polyglot-gpu 1.0.5

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Local GPU translation Python library + MCP server — TranslateGemma via Ollama, 57 languages, zero cloud dependency.

Python port of polyglot-mcp. Use as a pip-installable library for your Python projects or as an MCP server for Claude Code, Claude Desktop, and other MCP clients.

Features

• 57 languages — TranslateGemma via Ollama, running 100% locally on your GPU
• Zero cloud dependency — no API keys, no internet required after model download
• Dual-use — Python library API + MCP server in one package
• Markdown-aware — preserves code blocks, tables, HTML, URLs, badges
• Smart caching — segment-level cache with fuzzy matching (translation memory)
• Software glossary — 12 built-in tech terms for accurate translations
• Auto-everything — auto-starts Ollama, auto-pulls models on first use
• GPU-safe — semaphore-controlled concurrency prevents VRAM overload
• Production-hardened — connection pooling, structured logging, 100 tests

Requirements

• Python >= 3.10
• Ollama installed locally
• GPU with sufficient VRAM for your chosen model:
  • translategemma:4b — 3.3 GB (fast, good quality)
  • translategemma:12b — 8.1 GB (balanced, recommended)
  • translategemma:27b — 17 GB (slow, best quality)

Install

pip install polyglot-gpu

Library Usage

import asyncio
from pypolyglot import translate, translate_markdown, translate_all

async def main():
    # Simple translation
    result = await translate("Hello world", "en", "ja")
    print(result.translation)  # こんにちは世界

    # Markdown translation (preserves structure)
    md = "## Features\n\nLocal GPU translation with **zero cloud dependency**."
    result = await translate_markdown(md, "en", "fr")
    print(result.markdown)

    # Multi-language (7 languages at once)
    result = await translate_all(md, source_lang="en")
    for r in result.results:
        print(f"{r.name}: {r.status}")

asyncio.run(main())

Options

from pypolyglot import translate, TranslateOptions, GlossaryEntry

# Custom model
result = await translate("Hello", "en", "ja",
    TranslateOptions(model="translategemma:4b"))

# Custom glossary
result = await translate("Deploy the Widget", "en", "ja",
    TranslateOptions(glossary=[
        GlossaryEntry("Widget", {"ja": "ウィジェット"})
    ]))

# Streaming
result = await translate("Hello world", "en", "ja",
    TranslateOptions(on_token=lambda t: print(t, end="")))

MCP Server Usage

Claude Code

{
  "mcpServers": {
    "polyglot-gpu": {
      "command": "polyglot-gpu"
    }
  }
}

Or run directly:

python -m pypolyglot

MCP Tools

Architecture

MCP Client (Claude Code, etc.)
      │ MCP protocol (stdio)
      ▼
┌──────────────────┐
│   server.py      │  5 MCP tools (FastMCP)
├──────────────────┤
│  translate.py    │  Chunking, batching, streaming
│  markdown.py     │  Markdown segmentation
│  translate_all   │  Multi-language orchestrator
│  semaphore.py    │  GPU-safe concurrency
│  validate.py     │  Output validation
├──────────────────┤
│   ollama.py      │  httpx pooled client → Ollama
│   cache.py       │  Segment cache + fuzzy memory
│  glossary.py     │  Software term dictionary
│ languages.py     │  57 language definitions
│   polish.py      │  Post-translation cleanup
│   errors.py      │  Structured error class
└──────────────────┘
      │ HTTP (httpx)
      ▼
   Ollama + TranslateGemma (GPU)

Environment Variables

Security

• All translation runs locally — zero data leaves your machine
• No telemetry, no API keys, no cloud dependency
• See SECURITY.md for threat model

License

MIT

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