LLM Models Fine-Tuning for QA Generation (Low budget. Study purpose) -- 3

Hi. I'm ML engineer specializing in NLP. I've already done projects like these for my own educational purposes as well as in production. By the way, your project seems to be not hard, it would be ideal for building reputation on this platform so I hope you choose me as your contractor :)

As an experienced web developer with a focus on the MERN stack, I am confident that I can optimally handle your LLM model fine-tuning project. Throughout my career, I have consistently shown proficiency in building responsive, high-performance web applications and SPAs — skills that directly convert to the task at hand. Working with RESTful APIs & GraphQL using Redux and Context API for state management, I couple these qualifications with a deep familiarity of Node.js and MongoDB - crucial for efficient question-answer generation. It's worth mentioning that I also bring a unique advantage of being proficient in Python which sets me apart from other bidders. My solid grasp on the language will enable me to effectively prepare the fine-tuning dataset, conduct thorough evaluations of the before and after performance using suitable metrics, and to finally extend these improvements to elaborate online quizzes showcasing your project effectively Furthermore, my competence isn't limited to just development. I've been providing research services tailored to the client's requirements for a long time and can leverage this expertise while preparing the extensive project report you need. With me on board, you're promised a 100% aligned solution that satisfies both your budget constraints and study goals without compromising on quality and efficacy.

As a PhD researcher in the relevant field, I believe I’m well-suited person for this task. Below is my proposal: MILESTONE 1: MODEL CHOICE: DistilBERT or TinyLlama, both efficient for local CPU use and capable of fine-tuning of various NLP tasks. • CAPABILITIES: The model will generate diverse question formats including True/False, Multiple Choice, Match the Following, Fill in the blanks. • PROMPT ENGINEERING: Techniques of zero-shot, one-shot, and few-shot learning will be demonstrated. • OFFICIAL DOCUMENTATION: DistilBERT GitHub & TinyLlama Documentation MILESTONE 2: • DATASET PREPARATION: I will curate a comprehensive dataset suitable for fine-tuning, covering a wide range of topics. • FINE-TUNING EXECUTION: The model will be fine-tuned, with performance metrics (graphs of training, evaluation, test loss) provided. • EVALUATION COMPARISON: I will show noticeable improvements before and after fine-tuning and store the results in a database like SQLite or MongoDB. • OFFICIAL DOCUMENTATION: Hugging Face Fine-Tuning Guides MILESTONE 3: • REPORT PREPARATION: A detailed 25-page technical report will cover model selection rationale, dataset preparation, fine-tuning details, evaluation results, and conclusions. MILESTONE 4: I will develop an interactive quiz using Gradio, Streamlit, or Flask to display questions and track user progress. TIMELINE & COMMUNICATIONS: Within 1 week, I'm committed to regular updates and welcome feedback to ensure your expectations are met. Thanks

Hi! How are you doing? I understand you are looking for a fine-tuning expert for LLM models. After reading the details, I am confident that I can help you with selecting a model, preparing datasets, and creating a detailed project report. Please initiate the chat for further discussions and my previous work.

⭐⭐I read your project and I UNDERSTAND YOUR REQUIREMENTS 100%⭐⭐ Hi there. Very happy to your project. Reading your project, I notice that you are looking for talent developer. I am very familiar with PHP, JavaScript, CSS, HTML, Laravel, Codeigniter, React.js, Vue.js, Angular.js, MySQL, PostgreSQL, Node.js, Python, Django, Flask..... I have 8+ years experience in these tech skills. So I am pretty sure I can build whatever that you want. Let's discuss more about the project by chatting. Looking forward to work with you. Sincerely.

I am an experienced Machine Learning Engineer specializing in the development of intelligent systems using large language models (LLMs). I am currently working on a project that leverages the LLaMA model to create a document question-answering chatbot. This chatbot is designed to provide accurate and context-aware responses by converting scanned PDFs into searchable PDFs through OCR (Optical Character Recognition) and then transforming these searchable PDFs into a vector store. Scanned PDF Conversion: I will implement OCR technology to accurately convert scanned PDFs into searchable PDFs, ensuring that all textual content is fully indexed and accessible for further processing. Vector Store Creation: The searchable PDFs will then be processed and converted into a vector store, which will serve as the knowledge base for the question-answering system. This vector store will enable efficient and accurate retrieval of information in response to user queries. Chatbot Development Using Chainlit, Streamlit, and Gradio: I will develop the chatbot interface using Chainlit, Streamlit, and Gradio. Question-Answering with LLaMA Model: Leveraging the power of the LLaMA model, the chatbot will be capable of understanding and answering questions based on the documents. The chatbot will be designed to deliver precise, contextually relevant responses, enhancing user experience and productivity.

Objective: Fine-tune a small LLM to generate diverse question types from given texts. Approach: 1. Select LLM (e.g... TinyLlama, DistilBert). 2. Prepare dataset. 3. Train LLM. 4. Evaluate model. 5. Create user interface. Deliverables: . Fine-tuned LLM. . Report. . User interface Benefits: 1. Improved question preparation. 2. Efficient solution. I am confident in my ability to deliver. Please contact me to discuss further.