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dsh-think-translate

Model Inference Updated 2026.09.15

Run the following command in DeepSeek Harness:

dsh plugin install UncleK/dsh-think-translate

Paste the following prompt into your AI chat to install this plugin:

Run dsh plugin install UncleK/dsh-think-translate in your terminal to install the plugin; its source is available at https://github.com/UncleK/dsh-think-translate

About this plugin

When working with DeepSeek-class models, the reasoning chain is often emitted in Chinese or whatever language the model happened to think in, leaving non-Chinese users unable to follow the model internal deliberation. dsh-think-translate is a client-side plugin for the DeepSeek Harness Web UI that translates thinking chains, task cards, and final answers into your chosen language in real time, purely at the display layer. Originals remain untouched in the transcript and the model context window; translated text never consumes a single token.

It supports 8 target languages and a single setting that applies globally and persists across reloads. The translation provider chain is drag-and-droppable: built-ins include Google GTX, Bing, and local Ollama; you can add any OpenAI-compatible or Anthropic Messages API endpoint; and providers already configured in DSH settings.yaml are auto-discovered with one click, their keys resolved from credentials at request time and never written to plugin config. Paragraph- and sentence-aware chunking keeps quality high even with small local models, and a 3x backoff retry policy ensures resilience. File paths, shell commands, URLs, regex patterns, and pure code lines are never translated.

Built for developers and researchers who want to read a model reasoning in their own language, teams deploying DeepSeek Harness in non-Chinese locales, and privacy-conscious users who prefer fully offline translation. Local Ollama auto-downloads and enables the model on first selection, so nothing leaves the machine.

Screenshots

Use Cases

  • Need live English subtitles while following a DeepSeek model reasoning in Chinese
  • Sharing Harness transcripts in a non-Chinese team so everyone can read the model thought process
  • Translating reasoning chains fully offline with a local Ollama model, keeping data on-machine

Best For

  • Developers and researchers using DeepSeek Harness daily
  • Engineers deploying model inference in globally distributed teams
  • Privacy-conscious users who prefer fully local offline pipelines