lengquan88/dsh-dual-auto
dsh-dual-auto
Dual-model auto-routing plugin for DeepSeek Harness: low-cost direct / high-cost upgrade + escape-learning closed loop
Install
npx -y @deepseek-ai/dsh plugin --profile web add @lengquan88/dsh-dual-autoRestart `dsh web` after install. Bundle APIs can change during the developer preview.
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From the README
Excerpt from lengquan88/dsh-dual-auto, cleaned of badges and images.
dsh-dual-auto
Dual-model auto-routing plugin for the DeepSeek Harness (dsh).
Low-cost direct / high-cost upgrade with an escape-learning closed loop.
Install
pnpm add @lengquan88/dsh-dual-auto
Enable
Add one row to your profile's cordis.patch.yml:
- insert:
- id: dual-auto
name: '@lengquan88/dsh-dual-auto'
Restart dsh web. The tools dual_model_route, dual_model_run, and
dual_model_mark become available in every session.
Tools
| Tool | Purpose |
|---|---|
dual_model_route | Six-criteria routing decision (length / context / domain coverage / rule conflict / confidence / novelty → six labels). Fingerprints that escaped once are force-upgraded. |
dual_model_run | Decision + real model call: direct → deepseek-v4-flash, upgrade → deepseek-v4-pro (auto-degrade to flash on failure, marked degraded). Probe tasks auto-validate against a gold set — wrong direct answers trigger escape learning. |
dual_model_mark | Mark the quality of a direct result. correct=false learns the fingerprint and rewrites the disk log marker; the same fingerprint is force-upgraded next time. |
Persistence
State persists to output/dsh_router_{fingerprints,stats}.json and
dsh_router_decision_log.jsonl — interoperable with the project's Python
dao/model_router.py (v2 dict fingerprints load directly).
Links
- npm: https://www.npmjs.com/package/@lengquan88/dsh-dual-auto
- Source mirror (atomgit): https://atomgit.com/guaikepa/zhonghua/tree/main/dsh-dual-auto
License
MIT
