facebookresearch/llama-recipes

Features

• Large Language Model Fine-Tuning - Provides a comprehensive framework for adapting pre-trained Llama models to specific tasks using custom datasets.
• Llama Model Fine-Tuning - Provides a specialized framework for adapting pre-trained Llama models to specific datasets and domains.
• AI Model Integrations - Connects Llama models with external services and providers to extend them with custom workflows.
• AI Workflow Orchestration - Orchestrates discrete inference and retrieval steps into structured pipelines for complex tasks.
• AI Workflow Orchestrators - Provides a framework for managing and automating multi-step reasoning and operational sequences using LLMs.
• Retrieval-Augmented Generation - Combines external document retrieval with model inference to provide grounded, factual responses.
• LLM and VLM Inference Pipelines - Implements workflows for executing large language and vision models to generate outputs across various provider services.
• AI Workflow Pipelines - Connects models with external providers and services to build complete solutions for complex problems.
• Retrieval Augmented Generation - Implements a retrieval-augmented generation pipeline to ground model responses in external data sources.
• Llama Model Inference - Executes Llama large language models to generate text and vision outputs across different providers.
• Model Inference - Provides frameworks for loading models and generating text or vision outputs from prompts.
• Low-Rank Adaptation - Implements low-rank adaptation (LoRA) to reduce memory and compute requirements during fine-tuning.
• Parameter Efficient Fine-Tuning - Provides a toolkit for updating small subsets of model weights to enable efficient adaptation.
• Parameter-Efficient Training Toolkits - Ships a toolkit for memory-efficient model adaptation using techniques like LoRA to update small subsets of weights.
• Supervised Fine-Tuning - Implements training loops that process labeled prompt-response datasets to align model behavior.
• Model Fine-Tuning - Optimizes pre-trained models using specialized datasets to improve performance on target domains.
• Context Injection - Dynamically inserts retrieved data and system instructions into prompts to guide model output.
• Instruction Tuning Datasets - Provides tools and formats for preparing raw data into prompt-response pairs for model alignment.
• Prompt Templates - Uses structured text patterns to format user input and retrieved data for consistent model behavior.
• Provider-Agnostic Model Interfaces - Provides a standardized abstraction layer to switch between different cloud and local model hosting services.
• Semantic Document Retrieval - Queries external databases for relevant text chunks using semantic similarity to ground responses.
• RAG Implementation Guides - Provides practical implementation guides and patterns for combining model inference with external data retrieval to improve response accuracy.
• Large Language Models - Practical examples for fine-tuning and deploying Llama models.
• Large Language Models (LLMs) - Listed in the “Large Language Models (LLMs)” section of the The Incredible Pytorch awesome list.

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