Llama 3.1 fine-tuning guide goes rogue

The main takeaway is that post-training can steer a base model’s behavior far more than many people expect, especially when the dataset is tightly structured. That makes this a strong technical demo, but also a sharp reminder that “safety” is brittle once a model is adapted carelessly.

• –Covers a practical SFT stack: Kaggle GPU setup, Unsloth, LoRA, and TRL
• –Uses a historical text corpus to illustrate how instruction-response tuning can reshape outputs
• –Reinforces the distinction between changing model behavior and adding new knowledge
• –The notebook format lowers friction for learners who want to run the example themselves
• –More interesting as a fine-tuning lesson than as a product release