## `HuggingfaceLLM` Documentation

### Introduction

The `HuggingfaceLLM` class is designed for running inference using models from the Hugging Face Transformers library. This documentation provides an in-depth understanding of the class, its purpose, attributes, methods, and usage examples.

#### Purpose

The `HuggingfaceLLM` class serves the following purposes:

1. Load pre-trained Hugging Face models and tokenizers.
2. Generate text-based responses from the loaded model using a given prompt.
3. Provide flexibility in device selection, quantization, and other configuration options.

### Class Definition

The `HuggingfaceLLM` class is defined as follows:

```python
class HuggingfaceLLM:
    def __init__(
        self,
        model_id: str,
        device: str = None,
        max_length: int = 20,
        quantize: bool = False,
        quantization_config: dict = None,
        verbose=False,
        distributed=False,
        decoding=False,
    ):
        # Attributes and initialization logic explained below
        pass

    def load_model(self):
        # Method to load the pre-trained model and tokenizer
        pass

    def run(self, prompt_text: str, max_length: int = None):
        # Method to generate text-based responses
        pass

    def __call__(self, prompt_text: str, max_length: int = None):
        # Alternate method for generating text-based responses
        pass
```

### Attributes

| Attribute            | Description                                                                                                               |
|----------------------|---------------------------------------------------------------------------------------------------------------------------|
| `model_id`           | The ID of the pre-trained model to be used.                                                                              |
| `device`             | The device on which the model runs (`'cuda'` for GPU or `'cpu'` for CPU).                                              |
| `max_length`         | The maximum length of the generated text.                                                                                |
| `quantize`           | A boolean indicating whether quantization should be used.                                                               |
| `quantization_config`| A dictionary with configuration options for quantization.                                                                |
| `verbose`            | A boolean indicating whether verbose logs should be printed.                                                             |
| `logger`             | An optional logger for logging messages (defaults to a basic logger).                                                   |
| `distributed`        | A boolean indicating whether distributed processing should be used.                                                     |
| `decoding`           | A boolean indicating whether to perform decoding during text generation.                                                  |

### Class Methods

#### `__init__` Method

The `__init__` method initializes an instance of the `HuggingfaceLLM` class with the specified parameters. It also loads the pre-trained model and tokenizer.

- `model_id` (str): The ID of the pre-trained model to use.
- `device` (str, optional): The device to run the model on ('cuda' or 'cpu').
- `max_length` (int, optional): The maximum length of the generated text.
- `quantize` (bool, optional): Whether to use quantization.
- `quantization_config` (dict, optional): Configuration for quantization.
- `verbose` (bool, optional): Whether to print verbose logs.
- `logger` (logging.Logger, optional): The logger to use.
- `distributed` (bool, optional): Whether to use distributed processing.
- `decoding` (bool, optional): Whether to perform decoding during text generation.

#### `load_model` Method

The `load_model` method loads the pre-trained model and tokenizer specified by `model_id`.

#### `run` and `__call__` Methods

Both `run` and `__call__` methods generate text-based responses based on a given prompt. They accept the following parameters:

- `prompt_text` (str): The text prompt to initiate text generation.
- `max_length` (int, optional): The maximum length of the generated text.

### Usage Examples

Here are three ways to use the `HuggingfaceLLM` class:

#### Example 1: Basic Usage

```python
from swarms.models import HuggingfaceLLM

# Initialize the HuggingfaceLLM instance with a model ID
model_id = "NousResearch/Nous-Hermes-2-Vision-Alpha"
inference = HuggingfaceLLM(model_id=model_id)

# Generate text based on a prompt
prompt_text = "Once upon a time"
generated_text = inference(prompt_text)
print(generated_text)
```

#### Example 2: Custom Configuration

```python
from swarms.models import HuggingfaceLLM

# Initialize with custom configuration
custom_config = {
    "quantize": True,
    "quantization_config": {"load_in_4bit": True},
    "verbose": True
}
inference = HuggingfaceLLM(model_id="NousResearch/Nous-Hermes-2-Vision-Alpha", **custom_config)

# Generate text based on a prompt
prompt_text = "Tell me a joke"
generated_text = inference(prompt_text)
print(generated_text)
```

#### Example 3: Distributed Processing

```python
from swarms.models import HuggingfaceLLM

# Initialize for distributed processing
inference = HuggingfaceLLM(model_id="gpt2-medium", distributed=True)

# Generate text based on a prompt
prompt_text = "Translate the following sentence to French"
generated_text = inference(prompt_text)
print(generated_text)
```

### Additional Information

- The `HuggingfaceLLM` class provides the flexibility to load and use pre-trained models from the Hugging Face Transformers library.
- Quantization can be enabled to reduce model size and inference time.
- Distributed processing can be used for parallelized inference.
- Verbose logging can help in debugging and understanding the text generation process.

### References

- [Hugging Face Transformers Documentation](https://huggingface.co/transformers/)
- [PyTorch Documentation](https://pytorch.org/docs/stable/index.html)

This documentation provides a comprehensive understanding of the `HuggingfaceLLM` class, its attributes, methods, and usage examples. Developers can use this class to perform text generation tasks efficiently using pre-trained models from the Hugging Face Transformers library.