# facebookresearch/llama-recipes

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18,379 stars · 2,744 forks · Jupyter Notebook · MIT

## Links

- GitHub: https://github.com/facebookresearch/llama-recipes
- Homepage: https://www.llama.com
- awesome-repositories: https://awesome-repositories.com/repository/facebookresearch-llama-recipes.md

## Description

This repository is a collection of frameworks and guides for Llama models, functioning as a fine-tuning framework, an inference pipeline, and an AI workflow orchestrator. It provides tools for adapting large language models to specific datasets and domains.

The project includes a parameter-efficient fine-tuning toolkit that utilizes techniques like low-rank adaptation to reduce memory and compute requirements. It also serves as an implementation guide for retrieval-augmented generation, combining model inference with external data retrieval to improve response accuracy.

The capability surface covers the creation of end-to-end AI workflows, supervised fine-tuning pipelines, and provider-agnostic inference. These tools enable the integration of models with external application services and the use of vector-based document retrieval for context injection.

## Tags

### Artificial Intelligence & ML

- [Large Language Model Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/large-language-model-fine-tuning.md) — Provides a comprehensive framework for adapting pre-trained Llama models to specific tasks using custom datasets.
- [Llama Model Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/llama-model-fine-tuning.md) — Provides a specialized framework for adapting pre-trained Llama models to specific datasets and domains.
- [AI Model Integrations](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-model-integrations.md) — Connects Llama models with external services and providers to extend them with custom workflows.
- [AI Workflow Orchestration](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-workflow-orchestration.md) — Orchestrates discrete inference and retrieval steps into structured pipelines for complex tasks.
- [AI Workflow Orchestrators](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-workflow-orchestrators.md) — Provides a framework for managing and automating multi-step reasoning and operational sequences using LLMs.
- [Retrieval-Augmented Generation](https://awesome-repositories.com/f/artificial-intelligence-ml/conversational-interfaces/retrieval-augmented-generation.md) — Combines external document retrieval with model inference to provide grounded, factual responses.
- [LLM and VLM Inference Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-training-frameworks/distributed-training/ray-based-training/ray-based-data-processing/llm-and-vlm-inference-pipelines.md) — Implements workflows for executing large language and vision models to generate outputs across various provider services.
- [AI Workflow Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-models/custom-generative-ai-model-building/ai-workflow-pipelines.md) — Connects models with external providers and services to build complete solutions for complex problems. ([source](https://github.com/facebookresearch/llama-recipes#readme))
- [Retrieval Augmented Generation](https://awesome-repositories.com/f/artificial-intelligence-ml/language-model-orchestration/retrieval-augmented-generation.md) — Implements a retrieval-augmented generation pipeline to ground model responses in external data sources. ([source](https://github.com/facebookresearch/llama-recipes#readme))
- [Llama Model Inference](https://awesome-repositories.com/f/artificial-intelligence-ml/llama-model-inference.md) — Executes Llama large language models to generate text and vision outputs across different providers.
- [Model Inference](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-inference-serving/model-integration-pipelines/model-inference.md) — Provides frameworks for loading models and generating text or vision outputs from prompts. ([source](https://github.com/facebookresearch/llama-recipes#readme))
- [Low-Rank Adaptation](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/fine-tuning-and-customization/model-fine-tuning/low-rank-adaptation.md) — Implements low-rank adaptation (LoRA) to reduce memory and compute requirements during fine-tuning.
- [Parameter Efficient Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/parameter-efficient-fine-tuning.md) — Provides a toolkit for updating small subsets of model weights to enable efficient adaptation.
- [Parameter-Efficient Training Toolkits](https://awesome-repositories.com/f/artificial-intelligence-ml/parameter-efficient-training-toolkits.md) — Ships a toolkit for memory-efficient model adaptation using techniques like LoRA to update small subsets of weights.
- [Supervised Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/supervised-fine-tuning.md) — Implements training loops that process labeled prompt-response datasets to align model behavior.
- [Context Injection](https://awesome-repositories.com/f/artificial-intelligence-ml/context-injection.md) — Dynamically inserts retrieved data and system instructions into prompts to guide model output.
- [Instruction Tuning Datasets](https://awesome-repositories.com/f/artificial-intelligence-ml/instruction-tuning-datasets.md) — Provides tools and formats for preparing raw data into prompt-response pairs for model alignment.
- [Prompt Templates](https://awesome-repositories.com/f/artificial-intelligence-ml/instructional-prompting/prompt-templates.md) — Uses structured text patterns to format user input and retrieved data for consistent model behavior.
- [Provider-Agnostic Model Interfaces](https://awesome-repositories.com/f/artificial-intelligence-ml/provider-agnostic-model-interfaces.md) — Provides a standardized abstraction layer to switch between different cloud and local model hosting services.

### Part of an Awesome List

- [Model Fine-Tuning](https://awesome-repositories.com/f/awesome-lists/ai/model-training-and-fine-tuning/model-fine-tuning.md) — Optimizes pre-trained models using specialized datasets to improve performance on target domains. ([source](https://github.com/facebookresearch/llama-recipes#readme))
- [Large Language Models](https://awesome-repositories.com/f/awesome-lists/ai/large-language-models.md) — Practical examples for fine-tuning and deploying Llama models.
- [Large Language Models (LLMs)](https://awesome-repositories.com/f/awesome-lists/more/large-language-models-llms.md) — Listed in the “Large Language Models (LLMs)” section of the The Incredible Pytorch awesome list.

### Data & Databases

- [Semantic Document Retrieval](https://awesome-repositories.com/f/data-databases/database-management-systems/database-engines/vector-databases/vector-document-indexing/semantic-document-retrieval.md) — Queries external databases for relevant text chunks using semantic similarity to ground responses.

### Education & Learning Resources

- [RAG Implementation Guides](https://awesome-repositories.com/f/education-learning-resources/rag-implementation-guides.md) — Provides practical implementation guides and patterns for combining model inference with external data retrieval to improve response accuracy.