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

# Huggingface

> Using Huggingface models with Upsonic

## Overview

Huggingface provides access to thousands of open-source models through their Inference API. Great for experimentation with cutting-edge models.

**Model Class:** `HuggingFaceModel`

## Authentication

```bash theme={null}
export HF_TOKEN="hf_..."
```

## Examples

```python theme={null}
from upsonic import Agent, Task
from upsonic.models.huggingface import HuggingFaceModel

model = HuggingFaceModel(model_name="meta-llama/Llama-3.3-70B-Instruct")
agent = Agent(model=model)

task = Task("Hello, how are you?")
result = agent.do(task)
print(result)
```

## Model Settings

You can set model parameters in two ways: on the model or on the Agent.

**On the model:**

```python theme={null}
from upsonic import Agent, Task
from upsonic.models.huggingface import HuggingFaceModel, HuggingFaceModelSettings

model = HuggingFaceModel(
    model_name="meta-llama/Llama-3.3-70B-Instruct",
    settings=HuggingFaceModelSettings(max_tokens=1024, temperature=0.7)
)
agent = Agent(model=model)
```

**On the Agent:**

```python theme={null}
from upsonic import Agent, Task
from upsonic.models.huggingface import HuggingFaceModelSettings

agent = Agent(
    model="huggingface/meta-llama/Llama-3.3-70B-Instruct",
    settings=HuggingFaceModelSettings(max_tokens=1024)
)
```

## Parameters

| Parameter | Type | Description | Default | Source |
| - | - | - | - | - |
| `max_tokens` | `int` | Maximum tokens to generate | 2048 | Base |
| `temperature` | `float` | Sampling temperature (0.0-1.0) | 0.7 | Base |
| `top_p` | `float` | Nucleus sampling | 0.95 | Base |
| `seed` | `int` | Random seed | None | Base |
| `stop_sequences` | `list[str]` | Stop sequences | None | Base |
| `timeout` | `float` | Request timeout (seconds) | 600 | Base |


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