Lamini Prompt Enchance Long

🚀 Lamini-Prompt-Enchance-Long

A fine - tuned model based on MBZUAI/LaMini - Flan - T5 - 248M for enhancing prompts.

🚀 Quick Start

This model can be easily used with the transformers library. Here's a simple example to get you started:

from transformers import pipeline     

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

✨ Features

• Fine - Tuned: Based on the MBZUAI/LaMini - Flan - T5 - 248M model, fine - tuned on an unknown dataset.
• Evaluation Metrics: Achieves good results on evaluation metrics such as Rouge1, Rouge2, etc.

📦 Installation

No specific installation steps are provided in the original README. If you want to use this model, you need to have the transformers library installed. You can install it using pip install transformers.

💻 Usage Examples

Basic Usage

from transformers import pipeline     

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

📚 Documentation

Model Information

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: 2.1624
• Rouge1: 20.2443
• Rouge2: 9.3642
• Rougel: 17.2484
• Rougelsum: 19.0703
• Gen Len: 19.0

Training Procedure

Training Hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e - 05
• train_batch_size: 8
• 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.