Mistral Finetune

This project is a language model finetuning framework designed to adapt large language models to specific datasets using supervised fine-tuning and low-rank adaptation. It serves as a distributed training manager that coordinates workloads and synchronizes gradients across multiple processing units to scale performance.

The framework includes a specialized toolkit for low-rank adaptation to update a subset of model weights, reducing memory and hardware requirements. It provides capabilities for instruction fine-tuning, domain adaptation, and the optimization of function calling to improve how models interact with external APIs.

The system covers the full training pipeline, including dataset processing for cleaning and validating conversational data, and an evaluation pipeline for tracking model accuracy. It also includes utilities for vocabulary extension to ensure compatibility between model checkpoints and tokenizers, and remote logging for real-time performance monitoring.

Features

Low-Rank Adaptation - Implements low-rank adaptation (LoRA) to efficiently tune a small subset of model weights.

Distributed Training Managers - Coordinates and scales language model training workloads across multiple compute nodes.

Distributed Gradient Synchronization - Implements mechanisms for coordinating gradient updates across multiple compute nodes during distributed training.

Instruction Fine-tuning - Implements methods for training language models to follow specific directions using structured instruction datasets.

LLM Evaluation Frameworks - Implements a monitoring system to track model accuracy and performance using systematic experiments and scorers.

LoRA Training - Uses low-rank adaptation to efficiently tune model weights while minimizing memory and hardware requirements.

Large Language Model Fine-Tuning Frameworks - Provides a comprehensive framework for adapting large language models using supervised fine-tuning and low-rank adaptation.

Distributed Training - Scales the training of large language models across multiple GPUs and processing units.

Model Training Pipelines - Manages end-to-end workflows for dataset preparation, training, and performance validation.

Dialogue Dataset Structuring - Converts raw conversational logs into structured schemas and templates required for instruction-following training.

LLM - Provides tools for cleaning, formatting, and validating raw text and conversational data.

Distributed Training Coordination - Coordinates training workloads across multiple processing units with device synchronization to scale computational performance.

Incremental Vocabulary Adaptation - Provides utilities to expand tokenizer vocabularies and adapt embedding layers without catastrophic forgetting.

Function Calling Interfaces - Trains models to accurately format tool calls and interact with external APIs via function calling interfaces.

LLM Domain Adaptations - Updates models with specific datasets to improve performance on niche topics or specialized knowledge.

Training Hyperparameter Configurations - Provides tools for adjusting specific training parameters like batch size and learning rate via configuration files.

Training Metric Streaming - Streams training progress and evaluation data to external dashboards for real-time performance tracking.

Model Performance Tracking - Implements a pipeline to track model accuracy and convergence by running test samples and logging metrics during training.

Model Training Metrics - Records diagnostic training metrics and evaluation data to external monitoring tools for performance analysis.

Experiment Tracking Dashboards - Streams training progress and evaluation data to remote dashboards for real-time performance visualization.

Fine-Tuning Frameworks - Lightweight codebase for memory-efficient model fine-tuning.

mistralaimistral-finetune

Features

Star history