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README.md

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+---
+language: en
+tags:
+- qwen2
+- lora
+- fine-tuned
+- code-generation
+- opencodeinstruct
+license: apache-2.0
+base_model: Qwen/Qwen2-0.5B
+---
+
+# Qwen2-0.5b LoRA Fine-tuned on OpenCodeInstruct
+
+This model is a LoRA fine-tuned version of Qwen/Qwen2-0.5B on the OpenCodeInstruct dataset.
+
+## Model Details
+
+- **Base Model**: Qwen/Qwen2-0.5B
+- **Fine-tuning Dataset**: OpenCodeInstruct (300 samples)
+- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
+- **LoRA Rank**: 16
+- **LoRA Alpha**: 32
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+# Load base model
+base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-0.5B")
+
+# 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
+
+- **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