LoRA Fine-Tune a Chat Model

Load a 4-bit Qwen model, attach LoRA adapters, format instructions, run SFT, and generate with the tuned model.

5 parts20 individual lessons1.7 estimated hours100 XP available

LoRA Fine-Tune a Chat Model project artwork

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Every source step is its own lesson with intuition, concepts, correctly rendered MathJax mathematics, implementation, tests, mistakes, and a checkpoint.

PART 1

Load the Quantized Base Model

Load the 4-bit Qwen2.5 model and tokenizer, inspect parameter count, verify quantization, and fix the pad token.

0/4

001load base model and tokenizer+5 XP 002count total parameters+5 XP 003is model 4bit quantized+5 XP 004ensure pad token+5 XP

PART 2

Attach LoRA Adapters

Select attention target modules, wrap the model with LoRA, and measure how many parameters become trainable.

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005get lora target modules+5 XP 006attach lora adapters+5 XP 007count trainable parameters+5 XP 008trainable fraction+5 XP

PART 3

Build the Instruction Dataset

Create example instruction/response pairs, format them into training strings, build a Dataset, and sanity-check tokenization.

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009build instruction examples+5 XP 010format instruction example+5 XP 011format all examples+5 XP 012build text dataset+5 XP 013tokenize text+5 XP 014count tokens+5 XP

PART 4

Run the SFT Training Loop

Configure TrainingArguments, construct an SFTTrainer, and run a few optimization steps to get a training loss.

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015build training arguments+5 XP 016build sft trainer+5 XP 017run sft training+5 XP

PART 5

Inference with the Tuned Model

Switch the model to inference mode, build a chat-template prompt, and generate and decode a reply.

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018switch to inference mode+5 XP 019build chat prompt+5 XP 020generate reply+5 XP