from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-0.5B-Instruct")

# Load LoRA adapters
model = PeftModel.from_pretrained(base_model, "alpayH/qwen2-0.5b-lora-opencodeinstruct")

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("alpayH/qwen2-0.5b-lora-opencodeinstruct")

# Generate code
prompt = "### Instruction:\nWrite a Python function to reverse a string\n\n### Response:\n"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=512)
print(tokenizer.decode(outputs[0]))

Training Details

Base Model: Qwen/Qwen2-0.5B-Instruct
Fine-tuning Dataset: OpenCodeInstruct (300 samples)
Fine-tuning Method: LoRA (Low-Rank Adaptation)
LoRA Rank: 16
LoRA Alpha: 32

Learning Rate: 2e-4
Batch Size: 16 (effective, with gradient accumulation)
Epochs: 3
Precision: bfloat16

Evaluation

This model has been evaluated on LiveCodeBench. See the main repository for evaluation results.

License

Apache 2.0