Muse Model by HuggingArtists

Introduction

The Muse model by HuggingArtists is a text-generation model fine-tuned on the lyrics of the band Muse. It leverages the capabilities of the GPT-2 architecture to generate text based on Muse's lyrical style.

Architecture

The model is built upon the pre-trained GPT-2 architecture, fine-tuned specifically on a dataset comprising Muse's lyrics. This allows the model to generate text that mimics the thematic and stylistic elements found in the band's music.

Training

Training Data: The model was trained using a dataset of Muse lyrics, which can be accessed via the Hugging Face datasets library.
```
from datasets import load_dataset
dataset = load_dataset("huggingartists/muse")
```
Training Procedure: The training involved fine-tuning GPT-2 on the lyric dataset. Hyperparameters and metrics were meticulously tracked using Weights & Biases (W&B) for transparency and reproducibility. The final trained model was logged and versioned through W&B.

Guide: Running Locally

To run the Muse model locally, follow these steps:

Install Dependencies: Ensure you have the transformers library installed.
```
pip install transformers
```

Load the Model: Use the Transformers library to load the model and tokenizer.

from transformers import pipeline

generator = pipeline('text-generation', model='huggingartists/muse')
generator("I am", num_return_sequences=5)

Cloud GPU Suggestion: For optimal performance, especially for large-scale text generation tasks, utilize cloud GPUs from providers like AWS, Google Cloud, or Azure.

License

The model adheres to the same limitations and biases as the GPT-2 model, including potential biases inherent in the training data. The code and model are made available for use following the licensing terms provided by the HuggingArtists project on their GitHub repository.

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