UPDATED: Comprehensive Learning Path for Training and Fine-Tuning Locally Hosted AI Models

Jarkko Iso-Kuortti Lead IT Specialist @ Q-Factory Oy | Quality & Test Management Expert

Introduction

The field of large language models (LLMs) is evolving rapidly, with new advancements such as OpenAI's o3, Google's Gemma series, Meta's LLaMA 3.1, and DeepSeek's LLM offering cutting-edge capabilities. This guide provides a structured learning path covering everything from foundational AI knowledge to advanced fine-tuning and deployment techniques.

1. Foundational Knowledge

Objective: Build a strong foundation in machine learning and deep learning concepts.

Recommended Courses:

• Machine Learning (Andrew Ng, Coursera)
• Deep Learning Specialization (Andrew Ng, Coursera)
• Fast.ai Practical Deep Learning for Coders

Recommended Books:

• "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
• "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron

2. Introduction to Large Language Models (LLMs)

Objective: Understand the core principles and applications of modern LLMs.

Key Research Papers & Articles:

• Attention Is All You Need (Vaswani et al.) – Transformer model foundation
• BERT: Pre-training of Deep Bidirectional Transformers
• GPT-4 Technical Report (OpenAI)
• Scaling Laws for Neural Language Models (Kaplan et al.)

Recommended Courses:

• NLP Specialization (Coursera)
• Hugging Face NLP Course

3. Advanced Topics in LLMs

Objective: Dive deeper into architecture, scaling, and optimization techniques.

Important Research Papers:

• LLaMA 3.1: Open and efficient foundation language models from Meta AI
• Fine-Tuning Language Models from Human Preferences (Reinforcement Learning with Human Feedback - RLHF)
• Parameter-Efficient Fine-Tuning Techniques (PEFT) – LoRA, QLoRA, and Adapters
• OpenAI o3: New reasoning-based architecture for improved logical problem-solving
• DeepSeek LLM: Efficient model challenging top-tier AI research firms

Workshops & Tutorials:

• Hugging Face Transformer Tutorials
• Google's TensorFlow and BERT Tutorials
• DeepSpeed & Megatron-LM for scaling LLMs efficiently

4. Practical Application: Training and Fine-Tuning

Objective: Learn hands-on training and fine-tuning techniques for LLMs.

Hands-On Tutorials:

• Hugging Face Course: Fine-Tuning Transformers
• Google Colab Notebooks: Fine-Tuning BERT & LLaMA 3.1
• Using LoRA and QLoRA for efficient fine-tuning

Key Tools & Frameworks:

• Hugging Face Transformers https://huggingface.co/learn/nlp-course/chapter1/1
• TensorFlow & PyTorch
• DeepSpeed & FSDP (Fully Sharded Data Parallel) for optimizing large-scale training

5. Specialized Training on Gemma and LLaMA 3.1

Objective: Master the specifics of Gemma and LLaMA 3.1 models.

Vendor Documentation & Tutorials:

• Google DeepMind Gemma Documentation
• Meta AI LLaMA 3.1 Documentation

Workshops & Webinars:

• Attend live sessions from Meta AI, Google, and Hugging Face
• Join model-specific communities (e.g., LLaMA & Gemma Discord servers)

6. Experimentation and Real-World Projects

Objective: Apply knowledge through real-world projects and collaborations.

Project Ideas:

• Fine-tune LLaMA 3.1 for a domain-specific application (e.g., legal, medical, finance)
• Develop a Gemma-powered chatbot and integrate it into a web application
• Benchmark Gemma vs. LLaMA 3.1 vs. OpenAI o3 on different datasets

Repositories & Collaboration:

• Contribute to open-source projects
• Participate in AI hackathons (Kaggle, Meta AI Challenges)
• Join research forums & communities (Reddit r/LocalLLMs, AI Discord groups)

7. Continuous Learning and Staying Updated

Objective: Keep pace with rapid advancements in AI and LLMs.

Follow Leading AI Researchers & Institutions:

• Twitter/X & LinkedIn updates from Meta AI, OpenAI, Google DeepMind
• DeepLearning.AI 'The Batch' Newsletter
• Arxiv Sanity Preserver for AI research papers

Conferences & Meetups:

• NeurIPS
• ICLR
• ACL
• Hugging Face Meetups & AI Research Seminars

Conclusion

The AI industry is rapidly advancing, and continuous learning is crucial for anyone working with LLMs like Gemma, LLaMA 3.1, OpenAI o3, and DeepSeek. By following this structured learning path, you can build expertise in training, fine-tuning, and deploying these cutting-edge models. Engage with the community, work on practical projects, and stay updated with the latest research to remain at the forefront of AI development.