# meta-llama/llama-models

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7,643 stars · 1,391 forks · Python · NOASSERTION

## Links

- GitHub: https://github.com/meta-llama/llama-models
- awesome-repositories: https://awesome-repositories.com/repository/meta-llama-llama-models.md

## Description

This project provides a foundational framework and reference implementation for executing causal language modeling and multimodal reasoning on local systems. It includes a set of core components for managing model assets, a fine-tuning framework, and structural definitions required to instantiate transformer-based architectures.

The system is distinguished by its ability to process combined text and image inputs through multimodal transformer models for visual reasoning and document analysis. It also supports the deployment of quantized models, reducing memory footprints through low-precision techniques to enable inference on edge devices.

The project covers broad capability areas including supervised fine-tuning and low-rank adaptation for domain customization, as well as a comprehensive asset manager for downloading, verifying, and organizing model weights and tokenizers. Additional functionality encompasses multilingual text generation, long context processing, and visual language grounding.

## Tags

### Artificial Intelligence & ML

- [LLM Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/llm-implementations.md) — Provides a foundational framework and reference implementation for executing causal language modeling and multimodal reasoning on local systems.
- [Model Weight Management](https://awesome-repositories.com/f/artificial-intelligence-ml/model-weight-management.md) — Ships a comprehensive command-line utility for downloading, verifying, and removing large language model weight files. ([source](https://github.com/meta-llama/llama-models#readme))
- [Causal Language Modeling](https://awesome-repositories.com/f/artificial-intelligence-ml/text-generation-strategies/token-prediction/causal-language-modeling.md) — Implements transformer architectures that predict the next token in a sequence for natural language generation.
- [Inference Execution](https://awesome-repositories.com/f/artificial-intelligence-ml/inference-execution.md) — Performs chat completion and text generation tasks on local hardware using optimized inference scripts. ([source](https://github.com/meta-llama/llama-models#readme))
- [Joint Embedding Spaces](https://awesome-repositories.com/f/artificial-intelligence-ml/joint-embedding-spaces.md) — Maps text and images into a unified vector space to enable joint reasoning and analysis.
- [Large Language Model Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/large-language-model-fine-tuning.md) — Customizes pretrained large language model weights for specific tasks or new languages.
- [LLM Fine-Tuning Toolsets](https://awesome-repositories.com/f/artificial-intelligence-ml/llm-fine-tuning-toolsets.md) — Provides a set of tools for supervised fine-tuning and parameter-efficient updates like LoRA for language models.
- [Local Model Lifecycle Management](https://awesome-repositories.com/f/artificial-intelligence-ml/local-model-lifecycle-management.md) — Manages the installation, cataloging, and removal of AI model files on a local system.
- [Quantized LLM Deployments](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-deployment-and-serving/local-and-on-device-inference/edge-ai-model-deployment/generative-ai-models/quantized-llm-deployments.md) — Provides a system for reducing model memory footprints through low-precision quantization to enable inference on edge devices.
- [Quantized Model Deployments](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-deployment-and-serving/local-and-on-device-inference/edge-ai-model-deployment/quantized-model-deployments.md) — Enables running large language models on compute-limited edge devices using low-precision weight quantization.
- [Model Inference](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-inference-serving/model-integration-pipelines/model-inference.md) — Enables running causal language modeling and multimodal reasoning architectures locally using various model checkpoints. ([source](https://github.com/meta-llama/llama-models#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) — Specializes base models by training a small subset of adapter weights using low-rank adaptation.
- [Model Asset Managers](https://awesome-repositories.com/f/artificial-intelligence-ml/model-asset-managers.md) — Provides a command-line utility to download, verify, and organize local model weights and tokenizers.
- [Model Metadata Inspection](https://awesome-repositories.com/f/artificial-intelligence-ml/model-metadata-inspection.md) — Allows inspection of model structural properties, available versions, and required prompt formats. ([source](https://github.com/meta-llama/llama-models#readme))
- [Model Quantization](https://awesome-repositories.com/f/artificial-intelligence-ml/model-quantization.md) — Applies low-precision techniques to weights to reduce memory footprint and increase processing speed. ([source](https://github.com/meta-llama/llama-models#readme))
- [Multimodal Visual Reasoning](https://awesome-repositories.com/f/artificial-intelligence-ml/multimodal-visual-reasoning.md) — Analyzes combined text and image inputs to perform visual recognition, document interpretation, and captioning.
- [Model-Specific Prompt Formats](https://awesome-repositories.com/f/artificial-intelligence-ml/prompt-formatting/model-specific-prompt-formats.md) — Identifies and displays the specific input structures and chat templates required for different model architectures. ([source](https://github.com/meta-llama/llama-models#readme))
- [Multimodal Completion Engines](https://awesome-repositories.com/f/artificial-intelligence-ml/prompt-formatting/model-specific-prompt-formats/multimodal-input-tuples/multimodal-completion-engines.md) — Generates text responses by simultaneously processing combined image and text inputs. ([source](https://github.com/meta-llama/llama-models/blob/main/pyproject.toml))
- [Weight Quantization](https://awesome-repositories.com/f/artificial-intelligence-ml/quantized-inference-runtimes/weight-quantization.md) — Compresses model weights into lower-precision formats to reduce memory footprint and accelerate inference.
- [Supervised Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/supervised-fine-tuning.md) — Adapts pretrained models to specific domains using labeled instruction datasets and supervised learning. ([source](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/MODEL_CARD.md))
