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Tharun007/
qwen2-7b-code

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Qwen2-7B-Instruct Fine-tuned for Code Improvement

This repository contains a fine-tuned version of Qwen2-7B-Instruct specifically optimized for analyzing and fixing buggy code. The model was fine-tuned using the Parameter-Efficient Fine-Tuning (PEFT) approach with LoRA on the Python subset of the CommitPackFT dataset.

Model Details

Base Model: Qwen/Qwen2-7B-Instruct
Fine-tuning Method: LoRA (Low-Rank Adaptation)
Task: Code improvement and bug fixing
Dataset: bigcode/commitpackft (Python subset)
Training Format: Instruction-Response pairs

LoRA Configuration

The model was fine-tuned using the following LoRA hyperparameters:

python

lora_config = LoraConfig(
    r=16,              # Rank
    lora_alpha=32,     
    target_modules=["q_proj", "v_proj"],  # LoRA on attention layers
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM"
)

Training Details

Training Data: 5% of the Python subset of CommitPackFT
Batch Size: 2 per device with gradient accumulation steps of 8
Learning Rate: 2e-4
Epochs: 3
Precision: Mixed precision (fp16)
Hardware: 4-bit quantization for memory efficiency

Usage

Loading the Model

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model and tokenizer
model_name = "Qwen/Qwen2-7B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token

# Load model with adapter weights
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    load_in_4bit=True,
    trust_remote_code=True
)

# Load LoRA adapter
adapter_path = "PATH_TO_ADAPTER"  # Update with your model path
model = PeftModel.from_pretrained(model, adapter_path)

Example Inference

python

def improve_code(code, max_new_tokens=200):
    # Format prompt in the same way as training
    prompt = f"### Instruction:\nFix the following buggy code:\n{code}\n\n### Response:\n"
    
    # Tokenize
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    
    # Generate
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=max_new_tokens,
            temperature=0.7,
            do_sample=True,
            pad_token_id=tokenizer.eos_token_id,
            repetition_penalty=1.1
        )
    
    # Decode only the generated part
    generated_text = tokenizer.decode(
        outputs[0][inputs.input_ids.shape[1]:], 
        skip_special_tokens=True
    )
    
    return generated_text

# Example usage
buggy_code = """
def calculate_average(numbers):
    return sum(numbers) / len(numbers)
"""

improved_code = improve_code(buggy_code)
print(improved_code)

Limitations

The model was fine-tuned on a small subset (5%) of the Python data in CommitPackFT
Performance might be limited on non-Python programming languages
Code fixes are based on patterns seen in the training data and may not address all bugs correctly
As with all code generation models, human review is essential for any generated code

Fine-tuning Process

The model was fine-tuned using the Hugging Face Transformers library with the following process:

Load the Qwen2-7B-Instruct model with 4-bit quantization for memory efficiency
Apply LoRA for parameter-efficient fine-tuning, targeting only query and value projection matrices
Format training data as instruction-response pairs
Train for 3 epochs with a batch size of 16 (2 per device × 8 gradient accumulation steps)
Save the LoRA adapter weights for efficient deployment

Citation

If you use this model in your research, please cite:

bibtex

@misc{qwen2-7b-code-improvement,
  author = {Tharun Kumar},
  title = {Qwen2-7B-Instruct Fine-tuned for Code Improvement},
  year = {2025},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/Tharun007/qwen2-7b-code}}
}

License

This model adapter is subject to the license of the original Qwen2-7B-Instruct model. Please refer to the Qwen2-7B-Instruct model card for license details.

Contact

GitHub