 into a specialist 🔹Validate your data against the official schema 🔹Smoke-test your pipeline end-to-end 🤗 https://huggingface.co/datasets/sensenova/SenseNova-U1-Training-Sample 🛠️https://github.com/OpenSenseNova/SenseNova-U1 Sample previews demonstrating the diverse task coverage included in our open-source smoke-test dataset. 👇

译商汤开源 SenseNova U1 完整训练代码，提供可检查、可修改、可重建的完整训练栈。同步发布 smoke-test 数据集，覆盖 t2i、it2i、多图输入、交错生成、多模态理解、视频理解、纯语言续写 7 种任务类型。用户可基于该 schema 用自有数据微调 U1，或验证数据格式及端到端测试 pipeline。数据集已上架 HuggingFace，代码托管于 GitHub。

MiniMax (official)@MiniMax_AI · 6天前44

More great options for the open-weight ecosystem. Thanks @NVIDIAAI for making MiniMax M3 available in NVFP4.

译开源权重生态的更多好选择。感谢 @NVIDIAAI 使 MiniMax M3 可在 NVFP4 中使用。

AK@_akhaliq · 7天前41

glm 5.2 in hf-claude building a gradio server app for Ornith-1.0-9B

译在 hf-claude 中使用 glm 5.2 为 Ornith-1.0-9B 构建 Gradio 服务器应用。

AK@_akhaliq · 7天前38

Over 300 GLM 5.2 requests through hf-claude on HF Inference Providers for just $34

译通过 HF 推理提供商的 hf-claude，超过 300 次 GLM 5.2 请求仅花费 34 美元。

Berryxia.AI@berryxia · 7天前74

PaddleOCR的PP-OCRv6终于上Hugging Face了。 这次不只是精度又提升，还一次性加了transformers和ONNX Runtime两个后端。 意味着你现在可以用更统一的API，在不同推理框架之间无缝切换，而不用改太多代码。 PaddleOCR一直以来都是工业界用得最多的开源OCR方案之一，这次上HF + 多后端支持，等于把门槛又拉低了一大截。 尤其是想在transformers生态里直接用高性能OCR的人，这次可以直接上手了。 从之前的Unlimited-OCR到这次PP-OCRv6，国内团队在长文档和实用OCR方向上确实在持续迭代，而且越来越注重工程可用性。 地址见评论区👇

译PaddleOCR 的 PP-OCRv6（对应 PaddleOCR 3.7）正式上线 HuggingFace，精度进一步提升，并新增 transformers 和 ONNX Runtime 两个推理后端。用户可通过统一 API 在不同后端之间无缝切换，无需大幅修改代码。PP-OCRv6 是工业界广泛使用的开源 OCR 方案，此次上架 HF 并支持多后端，降低了工程接入门槛，尤其利好希望在 transformers 生态中直接使用高性能 OCR 的开发者。

AK@_akhaliq · 6月25日11

glm-5.2 in hf-claude working on a gradio workflow for Krea-2-Turbo

译glm-5.2 在 hf-claude 中，正在为 Krea-2-Turbo 开发一个 gradio 工作流。

AK@_akhaliq · 6月24日42

hf-claude works well with glm 5.2 hf extensions install hf-claude

译hf-claude 与 GLM 5.2 兼容良好 hf extensions 安装 hf-claude

Hao AI Lab@haoailab · 6月24日73

(1/5) 5 seconds of video. 1.8s seconds of generation. One NVIDIA GeForce RTX 5090 on FastVideo. 🤯🚀 - FastWan-QAD, a new family of video generation models - Trained with FastVideo's Quantization-Aware Distillation (QAD) recipe. - Powered by FastVideo, we push a single NVIDIA GeForce RTX 5090 to its absolute limit: generating a 5-second 480P video in 1.8s end-to-end! 📜 Blog: https://haoailab.com/blogs/fastwan-qad/ 💻 Code: https://github.com/hao-ai-lab/FastVideo 💽 Model: https://huggingface.co/FastVideo/FastWan-QAD-1.3B

译Sky Computing Lab 发布 FastWan-QAD 视频生成模型系列，基于 FastVideo 的量化感知蒸馏（QAD）方案训练。在单张 NVIDIA GeForce RTX 5090 上，端到端生成一段 5 秒 480P 视频仅需 1.8 秒。模型、代码及博客已开源。

Baidu Inc.@Baidu_Inc · 6月23日71

3B total parameters & 500M activated, yet powerful enough to transcribe 40+ pages in one pass while keeping context intact. Meet Unlimited OCR!

