Gunna

🚀 HuggingArtists Model - Gunna

This is a HuggingArtists model based on Gunna's lyrics. It allows you to generate text in the style of Gunna. You can create your own bot using this model and explore the world of lyric generation.

🚀 Quick Start

You can use this model directly with a pipeline for text generation:

from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/gunna')
generator("I am", num_return_sequences=5)

Or with the Transformers library:

from transformers import AutoTokenizer, AutoModelWithLMHead
  
tokenizer = AutoTokenizer.from_pretrained("huggingartists/gunna")

model = AutoModelWithLMHead.from_pretrained("huggingartists/gunna")

✨ Features

• Artist-Specific Style: Generate text in the style of Gunna, leveraging his unique lyrical patterns.
• Easy to Use: Can be used directly with the Transformers pipeline or library.

📦 Installation

There is no specific installation step provided in the original README. If you want to use the model, you need to have the transformers and datasets libraries installed. You can install them using the following commands:

pip install transformers
pip install datasets

💻 Usage Examples

Basic Usage

from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/gunna')
generator("I am", num_return_sequences=5)

Advanced Usage

from transformers import AutoTokenizer, AutoModelWithLMHead
  
tokenizer = AutoTokenizer.from_pretrained("huggingartists/gunna")
model = AutoModelWithLMHead.from_pretrained("huggingartists/gunna")

input_text = "I am"
input_ids = tokenizer.encode(input_text, return_tensors='pt')
output = model.generate(input_ids, max_length=100, num_return_sequences=5)
for sequence in output:
    print(tokenizer.decode(sequence, skip_special_tokens=True))

📚 Documentation

How does it work?

To understand how the model was developed, check the W&B report.

Training data

The model was trained on lyrics from Gunna. Dataset is available here. And can be used with:

from datasets import load_dataset

dataset = load_dataset("huggingartists/gunna")

Explore the data, which is tracked with W&B artifacts at every step of the pipeline.

Training procedure

The model is based on a pre - trained GPT - 2 which is fine - tuned on Gunna's lyrics. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned.

🔧 Technical Details

The model is built on top of the pre - trained GPT - 2 architecture. By fine - tuning on Gunna's lyrics, it captures the unique language patterns and styles in his songs. The training process is tracked using Weights & Biases (W&B) to ensure transparency and reproducibility.

📄 License

No license information is provided in the original README.

Limitations and bias

The model suffers from the same limitations and bias as GPT - 2. In addition, the data present in the user's tweets further affects the text generated by the model.

About

Built by Aleksey Korshuk

For more details, visit the project repository.

⚠️ Important Note

The model suffers from the same limitations and bias as GPT - 2, and user - related data may affect the generated text.

💡 Usage Tip

You can adjust the parameters in the generate method, such as max_length and num_return_sequences, to get different text generation results.