Lamini Prompt Enchance

🚀 Lamini-Prompt-Enchance

A fine - tuned model for prompt enhancement based on MBZUAI/LaMini - Flan - T5 - 248M, achieving good results in evaluation metrics.

🚀 Quick Start

from transformers import pipeline     

# load model and tokenizer from huggingface hub with pipeline
enhancer = pipeline("summarization", model="gokaygokay/Lamini-Prompt-Enchance", device=0)

prompt = "A blue-tinted bedroom scene, surreal and serene, with a mysterious reflected interior."
prefix = "Enhance the description: "
# enhance prompt
res = enhancer(prefix + prompt)

print(res[0]['summary_text'])

# A surreal and serene bedroom scene with a mysterious mirrored interior, 
# awash in blue and green hues.
# The room is adorned with intricate patterns and a mirrored wall, 
# creating a sense of mystery and tranquility.

✨ Features

This model is a fine - tuned version of [MBZUAI/LaMini - Flan - T5 - 248M](https://huggingface.co/MBZUAI/LaMini - Flan - T5 - 248M) on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 2.0195
• Rouge1: 31.5042
• Rouge2: 13.2633
• Rougel: 26.4176
• Rougelsum: 28.4846
• Gen Len: 19.0

🔧 Technical Details

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e - 05
• train_batch_size: 24
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Framework versions

• Transformers 4.42.3
• Pytorch 2.3.0+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1

📄 License

This model is licensed under the CC - BY - NC - 4.0 license.