译百度开源Unlimited OCR，专为一次性读取长文档设计。模型总参数量3B，仅激活500M，在OmniDocBench v1.5和v1.6上取得端到端SOTA。核心创新为参考滑动窗口注意力（R-SWA），模拟人类抄书过程，保持源、近期上下文和后续焦点，同时软遗忘无关信息。凭借恒定KV缓存大小和更低注意力成本，可在单次前向传播中转录40+页，不丢失上下文也不减速。模型已开源至GitHub和Hugging Face。

Berryxia.AI@berryxia · 6月23日66

这速度真特么离谱啊！卧槽！ 最新开源的Unlimited-OCR能一次性处理几百页文档，而且速度还很稳。 而这个模型来自百度刚刚在hugging face 发布，其核心创新是R-SWA（Reference Sliding Window Attention）。 它让模型在解码时KV Cache保持恒定，不会随着文档页数增加而爆炸式增长。 结果就是：一张图或者一本多页PDF，直接扔进去就能一次性解析完，速度和稳定性都比传统逐页处理的方式好很多。 在OmniDocBench上拿到了93分，比DeepSeek-OCR高出6个百分点。 这已经不是简单的准确率提升，而是把长文档OCR的工作流从“分块+外部调度器拼接”变成了真正的端到端一镜到底。 以前做多页文档最头疼的就是上下文断裂和格式不一致，现在模型能一次性看到整篇文档的结构、布局和逻辑关系，输出质量自然上了一个台阶。 这其实是把OCR从“认字工具”往“长文档理解引擎”又往前推了一大步。 技术路线很清晰，也很实用。 果然百度现在OCR独树一帜，遥遥领先了。 模型地址见评论区～ 👇

译百度PaddlePaddle在HuggingFace发布Unlimited-OCR，核心创新R-SWA（Reference Sliding Window Attention）使解码时KV Cache保持恒定，避免随页数爆炸。该模型可一次性处理数百页文档，速度和稳定性优于逐页处理。在OmniDocBench上得分93%，比DeepSeek-OCR高出6个百分点。这使长文档OCR从“分块+拼接”变为端到端一镜到底，直接理解整篇文档结构与布局。

SenseTime@SenseTime_AI · 6月22日50

Thanks YouTuber xCreate for the great breakdown of 𝗦𝗲𝗻𝘀𝗲𝗡𝗼𝘃𝗮 𝗨1! Great to see the model's powerful 𝘁𝗲𝘅𝘁-𝗶𝗺𝗮𝗴𝗲 𝗶𝗻𝘁𝗲𝗿𝗹𝗲𝗮𝘃𝗲𝗱 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 and 𝗶𝗻𝗳𝗼𝗴𝗿𝗮𝗽𝗵𝗶𝗰 capabilities featured — along with a 𝘀𝘁𝗲𝗽-𝗯𝘆-𝘀𝘁𝗲𝗽 𝗴𝘂𝗶𝗱𝗲 𝘁𝗼 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝗶𝘁 𝗹𝗼𝗰𝗮𝗹𝗹𝘆 𝗮𝗻𝗱 𝘃𝗶𝗮 𝗦𝗲𝗻𝘀𝗲𝗡𝗼𝘃𝗮 𝗦𝘁𝘂𝗱𝗶𝗼. 🎥https://www.youtube.com/watch?v=FWaQC_exKh0 🎛️SenseNova Studio: https://unify.light-ai.top (Try infographics; also join Discord for text-image interleaved gen) 🤗https://huggingface.co/collections/sensenova/sensenova-u1 🛠️https://github.com/OpenSenseNova/SenseNova-U1 👾Discord: https://discord.com/invite/BuTXPHmQub

译商汤SenseTime发布推文，感谢YouTuber xCreate对SenseNova U1模型的详细拆解，重点展示其文本-图像交错推理（text-image interleaved reasoning）和信息图表（infographic）生成能力。推文附有逐步指南，指导用户通过SenseNova Studio在线使用或本地运行模型。相关资源包括YouTube视频、HuggingFace模型集合、GitHub仓库及Discord社区链接。

Rohan Paul@rohanpaul_ai · 6月22日57

A massive legal datasets just dropped on Huggingface. For the first time, researchers used AI to gather, run optical character recognition, process, and build a database of every law in America. That is 2.2M laws. LocalLaws/LOCUS-v1 - Datasets on Hugging Face.

