Lightning-AI/litgpt

Features

• Large Language Model Training Frameworks - Provides a comprehensive toolkit for pretraining, finetuning, and deploying large language models using optimized recipes.
• Instruction Tuning - Provides efficient recipes and pipelines for adapting pre-trained models to follow specific user instructions.
• Model Inference Servers - Provides a dedicated server application to host quantized large language models via web interfaces for external apps.
• Model Quantization Tools - Includes utilities to reduce the precision of model weights, decreasing memory usage and increasing inference speed.
• Model Fine-Tuning and Adaptation - Includes workflows for refining pre-trained models to meet specific domain or task requirements.
• Model Fine-Tuning - Implements specialized parameter techniques to adapt large models to specific tasks and datasets.
• Model Pretraining Frameworks - Provides a framework for the large-scale unsupervised training of foundation models from scratch.
• Finetuning Workflows - Adapts pretrained foundation models to specific tasks or industry datasets using supervised or unsupervised tuning processes.
• Model Quantization - Provides tools to reduce the precision of model weights to decrease memory footprint and increase inference speed.
• Language Model Pretraining - Trains foundation language models from scratch or using inherited blocks to establish core knowledge on massive datasets.
• Parameter Efficient Fine-Tuning - Implements memory-efficient adaptation techniques that update only a small subset of parameters to reduce resource requirements.
• Weight Quantization - Compresses model weights into lower-precision formats to decrease memory footprint and accelerate inference on constrained hardware.
• ML Model Hosting - Hosts models on web servers to provide a network-accessible interface for external websites and applications.
• Dataset Integration - Provides interfaces for integrating industry-standard datasets or custom user-provided data sources into training pipelines.
• Model Performance Benchmarking - Includes a benchmarking suite to measure the generation quality and accuracy of language models.
• Custom Model Architectures - Enables the implementation of high-performance custom model structures to maximize execution speed and minimize abstraction.
• Inference Optimization Tools - Applies hardware-specific quantization and memory optimizations to improve the performance of production model inference.
• Hardware-Aware Selection - Optimizes model execution by automatically selecting memory and precision settings based on target hardware specifications.
• Post-Training Configuration Recipes - Uses predefined configuration recipes to standardize the pretraining and finetuning processes across different model architectures.
• Weight Inheritance - Allows new models to reuse trained transformer layers from existing architectures to reduce pretraining data and compute.
• Model Inference Deployment - Supports the setup of production environments capable of serving high volumes of inferences to end users.
• LLM Production Infrastructure - Manages the deployment, scaling, and reliability of optimized large language models in production environments.
• LLM Evaluation - Provides a benchmarking toolset for testing the generation quality and understanding of models against standardized datasets.
• Interactive Model Interfaces - Ships a chat interface for manually verifying model responses and extracting embeddings for analysis.
• Fine-Tuning Frameworks - Library for pretraining and fine-tuning using advanced techniques.
• Model Training - High-performance recipes for pretraining and fine-tuning LLMs.
• Model Training and Fine-tuning - Fast training and fine-tuning for LLMs.
• Model Training Frameworks - High-performance framework for pretraining and fine-tuning LLMs.
• Fine-Tuning Frameworks - Toolkit for pretraining and fine-tuning with state-of-the-art techniques.

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