MLOps Adventure - Learning to Fine-tune LLMs, create datasets and neural nets

Table of contents

• Introduction
• Cloud GPU Services and Jupyter Notebooks
• Creating Custom Datasets
• Fine-tuning LLama 3.1
• Model Adapters: LoRA and QLoRA
• Building a RAG Pipeline on Custom Data
• Future Directions

Introduction

Neural networks fascinate me. As an application and infrastructure developer by background, I'm building side projects to get hands-on with neural networks, MLOps, and the intricacies of training models and building inference endpoints.

I'm going looking for tedium, frustration, and sharp edges and I'm rarely disappointed.

Cloud GPU Services and Jupyter Notebooks

I started by evaluating cloud GPU services for deep learning and fine-tuning.

I learned that while there are numerous options available, each comes with its own set of trade-offs in terms of pricing, performance, and ease of use.

Creating Custom Datasets

I created a guide on How to create a custom Alpaca instruction dataset for fine-tuning LLMs.

I learned that creating a good dataset is paramount. It involves careful consideration of the data structure and target model, ensuring diversity in the instruction-output pairs, and maintaining consistency in the formatting.

Fine-tuning LLama 3.1

With a custom dataset in hand, the next logical step was to fine-tune a large language model. I chose LLama 3.1 for this task and documented the process in How to Fine-tune LLama 3.1 on Lightning.ai with Torchtune.

I gained practical experience in:

• Preparing a model for fine-tuning
• Configuring hyperparameters
• Monitoring training progress

This was incredibly tedious. I encountered numerous errors, from out-of-memory issues to unexpected convergence problems. Each obstacle, however, deepened my understanding of the intricacies involved in training large language models.

Model Adapters: LoRA and QLoRA

Driven by Out of memory errors, I explored LoRA (Low-Rank Adaptation) and QLoRA (Quantized Low-Rank Adaptation), which I detailed in The Rich Don't Fine-tune Like You and Me: Intro to LoRA and QLoRA.

I learned how LoRA allows for efficient fine-tuning of large models by updating only a small number of parameters. QLoRA took this a step further by introducing quantization, making it possible to fine-tune models on consumer-grade hardware.

Implementing these techniques taught me about:

• The trade-offs between model performance and computational efficiency
• The importance of parameter-efficient fine-tuning methods
• The potential of quantization in democratizing access to large language models

Building a RAG Pipeline on Custom Data

One of my most comprehensive projects was building a Retrieval Augmented Generation (RAG) pipeline for my blog:

1. Full ML Pipeline Implementation: The project covers the entire lifecycle of an ML application, from data ingestion and processing to model deployment and serving.

2. Data Processing and Knowledge Base Creation: The project shows how to convert an entire blog (with MDX files) into a searchable knowledge base, highlighting the importance of data preparation in ML projects.

3. Real-time AI Interaction: By implementing a chat interface that interacts with the blog's content, the project showcases how to deploy ML models for real-time user interaction.

4. Streaming Responses and Frontend Integration: The implementation includes handling streaming responses from language models and integrating them seamlessly with a React frontend.

5. MLOps Best Practices: The project incorporates CI/CD practices, using GitHub Actions to automatically update the knowledge base when new blog posts are added.

6. Vector Search and Semantic Understanding: By using Pinecone for vector search, the project demonstrates how to implement semantic search capabilities in an ML application.

7. Prompt Engineering: The article discusses the nuances of crafting effective prompts for language models, an essential skill in working with LLMs.

8. Performance Optimization: The project addresses challenges like efficient data retrieval and processing, crucial for maintaining good performance in ML applications.

9. Scalability Considerations: By using cloud-based services and discussing potential improvements, the project touches on how to build scalable ML solutions.

This RAG pipeline project is a prime example of applying MLOps principles to create a practical, user-facing application. It combines several aspects of machine learning engineering, from data processing and model integration to deployment and user interface design.

Future Directions

Looking ahead, I'm excited to explore:

1. Computer Vision: Delving into image processing, object detection, and facial recognition tasks.
2. Edge AI / Tiny ML: Exploring the deployment of ML models on resource-constrained devices and edge computing scenarios.
3. Ensemble Methods: Investigating techniques like Random Forests or Gradient Boosting for improved model performance.
4. MLOps at Scale: Tackling the challenges of large-scale deployment, monitoring, and maintenance of ML systems in production environments.
5. Developing more sophisticated custom datasets for various domains.
6. Experimenting with different model architectures to solve complex problems.