The meta-llama organization on GitHub features a wide range of public repositories primarily focused on Python, Jupyter Notebook, TypeScript, and Shell. Notable projects include the inference code for Llama models and the official Meta Llama 3 site, along with resources like the Llama Cookbook that assist developers in working with Llama models.
Top languages
Public repositories
llama
Inference code for Llama models
llama3
The official Meta Llama 3 GitHub site
llama-cookbook
Welcome to the Llama Cookbook! This is your go to guide for Building with Llama: Getting started with Inference, Fine-Tuning, RAG. We also show you how to solve end to end problems using Llama model family and using them on various provider services
codellama
Inference code for CodeLlama models
llama-models
Utilities intended for use with Llama models.
PurpleLlama
Set of tools to assess and improve LLM security.
synthetic-data-kit
Tool for generating high quality Synthetic datasets
prompt-ops
An open-source tool for LLM prompt optimization.
llama-api-python
The official Python library for the Llama API
llama-api-typescript
The official Typescript library for the Llama API
llama-verifications
Functional tests and benchmarks for verifying Llama model providers.
llama-stack-ops
Ops files for https//github.com/meta-llama/llama-stack
Frequently asked questions
What does meta-llama build on GitHub?
Meta-llama builds various projects related to Llama models, including inference code and utilities. Their repositories serve as resources for developers, featuring tools like the Llama Cookbook that guide users in building and fine-tuning models.
Which programming languages does meta-llama use?
Meta-llama primarily utilizes Python, Jupyter Notebook, TypeScript, and Shell for their projects. This diverse selection of languages allows the organization to create robust tools and libraries for working with Llama models.
Are meta-llama's repositories public?
Yes, all repositories under the meta-llama organization are public. This transparency enables the community to access, contribute to, and learn from the various projects focused on Llama models and related technologies.
Is this exposure intended?
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