liteLLM: A Proxy Server for Large Language Models

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LitellM

Enhancing User Experience with LitellM: A Proxy Server for Large Language Models

Large language models (LLMs) are becoming increasingly popular for a variety of tasks, such as natural language generation, machine translation, and question answering. However, LLMs can be expensive to use, and they can also be difficult to set up and maintain.

liteLLM is a proxy server that makes it easy to use LLMs. It supports over 50 LLMs, including those from Azure, OpenAI, Replicate, Anthropic, and Hugging Face. liteLLM also provides a unified input/output format, which makes it easy to switch between different LLMs.

In addition to making it easy to use LLMs, liteLLM also provides a number of other features, such as:

  • Model fallback: liteLLM can automatically fallback to a different LLM if the first LLM fails. This is useful for ensuring that requests are always successful, even if one LLM is unavailable.
  • Logging support: liteLLM supports a variety of logging backends, such as Supabase, Posthog, Mixpanel, Sentry, and Helicone. This makes it easy to track the usage of LLMs and identify any potential problems.
  • Token usage tracking: liteLLM tracks the number of tokens used by each LLM. This information can be used to manage LLM budgets and prevent overage charges.
  • Semantic caching: liteLLM implements semantic caching, which means that it only caches the results of requests that are semantically equivalent. This reduces the amount of data that needs to be stored in the cache, and it also improves performance.
  • Streaming and asynchronous support: liteLLM supports streaming and asynchronous requests. This makes it possible to use LLMs for tasks that require a large amount of data, such as machine translation.

liteLLM is a powerful tool that makes it easy to use LLMs. It is a valuable resource for anyone who needs to use LLMs for their work.

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