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Open-weight models
Releases of models with public weights, their licences and the open ecosystem.
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Skills and knowledge entries are written in Chinese, as are signals from Chinese sources.
Published signals
19 signalsAnyLanguageModel 1.0: One Year of Local Models on Apple Platforms
AnyLanguageModel 1.0: One Year of Local Models on Apple Platforms
记忆张量开源 MemT 1.0：把记忆原生写进 Transformer
记忆张量开源记忆原生模型 MemT 1.0，含 36B 总参数、激活 3B 的 Nano 与 288B 总参数、激活 17B 的 Flash 两个型号，在 Transformer 模块中加入与注意力并行的记忆分支，把经验编码为可读写的内部状态。论文称这是记忆原生模型首次完成近 300B 参数规模的工程验证。
DeepSeek V4.1 Flash 新版 1017：更强也更省
DeepSeek 推出 V4.1-Flash-1017 模型，在代码与智能体任务基准上提升的同时压缩了任务运行时间和输出长度，官方称为「性能—费用的帕累托改进」。配套 Harness 升级至 0.2.1 版本，并开放自研 Search 与 Fetch 两个搜索 API，每千次成功请求 5 元，11 月 1 日前免费。
Welcome Beam: a 501B open model you can own, customize, and scale
Welcome Beam: a 501B open model you can own, customize, and scale
Welcome Muse Spark Open: open-weight frontier multimodal mixture-of-experts model from Meta
Welcome Muse Spark Open: open-weight frontier multimodal mixture-of-experts model from Meta
EmbeddingGemma 2: an open, lightweight multimodal embedding model
EmbeddingGemma 2: an open, lightweight multimodal embedding model
Ollama v0.35.1: Clef decision models
Clef decision models Ollama now supports Clef and Clef Flash, Cloudflare's new open-source decision models, through /v1/systemone. Clef (27B) and Clef Flash (9B) are multimodal: requests can now include images alongside the text state, shared by all questions…
NVIDIA Kumo Tabular Sets a New Accuracy-Efficiency Frontier for Tabular Prediction
NVIDIA Kumo Tabular Sets a New Accuracy-Efficiency Frontier for Tabular Prediction
Holo4: powering generalist computer-use agents
H company released Holo4, a family of open-weight agentic models for computer use across GUIs, code, MCP and APIs: Holo4-27B (dense), Holo4-35B-A3B (MoE) and Holotron4 Nano (30B-A3B). Holo4-27B scores 61.7% on OSWorld 2.0.
Muse Glimmer 开源：30B 压进消费级显卡，报错不停下
Meta 发布 Muse Glimmer：300 亿参数、Apache 2.0 开放权重，专为常驻本地的智能体工作流设计。4 位动态量化把 55GB 以上的显存需求压到 17 至 20GB，配合 DFlash 推测解码在 M5 Max 与 RTX 5090 上把吞吐提到最多 3.1 倍。真正值得注意的不是尺寸，而是它把「工具调用失败之后怎么办」写进了训练目标：API 或终端命令报错时，模型诊断故障并另找路径，而不是终止执行。
Qwen 3.8 27B is excellent, but it defaults to wildly overthinking things
Hands-on evaluation of Qwen 3.8 27B on a 128GB M5 Max and an NVIDIA DGX Spark: the shipped xhigh reasoning default burns tokens on trivial prompts, vision bounding boxes are strong, and throughput of 15–30 tokens per second is the real catch.
Qwen3.8-27B 开源：消费级显卡跑得动，尺子却是自己命名的
270 亿参数、原生多模态、262K 上下文可外推到 100 万，Apache 2.0，量化后一张 24GB 消费级显卡装得下——这一代 Qwen 把「本地跑得动的智能体」这条线又往前推了一格。榜单确实好看，但领先幅度最大的那一项跑在一把以厂商自己命名的基准上，而所有对比都由厂商自己完成。
State of Open Models: Summer 2026 Observations
Hugging Face's mid-2026 ecosystem report on the Hub: model size distribution by lab, licensing, derivative counts, download concentration, and the first measurement of agent traffic.
Meta 重回开放权重：一封长文、一个独立董事会，和一组本地部署的数字
Muse Glimmer 以 Apache 2.0 开放权重的同时，Meta 宣布重回开放路线：扎克伯格发表长文反对能力集中，公司设立独立董事会逐项审核每次模型发布，并披露了让本地部署成立的量化与推测解码数字。Muse Spark 1.2 的权重据称随后开放。
Meta is back with Muse Glimmer: local, agentic, multimodal, and open source
Meta released Muse Glimmer, a 30B multimodal model distilled from Muse under Apache 2.0, designed for local agentic use, with day-0 support across transformers, llama.cpp and vLLM.
BigBang-v1：把数据生产系统本身当成优化对象，以及一处对不上的口径
上海交大人工智能学院、深势科技与上海算法创新研究院组成的无尽前沿团队发布 BigBang-v1：35B 参数、推理时约 3B 激活、262K 上下文，后训练数据称 100% 由 AI 自主合成，Apache-2.0 开源。可复用的不是跑分，而是它对任务的两个硬条件——必须同时前沿且可验证；缺一，合成数据都会迅速贬值。附一条核对：报道称它是「首个 RSI 原生训练的基座模型」，而 Hugging Face 上的元数据把它标为 Qwen3.6-35B-A3B 的微调。
LFM2.5-Encoders for Fast Long-Context Inference on CPU
Two new encoder models, LFM2.5-Encoder-230M and 350M: they match larger encoders on GLUE, SuperGLUE and multilingual tasks, handle 8,192-token context, and run about 3.7x faster than ModernBERT-base on CPU at long context.
Kimi K3, and what we can still learn from the pelican benchmark
Moonshot AI announces Kimi K3, a 2.8-trillion-parameter flagship with an open-weight release promised by July 27 — the first 'open 3T-class' model. Third-party evaluation (Artificial Analysis) puts it behind only Claude Fable 5 on long-horizon knowledge work, at a cost per task comparable to GPT-5.6 Sol.
Welcome Inkling by Thinking Machines
Thinking Machines releases Inkling, an open-weight ~1T-parameter (41B active) Mixture-of-Experts multimodal model with a 1M context window, trained on 45T tokens of text, images, audio and video, with day-one support across Transformers, vLLM, SGLang, llama.cpp and Unsloth.