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Fine-Tune Pipeline

Welcome to the Fine-Tune Pipeline documentation! This is a comprehensive fine-tuning pipeline for language models that includes training, inference, and evaluation capabilities.

🚀 Features

  • Easy Configuration: TOML-based configuration system for all components
  • Modern Architecture: Built with Unsloth, Transformers, and TRL for efficient fine-tuning
  • Comprehensive Evaluation: Multiple evaluation metrics including BLEU, ROUGE, and semantic similarity
  • CI/CD Ready: Designed to work seamlessly with GitHub Actions and Jenkins
  • Flexible Inference: Built-in inference capabilities with customizable parameters
  • Weights & Biases Integration: Automatic logging and experiment tracking

🏗️ Architecture

The pipeline consists of three main components:

1. Fine-Tuner (app/finetuner.py)

  • Handles model fine-tuning using LoRA (Low-Rank Adaptation)
  • Supports 4-bit and 8-bit quantization for memory efficiency
  • Integrates with Weights & Biases for experiment tracking
  • Automatic model publishing to Hugging Face Hub

2. Inferencer (app/inferencer.py)

  • Performs inference on test datasets
  • Configurable generation parameters
  • Supports both local and Hub models
  • Outputs results in JSONL format

3. Evaluator (app/evaluator.py)

  • Comprehensive evaluation suite with multiple metrics
  • Support for both traditional (BLEU, ROUGE) and LLM-based evaluation
  • Detailed reporting with Excel and JSON outputs
  • Semantic similarity and factual correctness evaluation

🛠️ Technology Stack

  • Unsloth: Efficient fine-tuning framework
  • Transformers: Hugging Face Transformers library
  • TRL: Transformer Reinforcement Learning
  • Datasets: Hugging Face Datasets library
  • Weights & Biases: Experiment tracking and logging
  • RAGAS: Retrieval Augmented Generation Assessment

📊 Supported Models

The pipeline supports various model architectures including:

  • Qwen 2.5 series
  • Llama models
  • Mistral models
  • And any model compatible with Unsloth

📈 Evaluation Metrics

Built-in support for:

  • BLEU Score: Translation quality assessment
  • ROUGE Score: Summarization evaluation
  • Factual Correctness: LLM-based factual evaluation
  • Semantic Similarity: Embedding-based similarity
  • Answer Accuracy: Custom accuracy metrics
  • Answer Relevancy: Relevance assessment

🔧 Quick Start

Ready to get started? Check out our Quick Start Guide to begin fine-tuning your first model!

📚 What's Next?

This documentation is automatically generated and maintained. For issues or contributions, please visit our GitHub repository.