LoRA-CodeExplainer is a lightweight fine-tuning project that uses LoRA (Low-Rank Adaptation) to adapt a pretrained CodeT5-small model for code explanation tasks. The model takes a Python code snippet as input and generates a human-readable explanation in English.
This project is designed as an entry-level LoRA fine-tuning experiment with ~900 simple Python examples, making it perfect for learning LoRA-based LLM adaptations.
- Format: JSON
- Each entry:
{
"input": "def add(a, b):\n return a + b",
"output": "A function that adds two numbers."
}- Language: English
- Examples: ~900 simple Python functions (math, strings, loops, lists, recursion, etc.)
pip install transformers peft datasetsfrom datasets import load_dataset
dataset = load_dataset("json", data_files="dataset_en.json")from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model_name = "Salesforce/codet5-small"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)from peft import LoraConfig, get_peft_model
lora_config = LoraConfig(
r=8,
lora_alpha=32,
target_modules=["q", "v"],
lora_dropout=0.05,
bias="none",
task_type="SEQ_2_SEQ_LM"
)
model = get_peft_model(model, lora_config)Use Hugging Face Trainer or your preferred training loop:
from transformers import Trainer, TrainingArguments
training_args = TrainingArguments(
output_dir="./lora_codet5",
per_device_train_batch_size=8,
num_train_epochs=12,
save_steps=500,
logging_steps=50,
learning_rate=3e-4,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=dataset["train"]
)
trainer.train()- Learn LoRA fine-tuning on a small dataset
- Understand adapting pretrained LLMs for code tasks
- Produce human-readable explanations of code snippets
- Dataset is intentionally simple to reduce training time
- The project can be extended to larger datasets and more complex code
- Fine-tuned model can be deployed for code summarization or code documentation generation
MIT