dsh-llm-sampling
DeepSeek Harness 的精确模型采样策略包
插件会安装到这里；不确定时保持 web。
npx -y @deepseek-ai/dsh plugin --profile web add github:kuma-loong/dsh-llm-sampling#5da04d9b3401678a188d8b318fdf43d7de1b8932Llm Sampling 以 dsh-llm-sampling 发布，当前版本为 0.1.0-rc.1。Plugin Hub 会校验它的 manifest，并保存精确安装来源，便于复现安装结果。
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Installable DeepSeek Harness bundle that enforces sampling policy for exact provider/model routes through the agent/request waterfall. The Harness core owns the provider-neutral request fields and durable request header; adapters own wire translation. This plugin owns deployment policy only.
The plugin is dormant until llm-sampling.providers names a route. A configured model's default profile replaces all sampling values on every request. When reasoningEffort is explicitly off, off overlays that complete profile. Unconfigured routes pass through unchanged.
The plugin requires a DeepSeek Harness build whose LlmCallConfig and GenerateOptions include topP, topK, minP, presencePenalty, and repetitionPenalty. Until that core change reaches an npm release, install the plugin only with a matching Harness source checkout.
Adapters must map the configured fields. @deepseek-ai/dsh-llm-pi-ai supports the extended fields for OpenAI Chat Completions and rejects them for other protocols.
Pin the reviewed commit when installing from GitHub:
dsh plugin --profile web add github:kuma-loong/dsh-llm-sampling#<commit>
Git installs run this package's prepare script. pnpm 10 and later require an explicit build allowance in the profile's pnpm-workspace.yaml:
allowBuilds:
dsh-llm-sampling@https://codeload.github.com/kuma-loong/dsh-llm-sampling/tar.gz/<commit>: true
Copy the exact key printed by pnpm, then re-run the dsh plugin add command. Grant this permission only after reviewing the pinned source because prepare executes on the host during installation.
Add an llm-sampling section to $DSH_HOME/settings.yaml:
llm-sampling:
providers:
sparse-vllm:
models:
Qwen3.8-27B:
default:
temperature: 1
topP: 0.95
topK: 20
minP: 0
presencePenalty: 0
repetitionPenalty: 1
off:
temperature: 0.7
topP: 0.8
presencePenalty: 1.5
Supported fields are temperature, topP, topK, minP, presencePenalty, and repetitionPenalty. Profiles are policy, not caller defaults: configured values win over earlier agent/request proposals. A later request policy may deliberately replace them through the normal waterfall order.
No prompt text or tool schema is added. The model receives the configured sampling values in its provider request, and the effective values are recorded in the session's request/header before dispatch.
The plugin adds no tokens. Sampling changes generation distribution and may change output length.
No prompt prefix changes. Providers may include sampling controls in request-cache identity, so a policy or reasoning-mode change can affect provider-side reuse even with identical input tokens.
off profile is selected only for an explicit reasoningEffort: off; an omitted effort preserves the default profile because provider-owned implicit reasoning state is not guessed.