modelspoke

English | 中文

A plugin for DeepSeek Harness for managing connections to local, OpenAI-compatible model servers

Features

First-class llama-swap and Ollama support

The router llama-swap is highly recommended for local model hoarders since it can serve as a source of truth for model capabilities across harnesses. modelspoke understands the capability data that llama-swap adds to its extended OpenAI-compatible endpoint. Capability discovery also supports Ollama's API extensions.

Presets for common models

To help out with endpoints that don't have full capability discovery (e.g. llama-server, vLLM, sglang) modelspoke includes a table of capabilities for common base models to use in initial configuration.

Full-featured setup UI

Setup is easy to use and covers all the day-to-day fields (the deep template-contract fields — compat — stay hand-edited in the file). Allows for overriding presets and discovered capabilities and maintaining multiple setting profiles of the same underlying model.

Reasoning effort levels

dsh custom provider functionality doesn't afford any way to set reasoning effort. Modelspoke can discover the supported effort levels and allows customizing the map from the UI effort setting to the model supported setting.

Image input

Models with multimodal capabilities are great but if you add them via the dsh custom provider setup that functionality is not available. Modelspoke can discover image input capability or allow you to specify it. It also includes a fix for the lack of upstream support for inline images in session chat.

Installation & setup

dsh

modelspoke is a dual-face Cordis package: the node half registers the LLM adapter + the modelspoke: settings section; the client half (same package) renders the Modelspoke settings section and a first-run onboarding step in dsh's web UI. One plugin row mounts both halves.

1. Bundle it into your dsh profile. Add the package as a dependency in the profile's package.json (pnpm link: to a checkout for local dev), plus a dsh.profile.bundles entry, then pnpm install in the profile directory:

   {
     "dependencies": {
       "modelspoke": "^0.1.0"
     },
     "dsh": {
       "profile": {
         "bundles": ["@deepseek-ai/dsh-base", "modelspoke"]
       }
     }
   }

   The package carries no config row of its own — dsh.cordis.yml holds only the single plugin row name: 'modelspoke', which boots dormant (zero routes) until a modelspoke: section exists.

2. Restart dsh once after (de)install (dsh caches package metadata for the process life). After that, content changes in a dev checkout are HMR-only — no restart needed.

3. Open the Modelspoke settings page. In the dsh web UI, the gear at the bottom of the left rail opens Settings; select modelspoke in the sidebar, then + Add provider:

   

4. Point it at your server. Set the provider's fields — name, base URL, the environment variable name holding the API key (omit for keyless local servers — no auth header is sent in that case), and an optional default effort (minimal … max; a default the model doesn't offer degrades to the nearest offered level; a per-request effort it doesn't offer is refused, never silently clamped) — then commit with the card's Apply button. The row's status dot goes green once the model fetch succeeds.

5. Configure per model where you want to. Expanding a provider fetches its model list; each model row has a chevron that opens an editable detail (context window, max output tokens, the thinking-level map, nothink, image input, reasoning effort):

   

   The model list is the curation — a model is addressable by the agent only while it is in the list; clearing a detail field releases that field back down the resolution chain.

Appendix

• docs/usage.md — using modelspoke after install: the resolution chain, the per-model detail, nothink models, images, and the settings.yaml shape
• docs/preset-authoring.md — authoring a model preset from the template in the artifact (the preset-draft / drift-check workflow)
• docs/llama-swap-setup.md — the minimal llama-swap setup, and how modelspoke reads llama-swap's extended endpoint
• docs/design.md — architecture and decisions
• docs/provider-details.md — the provider reference: why the five backends, where each capability value comes from, per-provider quirks
• docs/dsh-plugin-guidance.md — integrating with dsh: the adapter registration contract, the web-UI half, settings writes, and the read_image tool-view workaround