- [Text Generation APIs](https://awesome-repositories.com/f/artificial-intelligence-ml/text-generation-apis.md) — Provides interfaces for interacting with large language models to produce text, chat, and code completions. ([source](https://github.com/meta-llama/llama-models#readme))
- [Visual-Language Multimodal Integration](https://awesome-repositories.com/f/artificial-intelligence-ml/visual-language-multimodal-integration.md) — Integrates visual and textual data streams into a shared embedding space to enable cross-modal reasoning.
- [Conversational Agent Development](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-development-environments/conversational-agent-development.md) — Supports the development of agents optimized for multi-turn conversational interaction and human dialogue.
- [Architecture Definitions](https://awesome-repositories.com/f/artificial-intelligence-ml/architecture-definitions.md) — Provides the structural definitions and reference implementations required to instantiate complex transformer-based architectures. ([source](https://github.com/meta-llama/llama-models#readme))
- [Conversational AI Models](https://awesome-repositories.com/f/artificial-intelligence-ml/conversational-ai-models.md) — Generates natural dialogue responses by processing conversation history through large language models. ([source](https://github.com/meta-llama/llama-models/blob/main/pyproject.toml))
- [Generative Text Inference](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/generative-ai/generative-text-inference.md) — Loads weights and tokenizers to produce text responses from user prompts using local hardware. ([source](https://github.com/meta-llama/llama-models#readme))
- [Multilingual Text Generation](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/generative-ai/generative-text-inference/multilingual-text-generation.md) — Produces text and code in multiple languages to support global communication needs. ([source](https://github.com/meta-llama/llama-models/blob/main/models/llama4/MODEL_CARD.md))
- [Generative Text Inference](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-text-inference.md) — Produces coherent natural language text completions by processing input prompts through defined architectures. ([source](https://github.com/meta-llama/llama-models#readme))
- [KV Cache Management](https://awesome-repositories.com/f/artificial-intelligence-ml/kv-cache-management.md) — Optimizes inference efficiency by storing and retrieving key-value pairs in transformer models.
- [Llama Model Inference](https://awesome-repositories.com/f/artificial-intelligence-ml/llama-model-inference.md) — Executes Llama-family causal language modeling and multimodal reasoning on local systems. ([source](https://github.com/meta-llama/llama-models#readme))
- [Long Context Processing](https://awesome-repositories.com/f/artificial-intelligence-ml/long-context-processing.md) — Handles large volumes of input text in single requests to maintain coherence across extended documents. ([source](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md))
- [Supervised Instruction Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/fine-tuning-and-alignment/supervised-instruction-fine-tuning.md) — Refines base model weights through supervised fine-tuning to align generated responses with safety and helpfulness guidelines.
- [Edge AI Model Deployment](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-deployment-and-serving/local-and-on-device-inference/edge-ai-model-deployment.md) — Optimizes and deploys quantized large language models to run efficiently on resource-constrained edge devices. ([source](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/MODEL_CARD.md))
- [Multimodal Model Runners](https://awesome-repositories.com/f/artificial-intelligence-ml/multimodal-models/multimodal-model-runners.md) — Loads and runs models that process text alongside image inputs for visual reasoning and document analysis. ([source](https://github.com/meta-llama/llama-models#readme))
- [Document Layout Analysis](https://awesome-repositories.com/f/artificial-intelligence-ml/natural-language-processing/document-layout-analysis.md) — Interprets the spatial organization and text of documents to enable visual reasoning and question answering. ([source](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/MODEL_CARD_VISION.md))
- [Supervised Instruction Learning](https://awesome-repositories.com/f/artificial-intelligence-ml/supervised-instruction-learning.md) — Refines model outputs to align with safety and helpfulness guidelines using supervised instruction learning.
- [Transformer Architecture Implementation](https://awesome-repositories.com/f/artificial-intelligence-ml/transformer-architecture-implementation.md) — Executes causal transformer architectures using stacked attention layers to predict subsequent tokens.
- [Vision-Language Grounding Models](https://awesome-repositories.com/f/artificial-intelligence-ml/vision-language-grounding-models.md) — Maps natural language descriptions to specific objects or spatial regions within an image. ([source](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/MODEL_CARD_VISION.md))

### Part of an Awesome List

- [Image Captioning](https://awesome-repositories.com/f/awesome-lists/ai/image-captioning.md) — Analyzes visual scenes to generate descriptive text summaries using multimodal transformer models. ([source](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/MODEL_CARD_VISION.md))
- [Vision Language Models](https://awesome-repositories.com/f/awesome-lists/ai/vision-language-models.md) — Multimodal extension of a text-only model using a vision adapter.

### DevOps & Infrastructure

- [Agentic Dialogue Orchestration](https://awesome-repositories.com/f/devops-infrastructure/automation-orchestration/task-execution-frameworks/task-job-management/task-schedulers/agent-task-managers/conversational-task-wrappers/agentic-dialogue-orchestration.md) — Coordinates assistant-like dialogue and knowledge retrieval to complete complex agentic workflows. ([source](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/MODEL_CARD.md))

### Operating Systems & Systems Programming

- [Inference Precision Optimization](https://awesome-repositories.com/f/operating-systems-systems-programming/memory-footprint-reduction/inference-precision-optimization.md) — Lowers GPU memory requirements by using mixed precision for weights during inference. ([source](https://github.com/meta-llama/llama-models#readme))