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#端侧

今日 3 条
今天10月2日周五
  1. Chubby♨️45

    webAI 发布 3.66B 参数形式逻辑模型 TwIL-LM3-Pro,可在笔记本本地运行。其综合逻辑评测与 Qwen3-8B 持平,参数量不足后者一半,并在全部六项形式逻辑任务上领先 VibeThinker-3B。该模型基于 IBM Granite 4.2 后训练,Q4 GGUF 权重仅 2.09 GiB,可通过 llama.cpp 本地推理。

    引用David Stout@Davidstout

    Half a million downloads in a month. Today, our open source family takes another step forward. Thank you for the incredible support behind our first-generation models. We’re excited to introduce TwIL-LM3-Pro. At just 3.6 billion parameters, it brings powerful reasoning to everyday computers, with quantized builds that run locally. No cloud required. In our evaluation: Formal logic: Highest recorded headline score among the small models compared—beating China’s VibeThinker-3B by 35% and Qwen3.5-4B by 24%, and Liquid AI’s LFM2.5-8B-A1B by 47%. Broader reasoning: 95% on SVAMP and 64.1% on MuSR, the highest recorded scores among the small models compared. BIG-Bench Hard’s logic subset: 95.4%, compared with VibeThinker-3B’s 61.1%. We believe AI is entering a post-training era. The advantage will increasingly belong to companies with the best pipelines and those that can produce capable, personalized intelligence faster and more efficiently, then put it on devices people already own. That’s what we’re building at webAI. And we’re only beginning to share what’s coming out of our lab. Coming soon: Meridian, our family of frontier-class models built to run on device. Our most advanced models will be available through the @thewebAI application. Join the waitlist as we expand access. Proudly built in Austin, Texas. 🇺🇸

  2. elvis48

    webAI 开源 3.6B 参数模型 TwIL-LM3-Pro,可在普通电脑本地运行,BIG-Bench Hard 得分 95.4,远超 Qwen3-8B 的 63.7。其训练配方为:形式逻辑微调后将权重合并回基座模型,再用程序化验证器做 RL,逻辑分数提升且通用推理保持稳定。

    引用David Stout@Davidstout

    Half a million downloads in a month. Today, our open source family takes another step forward. Thank you for the incredible support behind our first-generation models. We’re excited to introduce TwIL-LM3-Pro. At just 3.6 billion parameters, it brings powerful reasoning to everyday computers, with quantized builds that run locally. No cloud required. In our evaluation: Formal logic: Highest recorded headline score among the small models compared—beating China’s VibeThinker-3B by 35% and Qwen3.5-4B by 24%, and Liquid AI’s LFM2.5-8B-A1B by 47%. Broader reasoning: 95% on SVAMP and 64.1% on MuSR, the highest recorded scores among the small models compared. BIG-Bench Hard’s logic subset: 95.4%, compared with VibeThinker-3B’s 61.1%. We believe AI is entering a post-training era. The advantage will increasingly belong to companies with the best pipelines and those that can produce capable, personalized intelligence faster and more efficiently, then put it on devices people already own. That’s what we’re building at webAI. And we’re only beginning to share what’s coming out of our lab. Coming soon: Meridian, our family of frontier-class models built to run on device. Our most advanced models will be available through the @thewebAI application. Join the waitlist as we expand access. Proudly built in Austin, Texas. 🇺🇸

9月28日周一
9月24日周四
8月21日周五
  1. Hugging Face Blog65

    Liquid AI 发布 LFM2.5-DSpark 草稿模型,推理吞吐最高提升 3.2x

    Liquid AI 为 LFM2.5-1.2B-Instruct、LFM2.5-2.6B 和 LFM2.5-8B-A1B 三个模型发布 DSpark 草稿模型 checkpoint,通过投机解码在不改变输出质量的前提下加速推理,GPU 吞吐最高提升 3.18x,端侧最高 2.87x。

    推荐理由:官方为 LFM2.5 三款模型发布 DSpark 草稿模型,给出从 H100 到 MacBook 的实测加速数据和开源接入方式。

8月10日周一
  1. Hugging Face Blog81

    Meta 发布开源多模态模型 Muse Glimmer-30B,主打本地智能体场景

    Meta 发布从 Muse 蒸馏而来的 30B 多模态模型 Muse Glimmer,采用 Apache 2.0 许可,面向本地隐私场景的智能体用途。模型由 2B ViT 视觉编码器和 28B 文本解码器组成,支持图像、视频、多模态工具调用和目标检测,并附带基于 DFlash 的可选投机解码。

    推荐理由:原文给出架构组成、基准对比和各推理框架的 day-0 用法,读者可以据此评估本地部署的可行路径。

6月27日周六
12月3日周三
  1. Mistral AI77

    Mistral 发布 Mistral 3 系列模型,含 Mistral Large 3 与 Ministral 3,均以 Apache 2.0 开源

    Mistral 发布新一代模型系列 Mistral 3,包括 14B/8B/3B 的 Ministral 3 小模型和 sparse MoE 架构的 Mistral Large 3(41B 激活、675B 总参数),全部以 Apache 2.0 许可开源,提供 base、instruct 和 reasoning 变体。

    推荐理由:官方公布完整模型规格、开源许可和部署路径,读者可据此评估在自建或端侧场景的可用性。