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LoRA for code fine-tuning

Q: LoRA for code fine-tuning

Use LoRA (Low-Rank Adaptation) to fine-tune large language models on code tasks by injecting trainable low-rank matrices into model weights, drastically reducing training costs and memory. Combine LoRA with 4-bit quantization (QLoRA) for efficient fine-tuning on code datasets using frameworks like transformers and peft.

Quick answer

Use LoRA (Low-Rank Adaptation) to fine-tune large language models on code tasks by injecting trainable low-rank matrices into model weights, drastically reducing training costs and memory. Combine LoRA with 4-bit quantization (QLoRA) for efficient fine-tuning on code datasets using frameworks like transformers and peft.

PREREQUISITES

Python 3.8+
pip install transformers>=4.30 peft bitsandbytes datasets torch
Basic knowledge of PyTorch and Hugging Face Transformers

Setup

Install required libraries for LoRA and QLoRA fine-tuning with code models. Set up environment variables for API keys if using hosted models.

bash

pip install transformers>=4.30 peft bitsandbytes datasets torch

Step by step

This example fine-tunes a code generation model using LoRA with 4-bit quantization (QLoRA) on a small code dataset. It shows loading a pretrained model, applying LoRA, preparing data, and training.

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import LoraConfig, get_peft_model, TaskType
from datasets import load_dataset

# Load pretrained code model (e.g., CodeLlama 7B)
model_name = "code-llama/CodeLlama-7b-hf"
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Load model with 4-bit quantization for efficiency
from transformers import BitsAndBytesConfig
quant_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=quant_config,
    device_map="auto"
)

# Configure LoRA for code fine-tuning
lora_config = LoraConfig(
    r=16,               # Low-rank dimension
    lora_alpha=32,      # Scaling factor
    target_modules=["q_proj", "v_proj"],  # Common for causal LM
    lora_dropout=0.05,
    task_type=TaskType.CAUSAL_LM
)

# Apply LoRA to the model
model = get_peft_model(model, lora_config)

# Load a small code dataset (e.g., CodeSearchNet subset)
dataset = load_dataset("code_search_net", "python", split="train[:1%]")

# Tokenize function
max_length = 512
def tokenize_function(examples):
    return tokenizer(examples["code"], truncation=True, max_length=max_length)

# Prepare dataset
tokenized_dataset = dataset.map(tokenize_function, batched=True)

# Data collator for causal LM
from transformers import DataCollatorForLanguageModeling
collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)

# Trainer setup
from transformers import Trainer, TrainingArguments
training_args = TrainingArguments(
    output_dir="./lora-code-finetune",
    per_device_train_batch_size=4,
    num_train_epochs=1,
    logging_steps=10,
    save_steps=100,
    evaluation_strategy="no",
    save_total_limit=1,
    fp16=True
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_dataset,
    data_collator=collator
)

# Train
trainer.train()

# Save LoRA adapters
model.save_pretrained("./lora-code-finetuned")

output

***** Running training *****
  Num examples = 1000
  Num Epochs = 1
  Instantaneous batch size per device = 4
  Total train batch size (w. parallel, distributed & accumulation) = 4
  Gradient Accumulation steps = 1
  Total optimization steps = 250
...
Training completed.
Saving model to ./lora-code-finetuned

Common variations

Use gpt-4o-mini or claude-sonnet-4-5 for code fine-tuning via API with LoRA adapters.
Apply QLoRA by combining BitsAndBytesConfig with LoraConfig for memory-efficient training.
Use async training loops or Hugging Face Accelerate for distributed setups.

Troubleshooting

If you get CUDA out-of-memory errors, reduce batch size or use gradient checkpointing.
Ensure bitsandbytes is installed correctly for 4-bit quantization support.
Verify target modules in LoraConfig match your model architecture (e.g., q_proj, v_proj for causal LMs).

✅

Key Takeaways

Use LoRA to fine-tune large code models efficiently by training low-rank adapters instead of full weights.
Combine LoRA with 4-bit quantization (QLoRA) to reduce GPU memory usage drastically.
Target q_proj and v_proj modules for causal language models during LoRA fine-tuning.
Use Hugging Face transformers and peft libraries for streamlined LoRA integration.
Adjust batch size and enable mixed precision to avoid out-of-memory errors during fine-tuning.

Verified 2026-04 · code-llama/CodeLlama-7b-hf, gpt-4o-mini, claude-sonnet-4-5

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