This peoject explores the development and optimization journey of a Large Language Model (LLM) focused on creative text generation. The primary objective is to empower the model, named Dolly-v2-3b, to generate engaging and coherent narratives in response to diverse prompts. By fine-tuning on a specialized dataset comprising 15,000 instruction/response pairs, curated across various domains, the model excels in tasks such as text-generation. Its efficiency, with 3 billion parameters, ensures high-quality responses while maintaining computational lightness, crucial for practical applications prioritizing responsiveness and cost effectiveness. Integration with the Intel Extension for Transformers plays a pivotal role in enhancing the model's performance. This collaboration optimizes hardware utilization, resulting in faster inference times and improved efficiency during text generation tasks. Evaluation metrics like eval_loss and eval_ppl underscore the model's accuracy and predictive capability, showcasing its ability to deliver precise and contextually appropriate responses. Benchmarking exercises highlight the model's robustness, with metrics indicating low latency and high throughput during inference. For instance, the model processes 100 samples in approximately 14.16 seconds, achieving an average throughput of 7.061 samples per second and demonstrating its suitability for real-time applications requiring rapid response capabilities. Furthermore, this project discusses the impact of fine-tuning methodologies, utilizing a systematic approach to ensure the model's outputs uphold ethical standards and inclusivity. By embedding prompts that encourage socially conscious storytelling, the training process mitigates bias and promotes the creation of engaging, unbiased narratives.
This project focuses on implementing and fine-tuning LLMs to develop a custom chatbot, emphasizing CPU inference and integration with Intel Extension for Transformers. The goal is to optimize model performance for real-world applications requiring efficient and responsive text generation capabilities.
Generative AI models are pivotal in transforming data into meaningful content across various domains, including text generation, image creation, and speech synthesis. This project leverages LLMs to advance text generation capabilities, demonstrating their versatility in creative and practical applications.
The project utilizes a curated dataset comprising instruction-response pairs to fine-tune the Dolly-v2-3b model. This dataset enhances the model's ability to generate contextually relevant and coherent responses, tailored to specific user prompts and interactions.
By exploring the intricacies of LLM development, CPU inference optimization, and integration with Intel Extension for Transformers, this project aims to showcase the capabilities of modern AI technologies in enhancing user interactions through advanced text generation and chatbot development.
Large Language Models, Fine-tuning, CPU Inference, Intel Extension for Transformers, Text Generation, Custom Chatbot
For more details and to access the model, visit our https://github.com/KushagraIsTaken/Finetuning_Dolly-v2-3b_on_Alpaca_Dataset.