# Using LiteLLM as Backend

Lighteval allows you to use LiteLLM as a backend, enabling you to call all LLM APIs
using the OpenAI format. LiteLLM supports various providers including Bedrock, Hugging Face, Vertex AI, Together AI, Azure,
OpenAI, Groq, and many others.

> [!TIP]
> Documentation for available APIs and compatible endpoints can be found [here](https://docs.litellm.ai/docs/).

## Basic Usage

```bash
lighteval endpoint litellm \
    "provider=openai,model_name=gpt-3.5-turbo" \
    gsm8k
```

## Using a Configuration File

LiteLLM allows generation with any OpenAI-compatible endpoint. For example, you
can evaluate a model running on a local VLLM server.

To do so, you will need to use a configuration file like this:

```yaml
model_parameters:
    model_name: "openai/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"
    base_url: "URL_OF_THE_ENDPOINT_YOU_WANT_TO_USE"
    api_key: "" # Remove or keep empty as needed
    generation_parameters:
      temperature: 0.5
      max_new_tokens: 256
      stop_tokens: [""]
      top_p: 0.9
      seed: 0
      repetition_penalty: 1.0
      frequency_penalty: 0.0
```

## Supported Providers

LiteLLM supports a wide range of LLM providers:

### Cloud Providers

all cloud providers can be found in the [litellm documentation](https://docs.litellm.ai/docs/providers).

### Local/On-Premise
- **VLLM**: Local VLLM servers
- **Hugging Face**: Local Hugging Face models
- **Custom endpoints**: Any OpenAI-compatible API

## Using with Local Models

### VLLM Server
To use with a local VLLM server:

1. Start your VLLM server:
```bash
vllm serve HuggingFaceH4/zephyr-7b-beta --host 0.0.0.0 --port 8000
```

2. Configure LiteLLM to use the local server:
```yaml
model_parameters:
    provider: "openai"
    model_name: "HuggingFaceH4/zephyr-7b-beta"
    base_url: "http://localhost:8000/v1"
    api_key: ""
```

For more detailed error handling and debugging, refer to the [LiteLLM documentation](https://docs.litellm.ai/docs/).


<EditOnGithub source="https://github.com/huggingface/lighteval/blob/main/docs/source/use-litellm-as-backend.mdx" />