This repo is a genuinely useful find for anyone who has ever stared at a Hugging Face tutorial and thought, "Okay, but how do these pieces actually fit together in a real project?" It's a three-stage LLM fine-tuning playbook that walks through the full modern recipe end-to-end, demonstrated on a pharmaceutical corpus.

The three stages mirror what frontier labs actually do:

• Continued pretraining (non-instruction) — adapting the base model to a new domain's vocabulary and style.
• Supervised fine-tuning (SFT) — teaching the model to follow instructions in that domain.
• Direct Preference Optimization (DPO) — aligning outputs to preferred responses without needing a reward model.

The toolchain on display is the current state of the art for the GPU-poor: TinyLlama as the base, Unsloth for accelerated training, QLoRA/LoRA via PEFT for memory-efficient adapters, and TRL for the DPO loop. Picking a pharma corpus is a smart choice too — it's a domain with enough specialized jargon that you can actually see domain adaptation working, rather than fine-tuning on yet another generic chat dataset where improvements are hard to measure.

What makes this stand out from the sea of "fine-tune your LLM in 5 minutes" notebooks is the staged pipeline. Most tutorials show one technique in isolation; this one shows how continued pretraining feeds SFT feeds DPO, which is the actual workflow teams use when adapting open models to enterprise use cases.

Useful for: ML engineers evaluating open-model fine-tuning for vertical domains, researchers benchmarking PEFT methods, and anyone preparing for ML interviews where these techniques come up constantly.