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dsh-hybrid-coder

客户端 更新于 2026.08.25

在终端中运行以下命令:

dsh plugin install jackiesre721/dsh-hybrid-coder

将以下提示词粘贴到 DeepSeek Harness 对话框中:

在 DeepSeek Harness 中运行命令 dsh plugin install jackiesre721/dsh-hybrid-coder 即可安装该插件,源码位于 https://github.com/jackiesre721/dsh-hybrid-coder 。

插件介绍

A local small model (say, Qwen running through Ollama) is lightweight and free for day-to-day coding, but it struggles the moment a task demands multi-step planning or a streak of failed tool calls. dsh-hybrid-coder is built for exactly that gap: it sits as a routing-policy layer that lets the local model handle routine steps while delegating planning and rescue to a premium model (DeepSeek, GLM, etc.), switching automatically between the two based on explicit escalation and de-escalation rules.

The core mechanism operates on three routing priorities: plan mode always routes to premium, routine steps go to local, and an escalation latch keeps everything on premium until enough clean premium steps accumulate. When the local model's consecutive tool failures hit a configurable threshold, the plugin escalates, injects a bounded guidance message carrying the recent failure trajectory, and lets the premium model take over. Transport-level failures such as Ollama not running or a refused connection trigger an immediate request-level fallback to premium. All routing state is persisted as session events, so fork, resume, and process restarts recover identical decisions with no in-process state to lose.

Ideal for DeepSeek Harness users who run a local inference engine like Ollama and want to keep daily coding costs low without giving up the safety net of a frontier model. The plugin is experimental; public contracts (event names, config fields, guidance text) may change before the first tagged release.

使用场景

  • 本地小模型执行日常编码,遇到复杂规划自动切换高端模型
  • 本地模型连续工具调用失败后自动升级至高端模型救援
  • 高端模型连续成功处理若干步骤后自动降级回本地模型

适合人员

  • 使用 Ollama 等本地推理引擎的 DeepSeek Harness 用户
  • 希望降低日常编码成本同时保留高端模型兜底的开发者
  • 需要多模型协同且关注路由状态持久化的高级用户