This repository provides Python code for running inference on the MPT-30B model using only a CPU, targeting users who want to leverage large language models without requiring expensive GPUs. It utilizes a ggml quantized model and the ctransformers Python library for efficient CPU execution.

How It Works

The project leverages ggml, a C library for machine learning that enables efficient tensor operations on CPUs. By using a ggml quantized version of the MPT-30B model, the memory footprint and computational requirements are significantly reduced, making it feasible to run on consumer-grade hardware. The ctransformers library provides Python bindings to ggml, simplifying the integration and inference process.

Quick Start & Requirements

• Install dependencies: pip install -r requirements.txt
• Download model weights: python download_model.py
• Run inference: python inference.py
• Prerequisites: Minimum 32GB RAM, Python 3.10 recommended. Docker is also recommended for easier setup.

Highlighted Details

• Enables MPT-30B inference on CPU.
• Uses ggml quantized model weights (approx. 19GB download).
• Relies on the ctransformers Python library.

Maintenance & Community

No specific information on contributors, sponsorships, or community channels is provided in the README.

Licensing & Compatibility

The repository does not explicitly state a license. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The project requires a substantial amount of RAM (32GB minimum). Performance benchmarks or comparisons to GPU inference are not yet available.