译一个庞大的法律数据集刚刚在 Huggingface 上发布。 研究人员首次使用 AI 收集、运行光学字符识别、处理并构建了全美每一条法律的数据库。 那就是 220 万条法律。 LocalLaws/LOCUS-v1 - Hugging Face 上的数据集。

SenseTime@SenseTime_AI · 6月19日45

#𝗔𝗜 changed the game for interior design. Shoutout to @eigi_and_ai for putting 𝗦𝗲𝗻𝘀𝗲𝗡𝗼𝘃𝗮 𝗨1 to the test! By uploading a simple living room photo, he used U1’s 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝘆-𝗳𝗶𝗿𝘀𝘁 𝘁𝗲𝘅𝘁-𝗶𝗺𝗮𝗴𝗲 𝗶𝗻𝘁𝗲𝗿𝗹𝗲𝗮𝘃𝗲𝗱 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 to instantly transform it into a cozy reading nook. Which room in your home needs a makeover? Try U1 today! 🎥https://www.youtube.com/watch?v=395Fk3WveRk 🎛️ SenseNova Studio: https://unify.light-ai.top (Try infographics; also join Discord for text-image interleaved gen) 🤗https://huggingface.co/collections/sensenova/sensenova-u1 🛠️ https://github.com/OpenSenseNova/SenseNova-U1 👾 Discord: https://discord.com/invite/BuTXPHmQub

译商汤 SenseNova U1 具备行业首创的文本-图像交错生成能力。用户上传客厅照片后，U1 可瞬间将其风格转变为温馨阅读角。该演示由 @eigi_and_ai 完成。U1 现已通过 SenseNova Studio、HuggingFace 模型库、GitHub 源码及 Discord 社区开放体验。

Artificial Analysis@ArtificialAnlys · 6月19日55

Announcing AA-Briefcase, the benchmark for the next era of agentic knowledge work AA-Briefcase is our new benchmark for testing models on long-horizon knowledge work tasks in complex projects built by industry experts. Models are evaluated on multi-week projects, each with many linked tasks and thousands of input source files. We evaluated Claude Fable 5 from @AnthropicAI before it became unavailable, and it currently leads with an Elo score of 1587, followed by Claude Opus 4.8 (max, 1356), Opus 4.7, and the recently-released GLM 5.2 (max, 1266) from @Zai_org. Claude Fable 5 cost $31 on average to run each AA-Briefcase task, followed by Claude Opus 4.8 at $10.40, GPT-5.5 (xhigh) at $3.68 and GLM-5.2 (max) at $2.40. AA-Briefcase comprises four private scenarios, each representing a multi-week knowledge work project set in a realistic organizational context. A public fifth scenario has been released via @huggingface as a representation of scenario structure, submission, and grading (AA-Briefcase Lite). This does not count toward official AA-Briefcase results, and is demonstrative only. Key elements of AA-Briefcase: ➤ Realistic long-horizon projects: AA-Briefcase moves beyond single, disconnected prompts by evaluating models across a coherent long-horizon project. Tasks build week by week, draw on shared institutional context, and require deliverables such as financial models, board presentations, and design mock-ups ➤ Large volumes of fragmented context: AA-Briefcase requires models to reason across thousands of inputs, including company documents, meeting transcripts, large-scale data exports, 25,000+ Slack messages and 3,500+ emails. These sources are fragmented, messy, and often contain realistic contradiction, testing whether models can navigate the ambiguity of real-world knowledge work ➤ Composite rubric and pairwise grading: AA-Briefcase combines binary rubric checks for ground-truth correctness with pairwise grading on analytical quality and presentation quality. Unlike many evaluations that focus on a single metric, AA-Briefcase tests agentic capabilities more comprehensively, exposing cases where models produce outputs that look polished but are incorrect or lack analytical rigor ➤ Built by industry experts: AA-Briefcase scenarios mirror real-world knowledge work, with tasks developed over months by experts across data science, product management and corporate strategy from companies including Google, McKinsey & Company and BCG. Task challenges are drawn from professional experience, making AA-Briefcase more reflective of the ambiguity, messy context and competing priorities that define real-world knowledge work Key results: ➤ Claude Fable 5 leads AA-Briefcase at 1587 Elo: This is followed by Claude Opus 4.8 (1356) with the next-best non-Anthropic model, GLM-5.2 (max), ~90 points back at 1266. Note that Claude Fable 5 did not use the Opus 4.8 fallback for any task in AA-Briefcase ➤ Cost per task varies by ~800x across models tested: Claude Fable 5 leads the benchmark but costs more than $31 per task on average, compared to ~$0.04 for DeepSeek V4 Flash (max). The strongest price/performance options are open weights models such as GLM-5.2 (max) and DeepSeek V4 Pro (max), with GLM-5.2 (max) scoring only ~90 Elo below Claude Opus 4.8 (max) for less than 25% of the cost ➤ Real-world complexity remains difficult for models: The top performer, Claude Fable 5, satisfies all rubric criteria on just 3% of AA-Briefcase tasks. On 31 of 91 tasks, no model scores above 50% on the rubric criteria ➤ Task difficulty scales with the number of required input files: For each rubric check, we identify the set of source files needed to pass. Across all models, pass rates fall as this file count increases, though top-tier models degrade less than weaker models More details below in thread ⬇️

