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  • Qwen3. 5-27B-Claude-4. 6-Opus-Reasoning-Distilled-v2-GGUF
    We’re on a journey to advance and democratize artificial intelligence through open source and open science
  • Claude-Opus-4. 6 蒸馏 Qwen3. 5 V2 来了 - 知乎
    现在 v2 来了,这次的升级重点不是"更准",而是"更快更省"——同样的准确率,思维链缩短了 24%,每个 Token 产出的正确答案多了 31 6%。 部署条件没变,Qwen3 5-27B 4bit 单张 4090 即可本地跑起
  • GitHub - QwenLM Qwen3. 5: Qwen3. 5 is the large language model series . . .
    Welcome to the GitHub repository of Qwen3 5 Here, you can find official information about Qwen3 5 (User Guide, coming soon), post your questions (Issues), and share your ideas with the community (Discussions)
  • Qwen3. 5-27B · Models
    Qwen3 5 Highlights Qwen3 5 features the following enhancement: Unified Vision-Language Foundation: Early fusion training on multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding benchmarks
  • Qwen3. 5: Towards Native Multimodal Agents
    As a native vision-language model, Qwen3 5-397B-A17B demonstrates outstanding results across a full range of benchmark evaluations, including reasoning, coding, agent capabilities, and multimodal understanding, empowering developers and enterprises to achieve significantly greater productivity
  • Qwen3. 5 - 本地运行指南 | Unsloth Documentation
    在你的本地设备上运行新的 Qwen3 5 LLM,包括中型:Qwen3 5-35B-A3B、27B、122B-A10B,小型:Qwen3 5-0 8B、2B、4B、9B 和 397B-A17B!
  • qwen3. 5:9b - ollama. com
    Qwen 3 5 is a family of open-source multimodal models that delivers exceptional utility and performance vision tools thinking cloud 0 8b 2b 4b 9b 27b 35b 122b ollama run qwen3 5:9b Details
  • qwen qwen3. 5-35b-a3b • LM Studio
    Qwen3 5 is a reasoning vision-language model that supports tool use With 35B total parameters and 3B activated, it outperforms previous generation models more than 6x its size
  • Claude-Opus-4. 6 蒸馏 Qwen3. 5 V2 来了|通用|正确率 . . . - 网易
    大家好,我是Ai学习的老章 Claude-Opus-4 6 蒸馏 Qwen3 5 我一直很关注: 现在 v2 来了,这次的升级重点不是"更准",而是"更快更省"——同样的准确率,思维链缩短了 24%,每个 Token 产出的正确答案多了 31 6%。 部署条件没变,Qwen3 5-27B 4bit 单张 4090 即可本地跑起 v2 到底改了啥? 先看核心数据: 指标 v1 v2
  • Qwen3. 5-27B-Claude-4. 6-0pus- Reasoning-Distilled-v2-GGUF
    v2 was trained with 14,000 Claude 4 6 Opus-style general reasoning samples 有没有佬试一下这个模型的效果,看介绍是蒸了 opus 4 6,蒸蒸日上啊





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