Hi @mehrdad-dev
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

$ litellm --model ollama/llama2 --api_base http://localhost:11434

Hugging Face Models

$ export HUGGINGFACE_API_KEY=my-api-key #[OPTIONAL]
$ litellm --model claude-instant-1

Anthropic

$ export ANTHROPIC_API_KEY=my-api-key
$ litellm --model claude-instant-1

Palm

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

Using to run an eval on lm harness:

python3 -m lm_eval \
  --model openai-completions \
  --model_args engine=davinci \
  --task crows_pairs_english_age

a) GPT-4 emerged as the top-performing chatbot across all categories, achieving a success rate of 84.1%. On the other hand, Bard faced challenges and achieved a success rate of 62.4%. b) Among the four models evaluated, one of them responded correctly approximately 93% of the time. However, all models were correct only about 44%.

These statements don't make logical sense