Summary
This part explored how large language models are trained and deployed, from foundational pre-training through alignment techniques to recent developments.
We studied pre-training and fine-tuning. Pre-training on massive text corpora teaches models general language patterns, requiring enormous computational resources. Fine-tuning adapts models to specific tasks using smaller, targeted datasets.
We examined instruction tuning and RLHF. Instruction tuning trains models to follow explicit instructions. RLHF refines behavior through human ratings, training a reward model, and using reinforcement learning to optimize toward human preferences. This alignment transformed base models into helpful assistants like ChatGPT.
We traced the rise of GPT and open-source models from early developments through GPT-3’s 175 billion parameters to ChatGPT’s mainstream breakthrough (late 2022). The current landscape includes proprietary models (GPT-5, Claude, Gemini) and competitive open-source alternatives (LLaMA, Mistral).
Finally, we examined recent trends: reasoning models using extended inference-time computation (OpenAI o1, DeepSeek-R1), efficiency techniques like Mixture of Experts, multimodal capabilities integrating text, images, and audio, and systems integration through RAG and tool use.
Key Takeaways
- Pre-training on massive corpora creates versatile base models learning general language patterns.
- Fine-tuning adapts models to specific tasks using smaller datasets.
- Instruction tuning and RLHF align models with human preferences, creating helpful assistants.
- GPT evolution progressed from research tools to mainstream applications, with ChatGPT marking widespread adoption.
- Open-source models have become increasingly competitive, democratizing access.
- Recent trends include reasoning models, efficiency techniques, multimodal capabilities, and systems integration.