| --- |
| base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct |
| library_name: peft |
| license: apache-2.0 |
| language: |
| - en |
| tags: |
| - code |
| - python |
| - lora |
| - peft |
| - qwen2 |
| - code-generation |
| datasets: |
| - iamtarun/python_code_instructions_18k_alpaca |
| --- |
| |
| # my-python-coder |
|
|
| A LoRA fine-tune of [Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct) specialized for Python code generation. |
|
|
| This model was fine-tuned as a learning project to demonstrate the full workflow of taking a base model, training it on a custom dataset, and publishing it to the Hugging Face Hub. |
|
|
| ## Training Details |
|
|
| | Parameter | Value | |
| |---|---| |
| | **Base model** | `Qwen/Qwen2.5-Coder-1.5B-Instruct` | |
| | **Dataset** | `iamtarun/python_code_instructions_18k_alpaca` (first 1,500 examples) | |
| | **Method** | LoRA (r=16, alpha=32, target_modules=`all-linear`) | |
| | **Training steps** | 200 | |
| | **Learning rate** | 2e-4 | |
| | **Effective batch size** | 8 (batch=2 × grad_accum=4) | |
| | **Max sequence length** | 1024 | |
| | **Hardware** | Google Colab (NVIDIA T4, 16 GB VRAM) | |
| | **Training time** | ~33 minutes | |
|
|
| ## Prompt Format |
|
|
| This model was trained with the following instruction format. Using the same format at inference time will give the best results: |
|
|
|
|
| ## Usage |
|
|
| ```python |
| import torch |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| from peft import PeftModel |
| |
| # Load base model and LoRA adapter |
| base_model = AutoModelForCausalLM.from_pretrained( |
| "Qwen/Qwen2.5-Coder-1.5B-Instruct", |
| dtype=torch.float16, |
| device_map="auto", |
| ) |
| model = PeftModel.from_pretrained(base_model, "SathishKumar89/my-python-coder") |
| tokenizer = AutoTokenizer.from_pretrained("SathishKumar89/my-python-coder") |
| |
| # Prepare a prompt |
| prompt = """### Instruction: |
| Write a Python function that checks if a number is prime. |
| |
| ### Response: |
| """ |
| |
| inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
| outputs = model.generate(**inputs, max_new_tokens=200, do_sample=False) |
| print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
| |
| ### Instruction: |
| Write a Python function that checks if a number is prime. |
| |
| ### Response: |
| def is_prime(num): |
| # Check for 0 and 1 |
| if num <= 1: |
| return False |
| |
| # Check for even numbers greater than 2 |
| elif num == 2: |
| return True |
| elif num % 2 == 0: |
| return False |
| |
| # Check for odd numbers greater than 3 |
| else: |
| for i in range(3, int(num**0.5) + 1, 2): |
| if num % i == 0: |
| return False |
| return True |
| |
