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It is a simple Python package that makes it easier to run large language models (LLMs) on your own machines using non-public data (possibly behind corporate firewalls). It is intended to help integrate local LLMs into practical applications. | It is the interface between your app and hosted LLMs. It streamlines API requests to OpenAI, Anthropic, Mistral, LLama2, Anyscale, Google Gemini, and more with a unified API. |
Run large language models on-premises;
Inspired largely by the privateGPT;
Intended to help integrate local LLMs into practical applications | Blazing fast (9.9x faster) with a tiny footprint (~45kb installed);
Load balance across multiple models, providers, and keys;
Fallbacks make sure your app stays resilient;
Automatic Retries with exponential fallbacks come by default;
Plug-in middleware as needed;
Battle-tested over 100B tokens |
Statistics | |
GitHub Stars 799 | GitHub Stars 9.8K |
GitHub Forks 50 | GitHub Forks 775 |
Stacks 0 | Stacks 2 |
Followers 5 | Followers 4 |
Votes 0 | Votes 0 |
Integrations | |

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It is a framework built around LLMs. It can be used for chatbots, generative question-answering, summarization, and much more. The core idea of the library is that we can “chain” together different components to create more advanced use cases around LLMs.

It allows you to run open-source large language models, such as Llama 2, locally.

It is a project that provides a central interface to connect your LLMs with external data. It offers you a comprehensive toolset trading off cost and performance.