🎅 I WISH LITELLM HAD...

This is a ticket to track a wishlist of items you wish LiteLLM had.

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[LiteLLM Client] Add new models via UI

Thinking aloud it seems intuitive that you'd be able to add new models / remap completion calls to different models via UI. Unsure on real problem though.

User / API Access Management

Different users have access to different models. It'd be helpful if there was a way to maybe leverage the BudgetManager to gate access. E.g. GPT-4 is expensive, i don't want to expose that to my free users but i do want my paid users to be able to use it.

cc: @yujonglee @WilliamEspegren @zakhar-kogan @ishaan-jaff @PhucTranThanh feel free to add any requests / ideas here.

[ishaan-jaff]

[Spend Dashboard] View analytics for spend per llm and per user

• This allows me to see what my most expensive llms are and what users are using litellm heavily

[ishaan-jaff]

Auto select the best LLM for a given task

If it's a simple task like responding to "hello" litlellm should auto-select a cheaper but faster llm like j2-light

[Pipboyguy]

Integration with NLP Cloud

That's awesome @Pipboyguy - dm'ing on linkedin to learn more!

@ishaan-jaff check out this truncate param in the cohere api

This looks super interesting. Similar to your token trimmer. If the prompt exceeds context window, trim in a particular manner.

I would maybe only run trimming on user/assistant messages. Not touch the system prompt (works for RAG scenarios as well).

[haseeb-heaven]

Option to use Inference API so we can use any model from Hugging Face 🤗

@haseeb-heaven you can already do this -

litellm/litellm/llms/huggingface_restapi.py

Line 53 in a63784d

completion_url = f"https://api-inference.huggingface.co/models/{model}"

from litellm import completion 
response = completion(model="huggingface/gpt2", messages=[{"role": "user", "content": "Hey, how's it going?"}])
print(response) 

[haseeb-heaven]

@haseeb-heaven you can already do this -

litellm/litellm/llms/huggingface_restapi.py

Line 53 in a63784d

completion_url = f"https://api-inference.huggingface.co/models/{model}"

from litellm import completion 
response = completion(model="huggingface/gpt2", messages=[{"role": "user", "content": "Hey, how's it going?"}])
print(response) 

Wow great thanks its working. Nice feature

[smig23]

Support for inferencing using models hosted on Petals swarms (https://github.com/bigscience-workshop/petals), both public and private.