HuggingFace

Hugging Face provides a wide range of state-of-the-art language models tailored to diverse NLP tasks, including text generation, summarization, translation, and question answering. These models are available through the Hugging Face Transformers library and are widely adopted due to their ease of use, flexibility, and comprehensive documentation. Explore HuggingFace’s language models here.

Authentication

Set your HF_TOKEN environment. You can get one from HuggingFace here.

export HF_TOKEN=***

setx HF_TOKEN ***

Example

Install the huggingface_hub package:

uv pip install -U huggingface_hub agno

Use HuggingFace with your Agent:

from agno.agent import Agent
from agno.models.huggingface import HuggingFace

agent = Agent(
    model=HuggingFace(
        id="meta-llama/Meta-Llama-3-8B-Instruct",
        max_tokens=4096,
    ),
    markdown=True
)

# Print the response on the terminal
agent.print_response("Share a 2 sentence horror story.")

View more examples here.

Parameters

Parameter	Type	Default	Description
`id`	`str`	`"meta-llama/Meta-Llama-3-8B-Instruct"`	The id of the Hugging Face model to use
`name`	`str`	`"HuggingFace"`	The name of the model
`provider`	`str`	`"HuggingFace"`	The provider of the model
`api_key`	`Optional[str]`	`None`	The API key for Hugging Face (defaults to HF_TOKEN env var)
`base_url`	`Optional[Union[str, httpx.URL]]`	`None`	The base URL for the inference endpoint
`max_tokens`	`Optional[int]`	`None`	Maximum number of tokens to generate
`temperature`	`Optional[float]`	`None`	Controls randomness in the model’s output
`top_p`	`Optional[float]`	`None`	Controls diversity via nucleus sampling
`timeout`	`Optional[float]`	`None`	Request timeout in seconds

HuggingFace is a subclass of the Model class and has access to the same params.

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HuggingFace

Authentication

Example

Parameters

​Authentication

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​Parameters

Authentication

Example

Parameters