hiyouga/LLaMA-Efficient-Tuning
LLaMA Efficient Tuning
This project is a fine-tuning framework and training pipeline designed to optimize and adapt large language and vision models. It provides a specialized toolkit for parameter-efficient tuning and supervised learning, serving as both a trainer for multimodal models and a deployment tool for serving fine-tuned models via high-performance inference engines.
The framework focuses on reducing memory and compute requirements by updating a small subset of model parameters. It supports a wide range of adaptation strategies, including vision-language model training to align text, image, video, and audio data, as well as preference alignment to match model behavior with human expectations.
The system covers a broad set of capabilities including supervised fine-tuning, instruction tuning, and core pre-training. It incorporates memory optimization through quantization and weight-merging pipelines, alongside data management for importing and preparing custom datasets. For operational management, it includes a web-based interface for task execution and integration with external dashboards for experiment metric tracking.
The project provides utilities for exporting model checkpoints and deploying tuned models as web services using standardized, OpenAI-compatible API interfaces.
Features
- LLM Fine-Tuning Engines - Serves as a specialized software engine optimized for the efficient fine-tuning of large language and vision models.
- Parameter Efficient Fine-Tuning - Provides a comprehensive framework for adapting large models by updating only a small subset of parameters to save compute.
- Preference Alignment - Combines supervised fine-tuning and reinforcement learning to align model outputs with human expectations.
- Language Model Fine-Tuning - Provides a comprehensive framework for adjusting pre-trained language models using memory-efficient tuning methods.
- Vision-Language Trainers - Provides a framework for training multimodal models to align and interpret text, image, video, and audio data.
- Model Inference and Serving - Serves fine-tuned models via APIs or user interfaces using high-performance inference engines.
- Model Training Pipelines - Provides end-to-end workflows for dataset preparation, model training, and performance validation.
- Model Fine-Tuning and Adaptation - Executes comprehensive training regimes to refine pre-trained models for specific domains and task requirements.
- Multimodal AI Pipelines - Provides pipelines to process and align text and visual data streams for cross-modal understanding in vision-language models.
- Multimodal Training - Offers workflows for training models on multiple modalities to achieve cross-modal understanding between text and video.
- Parameter-Efficient Training Toolkits - Includes a specialized toolkit for training only a small subset of parameters to reduce memory usage.
- Weight Quantization - Reduces precision of model weights to lower VRAM requirements and increase training throughput.
- Supervised Fine-Tuning - Updates pre-trained models using labeled instruction datasets to improve performance on specific tasks.
- Low-Rank Adaptation - Implements Low-Rank Adaptation (LoRA) to minimize trainable parameters and reduce memory requirements during fine-tuning.
- OpenAI-Compatible APIs - Provides a standardized interface for exposing tuned models as web services using OpenAI-compatible API endpoints.
- Core Pre-Training - Enables training a model from scratch or continuing training on a large corpus of unlabeled text.
- Reward Modeling - Provides capabilities to train reward models that score outputs to facilitate reinforcement learning and preference alignment.
- Inference Acceleration - Increases response generation speed from fine-tuned models through the use of optimized backends.
- Instruction Tuning - Adapts pre-trained models to follow specific user commands through supervised instruction tuning.
- Multimodal Training Interfaces - Implements interfaces to process mixed-modality datasets for extracting visual information from images and documents.
- Weight Merging Utilities - Combines learned adapter weights back into base model parameters to allow standalone inference without external modules.
- Data Ingestion and Preparation - Provides data pipelines to format and clean domain-specific knowledge for model ingestion.
- Preference-Based Model Alignments - Refines model behavior using human feedback and reinforcement learning to optimize for helpfulness.
- Fine-tuned Model Deployment - Streamlines the process of moving trained models and adapters into production environments.
- Multimodal Fine-Tuning - Adapts language and vision models using strategies like full-tuning and low-rank adaptation.
- Model Quantization - Utilizes quantization techniques to reduce the memory footprint and hardware requirements of large models.
- Model Exporting - Saves trained models in multiple formats to ensure compatibility with specific runtime environments.
- Preference Optimization - Adjusts model behavior using direct preference optimization algorithms to better match human expectations.
- Training Optimizations - Implements training efficiency improvements through specialized kernels, optimizers, and advanced data packing techniques.
- AI API Implementations - Serves fine-tuned models using a standardized API format for integration into external applications.
- Practice Projects - General-purpose framework for efficient model fine-tuning.
- Training Frameworks - PEFT-based framework for LLaMA and related model fine-tuning.
Star history
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