Huggingface

LiteLLM supports Huggingface Inference Endpoints that uses the text-generation-inference format.

API KEYS

import os 
os.environ["HUGGINGFACE_API_KEY"] = ""

Models with Prompt Formatting

For models with special prompt templates (e.g. Llama2), we format the prompt to fit their template.

What if we don't support a model you need? You can also specify you're own custom prompt formatting, in case we don't have your model covered yet.

Does this mean you have to specify a prompt for all models? No. By default we'll concatenate your message content to make a prompt.

Default Prompt Template

def default_pt(messages):
    return " ".join(message["content"] for message in messages)

Code for how prompt formats work in LiteLLM

Models with Special Prompt Templates

Model Name	Works for Models	Function Call	Required OS Variables
meta-llama/Llama-2-7b-chat	All meta-llama llama2 chat models	`completion(model='huggingface/meta-llama/Llama-2-7b', messages=messages, api_base="your_api_endpoint")`	`os.environ['HUGGINGFACE_API_KEY']`
tiiuae/falcon-7b-instruct	All falcon instruct models	`completion(model='huggingface/tiiuae/falcon-7b-instruct', messages=messages, api_base="your_api_endpoint")`	`os.environ['HUGGINGFACE_API_KEY']`
mosaicml/mpt-7b-chat	All mpt chat models	`completion(model='huggingface/mosaicml/mpt-7b-chat', messages=messages, api_base="your_api_endpoint")`	`os.environ['HUGGINGFACE_API_KEY']`
codellama/CodeLlama-34b-Instruct-hf	All codellama instruct models	`completion(model='huggingface/codellama/CodeLlama-34b-Instruct-hf', messages=messages, api_base="your_api_endpoint")`	`os.environ['HUGGINGFACE_API_KEY']`
WizardLM/WizardCoder-Python-34B-V1.0	All wizardcoder models	`completion(model='huggingface/WizardLM/WizardCoder-Python-34B-V1.0', messages=messages, api_base="your_api_endpoint")`	`os.environ['HUGGINGFACE_API_KEY']`
Phind/Phind-CodeLlama-34B-v2	All phind-codellama models	`completion(model='huggingface/Phind/Phind-CodeLlama-34B-v2', messages=messages, api_base="your_api_endpoint")`	`os.environ['HUGGINGFACE_API_KEY']`

Custom prompt templates

# Create your own custom prompt template works 
litellm.register_prompt_template(
    model="togethercomputer/LLaMA-2-7B-32K",
    role_dict={
            "system": {
                "pre_message": "[INST] <<SYS>>\n",
                "post_message": "\n<</SYS>>\n [/INST]\n"
            },
            "user": { 
                "pre_message": "[INST] ",
                "post_message": " [/INST]\n"
            }, 
            "assistant": {
                "pre_message": "\n",
                "post_message": "\n",
            }
        } # tell LiteLLM how you want to map the openai messages to this model
    pre_message_sep= "\n",
    post_message_sep= "\n"
)

def test_huggingface_custom_model():
    model = "huggingface/togethercomputer/LLaMA-2-7B-32K"
    response = completion(model=model, messages=messages, api_base="https://ecd4sb5n09bo4ei2.us-east-1.aws.endpoints.huggingface.cloud")
    print(response['choices'][0]['message']['content'])
    return response

test_huggingface_custom_model()

Implementation Code

deploying a model on huggingface

You can use any chat/text model from Hugging Face with the following steps:

Copy your model id/url from Huggingface Inference Endpoints
- Go to https://ui.endpoints.huggingface.co/
- Copy the url of the specific model you'd like to use
Set it as your model name
Set your HUGGINGFACE_API_KEY as an environment variable

Need help deploying a model on huggingface? Check out this guide.

usage

You need to tell LiteLLM when you're calling Huggingface.

Do that by passing in the custom llm provider as part of the model name -
completion(model="<custom_llm_provider>/<model_name>",...).

Model name - WizardLM/WizardCoder-Python-34B-V1.0

Model id - https://ji16r2iys9a8rjk2.us-east-1.aws.endpoints.huggingface.cloud

import os 
from litellm import completion 

# Set env variables
os.environ["HUGGINGFACE_API_KEY"] = "huggingface_api_key"

messages = [{ "content": "There's a llama in my garden 😱 What should I do?","role": "user"}]

# model = <custom_llm_provider>/<model_id>
response = completion(model="huggingface/WizardLM/WizardCoder-Python-34B-V1.0", messages=messages, api_base="https://ji16r2iys9a8rjk2.us-east-1.aws.endpoints.huggingface.cloud")

print(response)

output

Same as the OpenAI format, but also includes logprobs. See the code

{
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "message": {
        "content": "\ud83d\ude31\n\nComment: @SarahSzabo I'm",
        "role": "assistant",
        "logprobs": -22.697942825499993
      }
    }
  ],
  "created": 1693436637.38206,
  "model": "https://ji16r2iys9a8rjk2.us-east-1.aws.endpoints.huggingface.cloud",
  "usage": {
    "prompt_tokens": 14,
    "completion_tokens": 11,
    "total_tokens": 25
  }
}

FAQ

Does this support stop sequences?

Yes, we support stop sequences - and you can pass as many as allowed by Huggingface (or any provider!)

How do you deal with repetition penalty?

We map the presence penalty parameter in openai to the repetition penalty parameter on Huggingface. See code.

We welcome any suggestions for improving our Huggingface integration - Create an issue/Join the Discord!

Huggingface

API KEYS​

Models with Prompt Formatting​

Models with Special Prompt Templates​

Custom prompt templates​

deploying a model on huggingface​

usage​