Thank you for developing with Llama models. As part of the Llama 3.1 release, we’ve consolidated GitHub repos and added some additional repos as we’ve expanded Llama’s functionality into being an e2e Llama Stack. Please use the following repos going forward:

• llama-models - Central repo for the foundation models including basic utilities, model cards, license and use policies
• PurpleLlama - Key component of Llama Stack focusing on safety risks and inference time mitigations
• llama-toolchain - Model development (inference/fine-tuning/safety shields/synthetic data generation) interfaces and canonical implementations
• llama-agentic-system - E2E standalone Llama Stack system, along with opinionated underlying interface, that enables creation of agentic applications
• llama-cookbook - Community driven scripts and integrations

If you have any questions, please feel free to file an issue on any of the above repos and we will do our best to respond in a timely manner.

Thank you!

We are unlocking the power of large language models. Llama 2 is now accessible to individuals, creators, researchers, and businesses of all sizes so that they can experiment, innovate, and scale their ideas responsibly.

This release includes model weights and starting code for pre-trained and fine-tuned Llama language models — ranging from 7B to 70B parameters.

This repository is intended as a minimal example to load Llama 2 models and run inference. For more detailed examples leveraging Hugging Face, see llama-cookbook.

See UPDATES.md. Also for a running list of frequently asked questions, see here.

In order to download the model weights and tokenizer, please visit the Meta website and accept our License.

Once your request is approved, you will receive a signed URL over email. Then run the download.sh script, passing the URL provided when prompted to start the download.

Pre-requisites: Make sure you have wget and md5sum installed. Then run the script: