Ansonfishing/dsh-model-manager

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A control panel for your local LLM inference servers — a plugin for DSH (DeepSeek Harness).

Why

Run a couple of local inference servers (llama.cpp / SGLang / vLLM) and the parameter knowledge scatters across terminal history and notes: which -c / -np does this model want? Does the VRAM actually fit? Which framework boots on this card?

With this plugin, the answers live in one panel:

• Before: digging through shell history, hand-calculating KV memory, learning about over-budget configs from an OOM.
• After: a service registry that shows who runs on which card in real time, parameter profiles validated for VRAM before you save, one-click tok/s benchmarks.

Quick start

Prerequisites: DSH (with web) + pnpm; GPU detection needs python3 (falls back to nvidia-smi when missing).

cd ~/.dsh/profiles/web                      # your DSH web profile directory
pnpm add github:Ansonfishing/dsh-model-manager

Then add "dsh-model-manager" to the dsh.profile.bundles array in package.json and restart dsh. A "Model Manager" tab appears in the conversation view — done.

Features

• Service registry — register local inference servers (port, framework, model, GPU) with live health checks and one-click stop.
• Parameter profiles — named parameter versions per "model × framework × GPU" for llama.cpp / SGLang / vLLM. Rows follow a fixed 9-group order (model → context → KV → speculative decoding → sampling → performance → parallelism → server → misc); different quantizations of the same model × framework share one union row set with aligned rows — flags unset in the current version show as grey "other quant: value" rows, so cross-quant differences pop out. Recommended values resolve through a 3-tier provenance chain (same-quant live value → sibling quant → official best practice), labelled per cell; nothing is guessed.
• GPU detection — auto-enumerates cards (libcuda primary, nvidia-smi fallback); card index = the CUDA_VISIBLE_DEVICES value.
• VRAM validation — estimates KV memory from -c / -np when you save a profile and warns immediately if the target card can't hold it.
• One-click benchmark — sends a fixed prompt (non-streaming, 256 tokens) to a running server and records tok/s; a full-context hot benchmark additionally verifies the usable context is at least the target, then runs streaming warmup + measurement (TTFB / prefill / decode).
• Safety rails — never uses pkill; stopping an external service requires explicit force plus a two-click confirmation in the panel; stopping port 11437 (DSH's own inference port) additionally warns that it will interrupt the current session.

No DSH? Take a look anyway

Clone this repo and open test/harness/index.html in a browser — a zero-dependency render harness that shows the full panel with mock data; ?chrome=0 hides the harness bar.

Local GPU table (optional)

The repo ships no hardcoded GPU mappings. To show card names and memory capacity, create ~/.dsh/model-manager/builtin-gpus.local.json:

{
  "0": { "name": "RTX 4090", "memGb": 24 },
  "1": { "name": "RTX 6000 Ada", "memGb": 48 }
}

Without the file the panel falls back to "GPU 0 / GPU 1" and skips VRAM validation.

Development

npm test                          # node --test test/*.test.mjs
node build/build-client.cjs       # rebuild lib/client.js from mockup-v3.html

Local development: after cloning, use pnpm add link:../path/to/dsh-model-manager in your profile. Client-only changes need a browser refresh; Node-side changes (index.js / lib/*.js) need a dsh restart.

Architecture at a glance

Layer	File	Responsibility
Entry	index.js	registers 14 tools + 17 same-origin routes
Lifecycle	lib/lifecycle.js	registry CRUD, health probes, managed start/stop
Safety	lib/safety.js	stop policy (fuser/kill), protected ports
Validation	lib/validate.js	launchCommand parsing + rule engine
GPU	lib/gpu.js	detection + local GPU table loading
Adapters	lib/adapters/*.js	llama / sglang / vllm command assembly
Client	lib/client.js	single-file React component (built from the mockup)

License

MIT © Ansonfishing