Qwen3.8-27B-pi-GGUF

模型介绍

**Qwen3.8-27B-pi** builds on Qwen3.8-27B for coding work in the Pi agent harness. It is tailored to the loop of reading a repository, editing files, running tools, and responding to their feedback. The aim is more completed work with less generated text, while retaining the base model's familiar interface. Fine-tuned for the Pi agent harness, the model is designed to work through coding tasks as an iterative process: inspect the code, make a change, check the result, and use tool feedback to guide the next step. Adjustable reasoning effort lets you balance responsiveness with deeper problem-solving, from focused edits to more involved debugging and implementation work. - **Built for Pi:** Fine-tuned for the everyday coding loop—reading repositories, editing files, running tools, and working through feedback in the Pi harness—with an emphasis on turning plans into working, checked implementations. - **Curated coding experience:** Supervised fine-tuning on filtered, successful Pi sessions helps the model learn complete coding workflows, rather than isolated answers or code snippets, including how to adapt to existing environments and check results against task requirements. - **Task quality matters:** Development also included reviewing, repairing, and exploring more demanding coding tasks—with a focus on clear requirements and checks that distinguish working solutions from incorrect ones. - **Refined through reinforcement learning:** A second training stage pairs verified task outcomes with a custom reasoning-efficiency reward built on GRPO, encouraging successful low- and medium-effort solutions to reason more economically while leaving xhigh focused on correctness. - **Available in practical formats:** Choose a deployment format that fits your hardware, including GGUF quantizations calibrated using complete Pi coding sessions.

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

同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价，不将渠道价格视为厂商官方统一价格。

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：apache-2.0。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。