---
language: ko
license: mit
metrics:
- perplexity
- accuracy
tags:
- korean
- qwen
- finetunned
dataset_tags:
- kyujinpy/KOpen-platypus
---

# Qwen 2.5 3B Instruction-tuned Model

This model is a Instruction-tuned version of Qwen 2.5 3B for recipie recommandation.

## Model Description

- Fine-tuned from: Qwen/Qwen2.5-3B
- Fine-tuning task: [Instruction-tuning]
- Training data: [kyujinpy/KOpen-platypus + Recipe data]
- Evaluation results: [Add your evaluation metrics]

## Usage

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

base_model_path = "Qwen/Qwen2.5-3B"
adapter_model = "INo0121/qwen2.5_3b_instruction_tuning_241020"
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_path,
    torch_dtype="auto",
    device_map="auto",
    temperature=0.1
)
model = PeftModel.from_pretrained(base_model, adapter_model)
tokenizer = AutoTokenizer.from_pretrained(adapter_model)


# Example usage
input_text = "Your input text here"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Limitations and Biases

[Describe any known limitations or biases of your model]

## Training Details

- Training framework: Hugging Face Transformers
- Hyperparameters: [List your key hyperparameters]
- Training hardware: [Describe the hardware used]