Isaac_Muturi_assessment

**Project 1: Fine-Tuning a Hugging Face Transformer Model for Code Generation** In this project, I embarked on the journey of fine-tuning a powerful Hugging Face Transformer model for code generation. Leveraging the CodeAlpaca 20k dataset, I utilized Progressive Embedding Fine-Tuning (PEFT) and quantization techniques to train this Large Language Model. The process began with the pre-processing of the model to optimize memory usage and then proceeded to the fine-tuning phase. The model was exposed to various code-related prompts, enhancing its ability to generate code snippets effectively. This project resulted in a well-trained model capable of generating code based on natural language prompts, opening the door to applications in code automation and software development. **Project 2: AI-Powered Data Extraction and Content Retrieval with GenAI Stack** In my second project, I harnessed the capabilities of GenAI Stack, a versatile framework for AI-based content retrieval and data extraction. By integrating various components, including Langchain ETL for data extraction, Hugging Face Embeddings for natural language understanding, and Langchain Retriever for content retrieval, I developed a robust system for answering specific queries. The project involved the extraction of information from online sources, making it invaluable for information retrieval tasks. Furthermore, the system's ability to understand and answer natural language questions demonstrates its potential in various domains, from chatbots to knowledge management systems. This project opens the door to AI-powered content extraction and retrieval solutions, paving the way for more advanced applications of AI in information management.