ahczhg/LFM2-ColBERT-350M-inference

LFM2-ColBERT-350M Inference Example

This repository demonstrates local GPU inference using the LiquidAI/LFM2-ColBERT-350M model for document retrieval and ranking tasks.

Overview

The LFM2-ColBERT-350M is a neural retrieval model that uses contextualized embeddings to rank documents based on their relevance to queries. This project provides a complete example of:

• Loading the model and tokenizer
• Processing queries and documents
• Computing similarity scores
• Ranking documents by relevance

Requirements

• Python 3.7+
• PyTorch
• Transformers
• scikit-learn
• CUDA-capable GPU (recommended)

Installation

Install the required dependencies:

pip install transformers torch scikit-learn

Usage

The Jupyter notebook demonstrates a complete workflow:

1. Install Dependencies: Installs transformers and torch
2. Load Model: Loads the LFM2-ColBERT-350M model from Hugging Face
3. Prepare Data: Creates example queries and documents
4. Generate Embeddings: Computes embeddings for queries and documents
5. Rank Results: Uses cosine similarity to rank documents by relevance

Quick Start

Open the LFM2-ColBERT-350M.ipynb notebook in Jupyter and run all cells. The example demonstrates:

queries = [
    "What is the capital of France?",
    "Tell me about machine learning.",
    "How to train a neural network?"
]

documents = [
    "Paris is the capital and most populous city of France.",
    "Machine learning is a field of artificial intelligence...",
    # More documents...
]

The model successfully ranks relevant documents higher for each query.

Results

The example shows effective document ranking with high similarity scores for relevant query-document pairs:

• Query about France's capital correctly ranks Paris-related documents highest
• Machine learning queries prioritize ML-related content
• Neural network training queries rank technical documents first

Model Information

• Model: LiquidAI/LFM2-ColBERT-350M
• Task: Document retrieval and ranking
• Parameters: 350M
• Architecture: ColBERT-style retrieval model

Next Steps

• Evaluate on larger, more diverse datasets
• Compare performance with other retrieval models
• Fine-tune on domain-specific data
• Implement batch processing for larger document collections

License

MIT License - see LICENSE file for details

Acknowledgments

• Model developed by LiquidAI
• Built using Hugging Face Transformers

Issues

If you encounter any problems or have questions:

• Check the model repository for model-specific issues
• Open an issue in this repository for implementation questions