AI & ML
CodeMate
A hands-on coding-model fine-tuning study.
The problem
Adapting a general language model to coding tasks needs a reproducible path from instruction examples to training artifacts. Parameter-efficient fine-tuning makes that workflow more approachable.
The approach
- Convert CodeAlpaca-20k instruction, input, and response examples into consistent JSONL training records.
- Load Gemma in 4-bit precision and configure PEFT LoRA adapters on query and value projections.
- Use gradient accumulation during training and save the model and tokenizer artifacts for subsequent inference experiments.
How it fits together
CodeAlpaca-20k
JSONL examples
4-bit Gemma
LoRA adapters
Saved artifacts
The result
A reproducible workflow for parameter-efficient Gemma fine-tuning on code instruction data.
The engineering decision
Parameter-efficient fine-tuning reduces training memory requirements while keeping the base model frozen. Training data quality and evaluation remain essential to useful coding assistance.