---
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: