# CineGuide: Conversational Movie Recommendation Assistant

## Model Description
This model is a fine-tuned version of Qwen2.5-7B-Instruct for conversational movie recommendations.

## Training Details
- **Base Model**: Qwen/Qwen2.5-7B-Instruct
- **Method**: LoRA (Low-Rank Adaptation) with rank-16
- **Dataset**: ReDial corpus (7999 training examples)
- **Training Loss**: 0.9140
- **Perplexity**: 2.49

## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("./cineguide-merged")
model = AutoModelForCausalLM.from_pretrained("./cineguide-merged")

# Generate movie recommendations
prompt = "I love sci-fi movies with complex plots. Any recommendations?"
# ... [generation code]
```

## Performance
The model shows significant improvement over the base model in:
- Providing specific movie recommendations with rationales
- Maintaining conversational context
- Understanding genre preferences
- Giving compelling explanations for recommendations

Created for CS515 Deep Learning Course Project by Serhan Yilmaz (00031275)