Huggingface

LiteLLM supports the following types of Huggingface models:

Text-generation-interface: Here's all the models that use this format.
Conversational task: Here's all the models that use this format.
Non TGI/Conversational-task LLMs

Usage

You need to tell LiteLLM when you're calling Huggingface. This is done by adding the "huggingface/" prefix to model, example completion(model="huggingface/<model_name>",...).

Text-generation-interface (TGI)
Conversational-task (BlenderBot, etc.)
Non TGI/Conversational-task LLMs

import os 
from litellm import completion 

# [OPTIONAL] set env var
os.environ["HUGGINGFACE_API_KEY"] = "huggingface_api_key" 

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

# e.g. Call 'WizardLM/WizardCoder-Python-34B-V1.0' hosted on HF Inference endpoints
response = completion(
  model="huggingface/WizardLM/WizardCoder-Python-34B-V1.0", 
  messages=messages, 
  api_base="https://my-endpoint.huggingface.cloud"
)

print(response)

import os 
from litellm import completion 

# [OPTIONAL] set env var
os.environ["HUGGINGFACE_API_KEY"] = "huggingface_api_key" 

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

# e.g. Call 'facebook/blenderbot-400M-distill' hosted on HF Inference endpoints
response = completion(
  model="huggingface/facebook/blenderbot-400M-distill", 
  messages=messages, 
  api_base="https://my-endpoint.huggingface.cloud"
)

print(response)

import os 
from litellm import completion 

# [OPTIONAL] set env var
os.environ["HUGGINGFACE_API_KEY"] = "huggingface_api_key" 

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

# e.g. Call 'roneneldan/TinyStories-3M' hosted on HF Inference endpoints
response = completion(
  model="huggingface/roneneldan/TinyStories-3M", 
  messages=messages,
  api_base="https://p69xlsj6rpno5drq.us-east-1.aws.endpoints.huggingface.cloud",
)

print(response)

Usage - Streaming

You need to tell LiteLLM when you're calling Huggingface. This is done by adding the "huggingface/" prefix to model, example completion(model="huggingface/<model_name>",...).

import os 
from litellm import completion 

# [OPTIONAL] set env var
os.environ["HUGGINGFACE_API_KEY"] = "huggingface_api_key" 

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

# e.g. Call 'facebook/blenderbot-400M-distill' hosted on HF Inference endpoints
response = completion(
  model="huggingface/facebook/blenderbot-400M-distill", 
  messages=messages, 
  api_base="https://my-endpoint.huggingface.cloud",
  stream=True
)

print(response)
for chunk in response:
  print(chunk)

[OPTIONAL] API KEYS + API BASE

If required, you can set the api key + api base, set it in your os environment. Code for how it's sent

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

Models with Prompt Formatting

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

Models with natively Supported Prompt Templates

Model Name	Works for Models	Function Call	Required OS Variables
mistralai/Mistral-7B-Instruct-v0.1	mistralai/Mistral-7B-Instruct-v0.1	`completion(model='huggingface/mistralai/Mistral-7B-Instruct-v0.1', messages=messages, api_base="your_api_endpoint")`	`os.environ['HUGGINGFACE_API_KEY']`
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']`

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

Custom prompt templates

# Create your own custom prompt template works 
litellm.register_prompt_template(
        model="togethercomputer/LLaMA-2-7B-32K",
        roles={
            "system": {
                "pre_message": "[INST] <<SYS>>\n",
                "post_message": "\n<</SYS>>\n [/INST]\n"
            },
            "user": {
                "pre_message": "[INST] ",
                "post_message": " [/INST]\n"
            }, 
            "assistant": {
                "post_message": "\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.

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

Usage​

Usage - Streaming​

[OPTIONAL] API KEYS + API BASE​

Models with Prompt Formatting​

Models with natively Supported Prompt Templates​

Custom prompt templates​

deploying a model on huggingface​