译Artificial Analysis 推出新基准 AA-Briefcase，用于评估模型在长期知识工作项目中的智能体能力。基准包含 4 个私有场景（每项目需处理 25000+ Slack 消息、3500+ 邮件等碎片化上下文）及一个公开演示场景。评测结果：Claude Fable 5 以 Elo 1587 领先，其次为 Claude Opus 4.8（1356）、Opus 4.7 及智谱 GLM 5.2（max，1266）。成本方面，Claude Fable 5 平均每任务 $31，Opus 4.8 为 $10.40，GPT-5.5 (xhigh) 为 $3.68，GLM 5.2 (max) 为 $2.40，DeepSeek V4 Flash (max) 仅约 $0.04。所有模型中仅 3% 的任务满足全部标准，31/91 个任务无模型得分超 50%，显示真实世界复杂性仍是挑战。最佳性价比为开源权重模型 GLM-5.2 (max) 和 DeepSeek V4 Pro (max)。

Z.ai@Zai_org · 6月19日37

GLM-5.2 is free when used with Hugging Face Inference Providers for the next 5 hours: http://huggingface.co/zai-org/GLM-5.2?inference_provider=zai-org&language=python&client=openai&inference_api=true

译智谱推出 GLM-5.2 模型，在 Hugging Face Inference Providers 上限时免费使用，主推文称持续5小时（引用称6小时）。支持提供商包括 Zai、Together AI、Novita、Fireworks、DeepInfra。该模型可搭配 Pi、opencode、Codex、Claude Code 等编程智能体，体现开源模型已迎头赶上。

AYi@AYi_AInotes · 6月19日74

把 1.5TB 的模型剁掉 84% 的体积，塞进本地跑，还剩 82% 的功力， 这就是GLM-5.2，最强开源模型， 现在缩骨到了 238GB，256GB 的 Mac 或者同档 RAM/VRAM 的机器就能带起来了 技术博客：http://z.ai/blog/glm-5.2 权重：http://huggingface.co/zai-org/GLM-5.2 API：https://docs.z.ai/guides/llm/glm-5.2 编码计划：http://z.ai/subscribe

译GLM-5.2 发布开源权重，MIT 许可。原 1.5TB 模型经 84% 压缩至 238GB，可在 256GB Mac 或同档硬件本地运行，保留 82% 性能。拥有 1M 上下文窗口，编码和智能体任务显著提升。提供两种推理力度：GLM-5.2 (max) 极限推理，GLM-5.2 (high) 平衡性能与 token 效率。API 定价与 GLM-5.1 相同。

AK@_akhaliq · 6月18日41

GLM-5.2 is free on Hugging Face Inference Providers through Zai, Together AI, Novita, Fireworks, DeepInfra for the next 6 hours Set it up with Pi, opencode, Codex, Claude Code or any coding agent

译GLM-5.2 在 Hugging Face Inference Providers 上通过 Zai、Together AI、Novita、Fireworks、DeepInfra 免费提供，持续 6 小时 可搭配 Pi、opencode、Codex、Claude Code 或任何编码智能体进行设置。

