Building small language models end to end on free compute, and measuring them honestly - tokenizer, pretraining, post-training, QLoRA, DPO and quantization written by hand. Most interested in evaluation that doesn't flatter the model: KL against a reference rather than perplexity alone, a measured seed noise floor before calling an ablation a win, and publishing the negative results.