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@ -1,17 +1,14 @@
import Image from '@theme/IdealImage';
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
# Huggingface
LiteLLM supports Huggingface models that use the [text-generation-inference](https://github.com/huggingface/text-generation-inference) format or the [Conversational task](https://huggingface.co/docs/api-inference/detailed_parameters#conversational-task) format.
LiteLLM supports the following types of Huggingface models:
* Text-generation-interface: [Here's all the models that use this format](https://huggingface.co/models?other=text-generation-inference).
* Conversational task: [Here's all the models that use this format](https://huggingface.co/models?pipeline_tag=conversational).
* 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](#conversational-task-blenderbot-etc)
## Usage
<a target="_blank" href="https://colab.research.google.com/github/BerriAI/litellm/blob/main/cookbook/LiteLLM_HuggingFace.ipynb">
@ -21,7 +18,9 @@ This can be changed by setting `task="conversational"` in the completion call. [
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) - LLMs
<Tabs>
<TabItem value="tgi" label="Text-generation-interface (TGI)">
```python
import os
from litellm import completion
@ -32,14 +31,17 @@ 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")
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")`
</TabItem>
<TabItem value="conv" label="Conversational-task (BlenderBot, etc.)">
```python
import os
@ -51,14 +53,17 @@ 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")
response = completion(
model="huggingface/facebook/blenderbot-400M-distill",
messages=messages,
api_base="https://my-endpoint.huggingface.cloud"
)
print(response)
```
</TabItem>
<TabItem value="none" label="Non TGI/Conversational-task LLMs">
### Non TGI/Conversational-task LLMs
**Key Change**: `completion(..., task=None)`
```python
import os
from litellm import completion
@ -66,15 +71,19 @@ 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=[{ "content": "My name is Merve and my favorite", "role": "user"}],
messages=messages,
api_base="https://p69xlsj6rpno5drq.us-east-1.aws.endpoints.huggingface.cloud",
task=None,
)
# Add any assertions here to check the response
print(response)
```
</TabItem>
</Tabs>
### [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](https://github.com/BerriAI/litellm/blob/0100ab2382a0e720c7978fbf662cc6e6920e7e03/litellm/llms/huggingface_restapi.py#L25)