Fine-Tuning Small Language Models with Python
summary
Shows that small language models (0.5–13B params) can match or beat large LLMs for domain tasks using concrete tooling and datasets: trains a 1B model on 120k news titles (one epoch) and reports an ~8% gain over GPT-style outputs for topic classification, cites leaderboards where 3B models outperform GPT-4 by ~14% and 8B by ~20%. Walks through dataset formatting, tokenization, Axolotl orchestration, adapter tuning with PEFT/LoRA, and training with Transformers + Accelerate, plus multi‑GPU runs on Ranpod and Colab timings (~50 minutes on 1B).
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