8.3 KiB
import Image from '@theme/IdealImage';
Huggingface
LiteLLM supports Huggingface models that use the text-generation-inference format or the Conversational task format.
- 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
By default, we assume the you're trying to call models with the 'text-generation-interface' format (e.g. Llama2, Falcon, WizardCoder, MPT, etc.)
This can be changed by setting task="conversational" in the completion call. Example
Usage
You need to tell LiteLLM when you're calling Huggingface. Do that by setting it as part of the model name - completion(model="huggingface/<model_name>",...).
Text-generation-interface (TGI) - 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)
Conversational-task (BlenderBot, etc.) LLMs
Key Change: completion(..., task="conversational")
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", task="conversational")
print(response)
Non TGI/Conversational-task LLMs
Key Change: completion(..., task=None)
import os
from litellm import completion
# [OPTIONAL] set env var
os.environ["HUGGINGFACE_API_KEY"] = "huggingface_api_key"
response = completion(
model="huggingface/roneneldan/TinyStories-3M",
messages=[{ "content": "My name is Merve and my favorite", "role": "user"}],
api_base="https://p69xlsj6rpno5drq.us-east-1.aws.endpoints.huggingface.cloud",
task=None,
)
# Add any assertions here to check the response
print(response)
[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.
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",
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()
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 <Image img={require('../../img/hf_inference_endpoint.png')} alt="HF_Dashboard" style={{ maxWidth: '50%', height: 'auto' }}/>
- 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!