---
language:
- en
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
tags:
- code
- python
- educational
- lora
- qwen
library_name: peft
---

# Qwen2.5-Coder-1.5B-Educational (LoRA)

LoRA adapter for [Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct) fine-tuned on educational code generation.

## Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
"Qwen/Qwen2.5-Coder-1.5B-Instruct",
device_map="auto"
)

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "YOUR_USERNAME/qwen-coder-1.5b-educational")
tokenizer = AutoTokenizer.from_pretrained("YOUR_USERNAME/qwen-coder-1.5b-educational")

# Generate code
prompt = "Instruction: Write a Python function to reverse a string
Réponse:
"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs, skip_special_tokens=True))

## Training Details

- **Method**: LoRA (r=8, alpha=16, dropout=0.05)
- **Dataset**: OpenCoder-LLM/opc-sft-stage2 (educational_instruct)
- **Steps**: 2000
- **Final Loss**: 0.530
- **Hardware**: TPU v6e-16
- **Training Time**: 43 minutes

## Performance

Improved over base model on:
- Educational Python code generation
- Pythonic idioms and patterns
- Object-oriented architecture
- Code documentation and comments

## License

Apache 2.0