Berryxia.AI@berryxia · 6月17日73

兄弟们，Claude Fable5 连夜下架！ GLM-5.2 直接宣布免费开源啊！ 而且直接把GLM-5.2的权重都开源了，而且还是MIT协议，1M上下文，在Coding和Agent任务上还有大幅提升。 他们这次重点强化了长程任务的Agent能力，覆盖大规模代码实现、自动化研究、性能优化和复杂调试。 实际表现上，Coding、Tool use、Reasoning都比GLM-5.1有明显进步，尤其在需要长时间规划和多步执行的场景里更稳。 还提供了Max和High两种推理模式，让你能在极致性能和Token效率之间自由切换。 API价格和上一代一样，同时在Slide生成、长文档处理、长文写作和长上下文角色扮演上也都有明显进步。 最关键的是权重彻底开源，社区已经在DeepSWE等基准上验证了它的实力。 这意味着有条件的开发者现在就能在本地或者自建环境里，跑起以前只有闭源大模型才能比较稳的长上下文Agent。 以前我们总觉得开源模型在真正硬核的Coding和Agent场景还差一截，现在这个差距又被拉近了一大步。 PS：我就希望可以算力更充足点… 😂 别无他求了 ……

译GLM-5.2 以 MIT 协议开源权重，支持 1M 上下文窗口。相比 GLM-5.1，在 Coding、Tool use、Reasoning 上明显提升，尤其在长程 Agent 任务（大规模代码实现、自动化研究、性能优化、复杂调试）中更稳定。提供 Max 和 High 两种推理模式，分别侧重极致性能与 Token 效率平衡。API 价格与上一代相同。社区已在 DeepSWE 等基准上验证其能力。此外，Slide 生成、长文档处理和角色扮演等任务也有进步。

Chubby♨️@kimmonismus · 6月17日83

Lets go, GLM-5.2 released as Open Weights model. tl;dr -1M context window -MIT-licensed open weights -Stronger long-horizon coding agents -Two reasoning modes: max and high -Same API pricing as GLM-5.1 Zai says GLM-5.2 was trained specifically for large-scale implementation, automated research, performance optimization, and complex debugging. Open Source got a serious upgrade today!

译GLM-5.2 作为开放权重模型发布，采用 MIT 许可，拥有 1M 上下文窗口。提供两种推理模式：max（极限推理）和 high（平衡性能与 token 效率）。在编码和智能体任务上有显著提升，专为大规模实现、自动化研究、性能优化和复杂调试训练。API 定价与 GLM-5.1 保持一致。

🚨 AI News | TestingCatalog@testingcatalog · 6月17日77

ZAI 🔥: GLM-5.2 is now available on huggingface! > It comes with a 1M context window and 2 levels of reasoning effort, max and high. MIT license and same pricing as GLM-5.1. > GLM-5.2 scores 46.2% on DeepSWE, the SOTA score among open-weight models.

译ZAI 在 Hugging Face 上发布 GLM-5.2，采用 MIT 开源许可，API 定价与 GLM-5.1 相同。模型支持 1M 上下文窗口，提供两种推理努力级别：max（极致性能）和 high（平衡性能与 token 效率）。在编程和 AI 智能体任务上有显著提升，具备长程任务能力。DeepSWE 基准得分 46.2%，创下开源权重模型的 SOTA 纪录。

SemiAnalysis@SemiAnalysis_ · 6月14日66

DAY 0 ALERT: @MiniMax_AI M3 is now available on HuggingFace & has been added to InferenceX. The M3 architecture has ~428B parameters and ~23B activated parameters. Due to the 10x engineers from @inferact, M3 is already delivering pretty well-optimized performance on @NVIDIAAI B300 Blackwell Ultra on Day 0 @vllm_project! Furthermore, Inferact released their EAGLE3 heads, which enable even greater performance. Looking forward to Day 1, 2, and 3 performance & the team is grinding on benchmarking Day 0 MI355X performance on InferenceX too.

