![freepik__upload__74946.jpeg](freepik__upload__74946.jpeg) ## Abstract 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. ## Introduction 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. # **Methodology** This section details the technical approaches and tools employed in the project, ensuring that others can replicate our work effectively. ## **1. Techniques and Tools** ### **Frameworks and Libraries** - **PyTorch**: Utilized for building and training our deep learning model. - **Transformers**: From Hugging Face, this library is used for accessing pre-trained language models and their tokenizers. - **PEFT (Parameter-Efficient Fine-Tuning)**: This approach allows for efficient fine-tuning of models with fewer parameters, utilizing techniques like LoRA (Low-Rank Adaptation). - **Datasets**: A library for loading and processing datasets efficiently. ### **Key Code Snippets** To load the TinyLlama model with 8-bit quantization, we use the following code: ```python 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) ``` ## **2. Explanation of Choices** 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. ## **3. Workflow Overview** 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. ``` ## **4. Reproducibility** To ensure that our work can be easily replicated, we specify the library versions used: - PyTorch: 1.12.1 - Transformers: 4.21.1 - PEFT: 0.1.0 - Datasets: 1.18.1 ### **Hyperparameters** - **Batch Size**: 8 - **Learning Rate**: 2.5e-5 - **Training Steps**: 400 These details provide a comprehensive framework for others to follow in reproducing our results. ## **5. Code Clarity** All code snippets included focus on critical components relevant to the methodology, ensuring clarity without overwhelming the reader. # **Experiments** ## **1. Setup** - **Model**: TinyLlama-1.1B - **Dataset**: "awesome-chatgpt-prompts" (90% train, 10% test) - **Environment**: GPU for accelerated training. ## **2. Training Procedure** - **Batch Size**: 8 - **Max Steps**: 400 - **Learning Rate**: 2.5e-5 - **Gradient Accumulation**: 4 ### **Pseudocode** ```plaintext For each epoch: 1. Load training data. 2. Forward pass. 3. Compute loss. 4. Update parameters. 5. Evaluate on validation set. ``` ## **3. Evaluation Metrics** - **Loss**: Monitored during training. - **Output Quality**: Assessed by reviewing generated prompts for relevance. ## **4. Results** - Model generated coherent prompts based on input titles. # **Results** ## **1. Model Performance** The TinyLlama-1.1B model was fine-tuned to generate prompts based on input titles, achieving a noticeable reduction in training loss. ### **Training Loss** | Step | Loss | |------|------| | 0 | 3.57 | | 400 | 1.94 | *Graph of Training Loss Over Steps* *(Include line chart)* ## **2. Key Findings** - The model effectively generates coherent prompts. - Example: **Input**: "Linux Terminal" **Output**: "System: Based on input title generate the prompt..." ## **3. Interpretation** - Training loss decrease indicates effective learning. - Results met expectations, though potential overfitting is a concern due to limited dataset size. ## **4. Summary** The experiments validated the model's capability to generate relevant prompts. Future work will explore larger datasets and different hyperparameters for improved robustness. # **Discussion** ## **1. Analysis of Results** The fine-tuning of the TinyLlama-1.1B model effectively generated contextually relevant prompts, with training loss indicating successful learning from the dataset. ## **2. Model Performance** 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. ## **3. Limitations** - **Dataset Size**: A smaller dataset restricts generalizability. - **Hyperparameters**: Further tuning could enhance performance. ## **4. Future Work** Future efforts will focus on expanding the dataset, experimenting with different architectures, and evaluating real-world applications. ## **5. Conclusion** This project highlights the potential of large language models for prompt generation, with ongoing refinements needed to maximize their effectiveness. # **Conclusion** 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. # **References** 1. **Hugging Face Transformers**: [Transformers Documentation](https://huggingface.co/docs/transformers/index) - Comprehensive guide to the Transformers library used for model training and deployment. 2. **PEFT (Parameter-Efficient Fine-Tuning)**: [PEFT Documentation](https://github.com/huggingface/peft) - Detailed explanation of techniques for fine-tuning large models with limited resources. 3. **Datasets**: [Hugging Face Datasets](https://huggingface.co/docs/datasets/index) - Information on datasets available for training and evaluation. 4. **Markdown Formatting Guide**: [ReadyTensor Markdown Guide](https://www.readytensor.com/markdown-guide) - A helpful resource for understanding and using Markdown effectively. 5. **Choose a License**: [ChooseALicense.com](https://choosealicense.com/) - A website providing information on different open-source licenses to help users select an appropriate license for their projects. # Acknowledgements I would like to thank the following: - **OpenAI** for their pioneering work in artificial intelligence, which has inspired this project. - **Hugging Face** for providing accessible tools and models, making it easier to implement state-of-the-art NLP solutions. - The **community contributors** on GitHub and various forums whose insights and discussions greatly aided developments of this project. # Appendix ## Additional Code Snippets ### Hyperparameter Configuration ```python 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, ) ``` ## Extended Data Visualizations ### Model Performance Over Time - Include graphs showing the model's accuracy and loss during training. ## Installation Instructions To set up the environment for this project, follow these steps: 1. Clone the repository: ```bash git clone cd ``` 2. Install the required dependencies: ```bash pip install torch transformers peft datasets huggingface-hub ``` 3. Ensure you have the appropriate hardware (preferably a GPU) for optimal performance. ## Sample Outputs Example output from the model: ``` **System: Based on input title generate the prompt for generative Model ##input: Linux Terminal ##prompt: [Generated Prompt Here] ``` ## Full Dataset Description - **Dataset Used**: The dataset "fka/awesome-chatgpt-prompts" contains a variety of prompts that serve as input-output pairs for training the model. ## References to Related Work - [Hugging Face Transformers Documentation](https://huggingface.co/docs/transformers/index) - [PEFT Library Documentation](https://github.com/huggingface/peft) ## Glossary of Terms - **PEFT**: Parameter-Efficient Fine-Tuning, a method that allows efficient fine-tuning of large models. - **Gradient Checkpointing**: A technique that saves memory by trading computation for memory, allowing training of larger models.