---
datasets:
- Intel/orca_dpo_pairs
base_model:
- Qwen/Qwen2.5-0.5B-Instruct
license: apache-2.0
---
# Fine-tuned Qwen/Qwen2.5-0.5B-Instruct Model

## Model Overview

This is a fine-tuned version of the Qwen/Qwen2.5-0.5B-Instruct model. The fine-tuning process utilized the Intel/orca_dpo_pairs dataset and applied DPO (Direct Preference Optimization) and LoRA (Low-Rank Adaptation) techniques.

**Note**: This fine-tuning was done following the instructions in [this blog](https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac).

## Fine-tuning Details

- **Base Model**: Qwen/Qwen2.5-0.5B-Instruct
- **Dataset**: Intel/orca_dpo_pairs
- **Fine-tuning Method**: DPO + LoRA

## Usage Instructions

### Install Dependencies

Before using this model, make sure you have the following dependencies installed:

```bash
pip install transformers datasets
```

### Load the model

```python
import transformers
from transformers import AutoConfig, AutoModel, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("drive/MyDrive/result/Qwen-DPO")

message = [
    {"role": "system", "content": "You are a helpful assistant chatbot."},
    {"role": "user", "content": "What is a Large Language Model?"}
]
prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)

pipeline = transformers.pipeline(
    "text-generation",
    model="co-gy/Qwen-DPO",
    tokenizer=tokenizer
)

sequences = pipeline(
    prompt,
    do_sample=True,
    temperature=0.7,
    top_p=0.9,
    num_return_sequences=1,
    max_length=200,
)
print(sequences[0]['generated_text'])
```