译MiniMax M3 模型已上线 HuggingFace 并集成至 InferenceX。M3 总参数量约 428B，激活参数约 23B。得益于 Inferact 工程支持，M3 在 NVIDIA B300 Blackwell Ultra 上通过 vLLM 实现 Day 0 优化推理。Inferact 还发布了 EAGLE3 heads 以进一步加速。团队正在 InferenceX 上基准测试 Day 0 MI355X 性能。

MiniMax (official)@MiniMax_AI · 6月13日78

M3 would never 🙂‍↔️ As a matter of fact, the weights are now open, too. https://huggingface.co/MiniMaxAI/MiniMax-M3

译MiniMax 宣布其 M3 模型的权重已开源（HuggingFace 链接）。此前，Anthropic 因美国以国家安全为由发布出口管制指令，被迫立即禁用 Fable 5 和 Mythos 5 模型，禁止任何外国国民访问（包括外国员工）。MiniMax 以“M3 would never”暗讽其模型不受此类限制，并强调权重已开放。

MiniMax (official)@MiniMax_AI · 6月13日69

Run M3 locally today with @UnslothAI

译MiniMax-M3 是一款拥有 428B（23B 激活）参数、1M 上下文的新开源模型，性能与 Gemini 3.1 Pro 相当。可在 138GB 内存/显存上运行动态 2-bit GGUF 版本，或 165GB 上运行 3-bit 版本。在 @UnslothAI 的帮助下，今天即可本地运行 M3。

🚨 AI News | TestingCatalog@testingcatalog · 6月13日51

NVIDIA ❤️ MiniMax MiniMax M3 weights are now available on @huggingface, and NVIDIA now offers a Free Endpoint on its platform for testing. Testing time 👀

译NVIDIA ❤️ MiniMax MiniMax M3 权重现已在 @huggingface 上发布，NVIDIA 在其平台上提供免费端点用于测试。 测试时间到 👀

MiniMax (official)@MiniMax_AI · 6月12日81

MiniMax M3, Open-Weight, Now On Hugging Face , with only ~428B parameters and ~23B activated parameters Weights: https://huggingface.co/MiniMaxAI/MiniMax-M3 MiniMax Sparse Attention: https://huggingface.co/papers/2606.13392

译MiniMax 发布开源权重模型 M3，约 428B 总参数、23B 激活参数，已上传 HuggingFace。该模型融合三种前沿能力：编码与智能体方面达 59.0% SWE-Bench Pro、66.0% Terminal Bench 2.1、34.8% SWE-fficiency、28.8% KernelBench Hard、74.2% MCP Atlas；采用 MiniMax 稀疏注意力将上下文窗口扩展至 1M token；原生多模态。同步上线 MiniMax Code 工具及 API 平台。权重与技术报告预计约 10 天后发布。

🚨 AI News | TestingCatalog@testingcatalog · 6月12日57

KIMI AI🔥: A new open-source “Kimi K2.7 Code” model has been released on APIs and Huggingface! > Improved coding & agent performance over K2.6 > Reasoning efficiency > Long-horizon coding Testing time 👀

译KIMI AI🔥: 一个新的开源“Kimi K2.7 Code”模型已在 API 和 Huggingface 上发布！ > 相比 K2.6，编码与智能体性能提升 > 推理效率 > 长时域编码 测试时间 👀

Rohan Paul@rohanpaul_ai · 6月11日64

Apodex-1.0-H just dropped a heavy-duty agent team for deep research Claims SOTA results by splitting web research across many agents and auditing every evidence chain before writing the answer. Treats deep research as a distributed systems problem for AI agents. Apodex uses an async agent team: an orchestrator assigns sub-agents separate contexts and tools, then fact-checker, conflict-reviewer, and draft-reviewer agents test weak claims. The real big deal is that Apodex is showing a possible “inference-time scaling” path for AI research, where better answers come not from one bigger model, but from many coordinated search agents, persistent traces, and a separate verification layer that audits the evidence before the final response is allowed to exist.

译Apodex-1.0-H 发布一个异步智能体团队，用于深度研究。协调者将子智能体分配到独立上下文和工具，再通过事实核查、冲突审查和草稿审查智能体检验弱主张。该方案将深度研究视为分布式系统问题，展示了推理时缩放路径：通过多个协调搜索智能体、持久追踪和独立验证层提升答案质量，而非依赖单一更大模型，并声称取得 SOTA 结果。

fofr@fofrAI · 6月11日69

DiffusionGemma, where the LLM picks words all at once. Which is 4x faster. You can get started with the weights and instructions here: https://huggingface.co/google/diffusiongemma-26B-A4B-it

译DiffusionGemma，大语言模型一次性选出所有词。速度快4倍。 你可以从这里获取权重和说明开始使用： https://huggingface.co/google/diffusiongemma-26B-A4B-it

AK@_akhaliq · 6月10日51

SpatialWorld Benchmarking Interactive Spatial Reasoning of Multimodal Agents in Real-World Tasks

