Integrate with LiteLLM - Evaluate 100+LLMs, 92% faster

Hi @xudafeng @jglee2046
I'm the maintainer of LiteLLM. we allow you to create a proxy server to call 100+ LLMs to make it easier to run benchmark / evals

**I'm making this issue because I believe LiteLLM makes it easier for you to run benchmarks and evaluate LLMs (I'd love your feedback if it does not)**

Try it here: https://docs.litellm.ai/docs/simple_proxy
https://github.com/BerriAI/litellm

## Using LiteLLM Proxy Server
## Creating a proxy server
Ollama models
```shell
$ litellm --model ollama/llama2 --api_base http://localhost:11434
```

Hugging Face Models
```shell
$ export HUGGINGFACE_API_KEY=my-api-key #[OPTIONAL]
$ litellm --model claude-instant-1
```

Anthropic
```shell
$ export ANTHROPIC_API_KEY=my-api-key
$ litellm --model claude-instant-1
```
Palm

```shell
$ export PALM_API_KEY=my-palm-key
$ litellm --model palm/chat-bison
```

## Set api base to proxy
```
openai.api_base = "http://0.0.0.0:8000"
```

## Using to run an eval on lm harness:
```python
python3 -m lm_eval \
--model openai-completions \
--model_args engine=davinci \
--task crows_pairs_english_age

```

The issue proposes using LiteLLM as a proxy for benchmark and evaluation runs, but names no repository file, test, entry point, or acceptance criteria. Start by reviewing the LiteLLM proxy documentation and the repository's evaluation entry points; completion would require maintainers to define the integration scope and how it should be validated.

Written by the indexing model from the issue text.