metadata
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 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: