AI translation

The AI Translation feature lets you automatically generate translations for the open TS file using large language models (LLMs). You can use either a local LLM server (such as Ollama, LM Studio, or llama.cpp) or cloud-based APIs that support the OpenAI-compatible REST protocol (such as OpenAI, Groq, or Anthropic).

Setting up a local LLM server

To use AI Translation with a local server, install one of the following and download at least one model:

• Ollama - Easy to use, manages models automatically
• LM Studio - GUI application with model browser
• llama.cpp - Lightweight, runs GGUF models directly

For Ollama, pull a model using the command line:

ollama pull qwen3:14b
ollama serve

For LM Studio, download models through the application's interface and start the local server:

lms server start

For llama.cpp, you can either use one of the built-in model presets:

llama-server --fim-qwen-7b-default

Or download a GGUF model file and start the server manually:

llama-server -m model.gguf --port 8080

Using cloud APIs

To use cloud-based translation services, select OpenAI Compatible as the API type, enter the provider's API endpoint URL, and provide your API key. The API key field accepts authentication tokens for services like OpenAI, Anthropic, Groq, and other OpenAI-compatible providers.

Using the AI Translation dialog

In Linguist, choose Tools > AI Translation to open the AI Translation dialog:

The dialog provides:

• API Type: choose between Ollama for local Ollama servers or OpenAI Compatible for LM Studio, llama.cpp, or cloud APIs.
• Server URL: the REST endpoint where the server listens (default http://localhost:11434 for Ollama, http://localhost:8080 for OpenAI Compatible).
• API Key: authentication key for cloud APIs (optional for local servers).
• Model: drop-down list of available models.
• Context: optional application context to improve translation accuracy (e.g., "medical software", "video game", "financial application").
• File: the TS file to translate.
• Filter: limit translation to specific groups (contexts or labels).
• Translate: start the AI translation.
• Apply Translations: apply the translated items into the TS file.

During translation, progress messages appear in the Translation Log. When complete, review the translated texts in the log. Click Apply Translations to insert the AI-generated translations into the TS file.

Recommended models

The following models are recommended for translation tasks, balancing quality, speed, and resource usage. These models can be found on:

• Ollama - search by model name (e.g., ollama pull qwen3:14b)
• LM Studio - search in the model browser
• Hugging Face - download GGUF files for llama.cpp

For systems with limited resources, smaller variants like Qwen3 8B (5 GB) or Qwen2.5 7B (5 GB) provide reasonable translation quality while requiring less memory.