Ornith-1.5-35B-A3B-MTP-APEX-GGUF

模型介绍

Ornith-1.5-35B-A3B-MTP-APEX English | 📖 中文文档 Self-improving agentic coding model · APEX quantized GGUFs + BF16 + mmproj Ornith-1.5-35B-A3B is a self-improving agentic coding model from the Ornith Team . It extends Ornith-1.0 by expanding the self-improvement loop from scaffold and rollout optimization to jointly optimizing task generation, scaffold construction, and solution rollouts — continuously generating new training tasks, discovering effective strategies, and improving the policy through reinforcement learning. Activating only ~3B parameters per token, it significantly outperforms its similar-sized peer Qwen3.6-35B across all coding and agentic benchmarks: Terminal-Bench 2.1 67.8 , SWE-bench Verified 79 , SWE-bench Pro 59.6 , SWE-bench Multilingual 71.4 , NL2Repo 46.2 , MCP-Atlas 70.2 , ClawEval 72.5 . This GGUF package includes the mmproj-BF16.gguf vision projector for multimodal (image + text) capabilities with llama.cpp. Unlike Ornith-1.0, the MTP layer is native to the model — no external grafting required. License: MIT. Architecture Qwen3.5 MoE (Mixture of Experts) Parameters 35B total, 3B active per token Experts 256 routed experts, 8 active per token Layers 40 transformer layers + 1 MTP layer Context 262,144 tokens MTP 1 native MTP layer (785 tensors) License MIT

主要特点

特点摘要待依据来源补齐，不凭模型名称推断能力。

局限与注意事项

暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。

来源报价与费用分析

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公开评测与运行配置

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

常见问题

资料来源与可追溯记录

许可证：mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。