> ## Documentation Index
> Fetch the complete documentation index at: https://docs-v1.agno.com/llms.txt
> Use this file to discover all available pages before exploring further.

# HuggingFace

> Learn how to use HuggingFace models in Agno.

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](https://huggingface.co/docs/text-generation-inference/en/supported_models).

## Authentication

Set your `HF_TOKEN` environment. You can get one [from HuggingFace here](https://huggingface.co/settings/tokens).

<CodeGroup>
  ```bash Mac theme={null}
  export HF_TOKEN=***
  ```

  ```bash Windows theme={null}
  setx HF_TOKEN ***
  ```
</CodeGroup>

## Example

Use `HuggingFace` with your `Agent`:

<CodeGroup>
  ```python agent.py theme={null}
  from agno.agent import Agent, RunResponse
  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.")
  ```
</CodeGroup>

<Note> View more examples [here](../examples/models/huggingface). </Note>

## Params

<Snippet file="model-hf-params.mdx" />

`HuggingFace` is a subclass of the [Model](/reference/models/model) class and has access to the same params.