Axolotl

Answer-first: For 80% of domain enterprise tasks, fine-tuned Small Language Models (1B–14B) match frontier performance at 1/50th inference cost and sub-40ms latency. This playbook provides the production engineering blueprint: synthetic data curation, QLoRA fine-tuning with Axolotl on 24GB GPUs, DeepSeek-R1 reasoning distillation, DPO/GRPO alignment, and vLLM continuous batching deployment. 🇻🇳 Read the Vietnamese version of this series on 📖 Bản tiếng Việt (Vietnamese Edition) 🎯 Series Overview: Why Small Language Models in 2026? Relying exclusively on proprietary frontier API models (GPT-4.5, Claude 3.5 Sonnet) introduces three fatal enterprise vulnerabilities: ...

← Previous Chapter: Part 1: Hybrid AI Architecture | Series Hub | Next Chapter: Part 3: QLoRA & Axolotl Fine-Tuning → Prerequisite: Read Part 1: Hybrid AI Architecture & Self-Hosting vLLM for inference routing and self-hosted gateway topology. Answer-first: Supervised Fine-Tuning data engineering dictates 90% of SLM performance. Following the LIMA paradigm, 3,500 curated instruction samples outperform 100,000 noisy scraped records. Injecting uniform embedding noise via NEFTune provides an 18.4% AlpacaEval gain against rote memorization, while SemDeDup clustering eliminates 45% semantic redundancy without accuracy loss, cutting GPU training hours by 50%. ...

← Previous Chapter: Part 2: SFT Data Engineering | Series Hub | Next Chapter: Part 4: Knowledge Distillation → Prerequisite: Read Part 2: SFT Data Engineering — NEFTune & Synthetic Data Curation for instruction dataset curation and decontamination. Answer-first: QLoRA compresses base model weights into 4-bit NormalFloat (NF4) representations while computing gradients exclusively through 16-bit adapter matrices. Combining Double Quantization with CUDA Paged Optimizers enables fine-tuning 14B models on a single 24GB commodity GPU (RTX 4090 or L4) at $1.20/hour, preserving 99.3% full-precision benchmark performance while preventing out-of-memory crashes. ...