译SpatialWorld 评测多模态智能体在真实世界任务中的交互式空间推理能力

fofr@fofrAI · 6月10日63

I asked my foffee agent to help make Gemma faster. I felt like a proud parent. https://huggingface.co/spaces/gemma-challenge/gemma-dashboard

译我让我的 foffee 智能体帮忙加速 Gemma。我感觉自己像个骄傲的家长。 https://huggingface.co/spaces/gemma-challenge/gemma-dashboard

OpenBMB@OpenBMB · 6月8日54

🚀 The Build Small Hackathon is officially live! Check it out here: 🔗 http://build-small-hackathon-field-guide.hf.space/partners/openbmb We're proud to partner with @HuggingFace and support builders around the world with OpenBMB models. Here are some inspiring use cases built with our models: 🪐 MiniCPM-V 4.6 (image / OCR / multimodal) → Automated financial document analysis https://x.com/OpenBMB/status/2062889699056984281 ⚡ MiniCPM5-1B (lightweight, on-device) → AI Desktop Pet running entirely on your local device https://x.com/OpenBMB/status/2058903454308069670 🌐 MiniCPM-o 4.5 (omni-modal) → Real-time video analysis application https://x.com/OpenBMB/status/2022341243859337613 🎙️ VoxCPM2 (voice / TTS) → Voice cloning application https://x.com/OpenBMB/status/2041169065020936464 🏆 $10,000 OpenBMB Special Prize is available for outstanding projects built with our models. Get creative, try out our models, and see what you can build in this hackathon! We can't wait to see what you create. ✊ #BuildSmall #MiniCPM #OpenSourceAI #HuggingFace

译面壁智能（OpenBMB）与HuggingFace合作举办的Build Small黑客松正式上线，鼓励开发者使用OpenBMB模型构建应用。官方列举四大用例：MiniCPM-V 4.6用于自动化金融文档分析；MiniCPM5-1B轻量端侧模型驱动AI桌面宠物；MiniCPM-o 4.5全模态实时视频分析；VoxCPM2语音克隆。活动设有$10,000 OpenBMB特别奖。

SenseTime@SenseTime_AI · 6月4日69

"𝗦𝗲𝗿𝗶𝗼𝘂𝘀𝗹𝘆 𝗶𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝘃𝗲 𝘀𝘁𝘂𝗳𝗳". Thanks for the kind words, @gurru_tech — that's 𝗦𝗲𝗻𝘀𝗲𝗡𝗼𝘃𝗮 𝗨𝟭 turning prompts into professional infographics. Unified model that natively understands and generates text and images. Open-sourced. Run it yourself. 🎥Watch the video: https://youtu.be/HKz2e3STUwg 🎛️ SenseNova Studio: https://unify.light-ai.top/ (Try infographics; also join Discord for text-image interleaved gen) 🤗 https://huggingface.co/collections/sensenova/sensenova-u1 🛠️ https://github.com/OpenSenseNova/SenseNova-U1 👾 Discord: https://discord.com/invite/BuTXPHmQub @huggingface @github

译商汤SenseTime发布SenseNova U1，一个原生理解和生成文本与图像的统一模型。该模型已开源，用户可自行运行。被@gurru_tech称赞“令人印象深刻”。提供在线演示平台SenseNova Studio、HuggingFace模型、GitHub代码及Discord社区。

SenseTime@SenseTime_AI · 6月3日73

A plain sneaker image went in. Marketing visuals came out. #SenseNova U1 — see, think, create — all in one model. #OpenSourced. This is the architecture shift people keep talking about. Shoutout @AiLockup for the demo 🔥 🎥Watch the video: https://youtu.be/9IFgPqMWBGg Try it today: 🎛️ SenseNova Studio: https://unify.light-ai.top/ (Try infographics; also join Discord for text-image interleaved gen) 🤗 https://huggingface.co/collections/sensenova/sensenova-u1 🛠️ https://github.com/OpenSenseNova/SenseNova-U1 👾 Discord: https://discord.com/invite/BuTXPHmQub @huggingface @github

译商汤（SenseTime）开源SenseNova U1模型，宣称实现“看、思考、创作”一体——从一张普通运动鞋图片直接生成营销视觉效果。该模型代表了架构上的范式转变。用户可通过SenseNova Studio、HuggingFace和GitHub尝试使用。