<2-3 sentences summarising what the project addresses and solves, the target audience, and the benefit.> plamo-translate-cli provides a command-line interface for local text translation powered by the plamo-2-translate model. It targets developers and power users seeking to integrate private, on-device translation into scripts and workflows, offering an alternative to cloud-based services. The primary benefit is efficient, offline translation capabilities.

How It Works

<2-4 sentences on core approach / design (key algorithms, models, data flow, or architectural choices) and why this approach is advantageous or novel.> The CLI executes the plamo-2-translate model locally, with current optimization for macOS on Apple Silicon utilizing the MLX framework. This design prioritizes data privacy and enables offline translation by keeping all processing on the user's machine.

Quick Start & Requirements

• Installation: For Python < 3.13, use pip install plamo-translate. For Python >= 3.13, first install cmake via Homebrew (brew install cmake), then install sentencepiece from its GitHub repository (pip install git+https://github.com/google/sentencepiece.git@2734490#subdirectory=python), followed by pip install plamo-translate. uv package manager is also supported.
• Prerequisites: Python 3.10 or higher (with specific installation steps for 3.13), cmake, mcp[cli], numba. Optimized for macOS with Apple Silicon.
• Links: None provided.

Highlighted Details

• Supports translation for over 16 languages, including Japanese, English, Chinese, and Korean.
• Offers a server mode to reduce model loading times and enable integration with MCP clients like Claude Desktop.
• Allows users to select model weight precision (e.g., 4bit, 8bit, bf16) to balance performance and memory usage.

Maintenance & Community

The provided README does not contain information regarding notable contributors, community channels (like Discord/Slack), or a public roadmap.

Licensing & Compatibility

The README does not specify the software license or provide compatibility notes for commercial use or integration with closed-source projects.

Limitations & Caveats

<1-3 sentences on caveats: unsupported platforms, missing features, alpha status, known bugs, breaking changes, bus factor, deprecation, etc. Avoid vague non-statements and judgments.> The tool's primary optimization is for macOS with Apple Silicon using the MLX backend; support for other server backends like Ollama and vLLM is planned but not yet implemented. Installation on Python 3.13 requires a manual workaround for the sentencepiece dependency due to version incompatibilities on PyPI. The absence of explicit licensing information presents a potential adoption blocker.