license: apache-2.0
base_model: Qwen/Qwen3-4B-Instruct-2507
library_name: peft
tags:
- lora
- sft
- dpo
- knowledge-distillation
- fine-tuning
- it-support
Model Adaptation Book — companion models
Trained artifacts for the book LLM Customization and Fine-Tuning: Adaptation, Distillation, and Alignment (Manning). Code: https://github.com/bahree/ModelAdaptationBook
All are adaptations of Qwen/Qwen3-4B-Instruct-2507 on a real IT-support
dataset: Stack Exchange IT Q&A (Super User, Ask Ubuntu, Server Fault;
CC-BY-SA-4.0) plus a small Databricks Dolly slice (CC-BY-SA-3.0) for
general-capability retention. Each chapter's artifact is a subfolder, so you
can follow along on any machine (including Apple Silicon) by pulling a trained
model and running inference/eval, without training it yourself.
| Subfolder | Chapter | What | Base |
|---|---|---|---|
ch5-lora |
5 | LoRA adapter | Qwen3-4B-Instruct-2507 |
ch6-sft |
6 | full SFT model (standalone) | (full fine-tune) |
ch7-distilled |
7 | distilled student (LoRA) | Qwen3-4B-Instruct-2507 |
ch8-dpo |
8 | full DPO model (standalone) | (full fine-tune) |
ch8-dpo-lora |
8 | LoRA-DPO adapter (single-card path) | ch6-sft |
Load a full model:
from transformers import AutoModelForCausalLM
m = AutoModelForCausalLM.from_pretrained("bahree/ModelAdaptationBook", subfolder="ch6-sft")
Load an adapter (on its base):
from transformers import AutoModelForCausalLM
from peft import PeftModel
base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B-Instruct-2507")
m = PeftModel.from_pretrained(base, "bahree/ModelAdaptationBook", subfolder="ch5-lora")
Training these needs a CUDA 24 GB+ GPU (and the Ch8 full DPO uses multiple
GPUs; the ch8-dpo-lora adapter is the single-card alternative). Inference
and evaluation fit a single smaller GPU or Apple Silicon (MPS). See the book
repo for exact commands, datasets, and full attribution.