KpRT/qwen-python-finetuned

Qwen2.5-Coder-0.5B Python Fine-tuned

Fine-tuned version of Qwen/Qwen2.5-Coder-0.5B-Instruct for Python code generation.

Model Details

• Base Model: Qwen/Qwen2.5-Coder-0.5B-Instruct
• Fine-tuning Method: QLoRA (4-bit quantization + LoRA adapters)
• Dataset: iamtarun/python_code_instructions_18k_alpaca
• Task: Python code generation from natural language instructions

Training Details

• Training Samples: 16000
• Validation Samples: 1000
• Epochs: 3
• Training Time: N/A
• Final Loss: N/A

Performance

• Syntax Validity: 95.2%
• Pass@1: 54.4%
• Verbosity Reduction: 95%

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("KpRT/qwen-python-finetuned")
tokenizer = AutoTokenizer.from_pretrained("KpRT/qwen-python-finetuned")

prompt = "Write a function to reverse a string"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
code = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(code)

Citation

If you use this model, please cite:

@misc{qwen-python-finetuned,
  author = {K R T},
  title = {Qwen2.5-Coder Python Fine-tuned},
  year = {2026},
  publisher = {HuggingFace},
  url = {https://huggingface.co/KpRT/qwen-python-finetuned}
}