This project presents an innovative approach to prompt generation utilizing the Qlora method combined with Parameter-Efficient Fine-Tuning (PEFT) techniques. Leveraging the capabilities of the TinyLlama model, we demonstrate how to effectively generate high-quality prompts while optimizing computational efficiency through the use of 8-bit model configurations with BitsAndBytesConfig. The repository includes comprehensive implementations in both Kaggle and Google Colab environments, making it accessible for diverse users. Performance metrics are analyzed using TensorBoard, showcasing the model's effectiveness. Additionally, the project utilizes a curated dataset from Hugging Face, enhancing the prompt generation process. This work aims to contribute to the growing field of natural language processing by providing a user-friendly interface for prompt creation and insights into model performance.
The Qlora-Peft-LLMs-Prompt-Generation project focuses on efficient prompt generation using the Qlora method and Parameter-Efficient Fine-Tuning (PEFT) with the TinyLlama model. This repository addresses the growing need for optimized techniques in natural language processing by employing an 8-bit configuration via BitsAndBytesConfig, which minimizes computational requirements without compromising performance.
Offering implementations in both Kaggle and Google Colab, the project ensures accessibility for diverse users. Performance metrics are visualized through TensorBoard, providing insights into the prompt generation process. By utilizing a curated dataset from Hugging Face, this work demonstrates the potential of Qlora while serving as a valuable resource for practitioners and researchers in prompt engineering.
This section details the technical approaches and tools employed in the project, ensuring that others can replicate our work effectively.
To load the TinyLlama model with 8-bit quantization, we use the following code:
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig # Load model and tokenizer model_checkpoint = "TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T" tokenizer = AutoTokenizer.from_pretrained(model_checkpoint) # 8-bit Quantization configuration bnb_config = BitsAndBytesConfig(load_in_8bit=True, bnb_8bit_compute_dtype=torch.bfloat16) model = AutoModelForCausalLM.from_pretrained(model_checkpoint, quantization_config=bnb_config)
The TinyLlama model was selected for its effectiveness in generative tasks, particularly due to its pre-training which enables quicker adaptation for specific applications. The use of 8-bit quantization allows us to run this large model on standard hardware without excessive memory demands.
The implementation of LoRA is significant for minimizing the number of parameters we need to tune, allowing for efficient fine-tuning while maintaining performance.
Here’s a simplified overview of the main steps in our workflow:
1. Import required libraries and set up device.
2. Load the model and tokenizer with quantization settings.
3. Define the training dataset and preprocess it.
4. Set training configurations and initialize the trainer.
5. Train the model and evaluate its performance.
To ensure that our work can be easily replicated, we specify the library versions used:
These details provide a comprehensive framework for others to follow in reproducing our results.
All code snippets included focus on critical components relevant to the methodology, ensuring clarity without overwhelming the reader.
For each epoch: 1. Load training data. 2. Forward pass. 3. Compute loss. 4. Update parameters. 5. Evaluate on validation set.
The TinyLlama-1.1B model was fine-tuned to generate prompts based on input titles, achieving a noticeable reduction in training loss.
| Step | Loss |
|---|---|
| 0 | 3.57 |
| 400 | 1.94 |
Graph of Training Loss Over Steps
(Include line chart)
The experiments validated the model's capability to generate relevant prompts. Future work will explore larger datasets and different hyperparameters for improved robustness.
The fine-tuning of the TinyLlama-1.1B model effectively generated contextually relevant prompts, with training loss indicating successful learning from the dataset.
The use of LoRA for parameter-efficient fine-tuning proved effective, but the limited dataset may lead to overfitting, suggesting that larger, more diverse datasets should be explored.
Future efforts will focus on expanding the dataset, experimenting with different architectures, and evaluating real-world applications.
This project highlights the potential of large language models for prompt generation, with ongoing refinements needed to maximize their effectiveness.
In this project, we successfully fine-tuned the TinyLlama-1.1B model to generate prompts based on specific inputs, demonstrating its capability for context-aware text generation. The implementation of LoRA allowed for efficient parameter tuning, leading to improved model performance.
However, limitations were noted, particularly regarding the dataset size, which may hinder the model's generalization to unseen prompts. Future work should focus on expanding the dataset and experimenting with additional hyperparameters to enhance the model's robustness.
Overall, this project showcases the potential of large language models in generating tailored prompts, paving the way for further research and development in this domain. We encourage future exploration into larger datasets and diverse architectures to maximize effectiveness and applicability.
Hugging Face Transformers: Transformers Documentation - Comprehensive guide to the Transformers library used for model training and deployment.
PEFT (Parameter-Efficient Fine-Tuning): PEFT Documentation - Detailed explanation of techniques for fine-tuning large models with limited resources.
Datasets: Hugging Face Datasets - Information on datasets available for training and evaluation.
Markdown Formatting Guide: ReadyTensor Markdown Guide - A helpful resource for understanding and using Markdown effectively.
Choose a License: ChooseALicense.com - A website providing information on different open-source licenses to help users select an appropriate license for their projects.
I would like to thank the following:
training_args = TrainingArguments( output_dir="./kaggle/working/", per_device_train_batch_size=8, gradient_checkpointing=True, gradient_accumulation_steps=4, max_steps=400, learning_rate=2.5e-5, logging_steps=5, fp16=True, save_strategy="steps", save_steps=50, evaluation_strategy="steps", eval_steps=5, do_eval=True, save_total_limit=3, )
To set up the environment for this project, follow these steps:
Clone the repository:
git clone <https://github.com/Warishayat/Qlora-Peft-LLM-s.git> cd <Qlora-Peft-LLM-s>
Install the required dependencies:
pip install torch transformers peft datasets huggingface-hub
Ensure you have the appropriate hardware (preferably a GPU) for optimal performance.
Example output from the model:
**System: Based on input title generate the prompt for generative Model
##input: Linux Terminal
##prompt: [Generated Prompt Here]