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license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B-Instruct
tags:
- lora
- peft
- conversational
- chatbot
- taylor-swift
- roleplay
- qwen
language:
- en
pipeline_tag: text-generation
---
# TaylorSwiftChatbot 🎸✨
A LoRA fine-tuned version of **Qwen2.5-0.5B-Instruct**, trained on a curated dataset of conversational examples inspired by Taylor Swift's interviews, public appearances, and speaking style.
> ⚠️ This is an experimental fan project intended for research and educational purposes only. It is not affiliated with or endorsed by Taylor Swift.
---
## Model Details
- **Base Model:** Qwen/Qwen2.5-0.5B-Instruct
- **Fine-Tuning Method:** LoRA (PEFT)
- **Training Hardware:** NVIDIA RTX 3050 Laptop GPU (6GB VRAM)
- **Training Time:** ~15 minutes
- **Dataset Size:** ~367 conversational examples
- **Epochs:** 5
---
## Goal
The goal of this project is to explore whether a small language model can learn:
- Conversational tone
- Storytelling style
- Emotional responses
- Interview mannerisms
- Personality traits and speaking patterns
This model focuses on **style imitation**, not factual knowledge.
---
## Current Status
Version 1 is an early prototype.
### Strengths
✅ Captures some aspects of Taylor's reflective and conversational tone.
✅ Produces longer and more personal responses than the base model.
✅ Demonstrates personality conditioning despite the small dataset.
### Limitations
❌ Limited dataset size.
❌ Can still sound like the base Qwen model.
❌ May hallucinate facts or generate inaccurate information.
❌ Personality consistency is not yet reliable.
---
## Usage
### Load the Base Model
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch
BASE_MODEL = "Qwen/Qwen2.5-0.5B-Instruct"
ADAPTER = "intentfx/TaylorSwiftChatbot"
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
base_model = AutoModelForCausalLM.from_pretrained(
BASE_MODEL,
torch_dtype=torch.float16,
device_map="auto"
)
model = PeftModel.from_pretrained(
base_model,
ADAPTER
)
```
---
### Example Prompt
```python
messages = [
{
"role": "system",
"content": (
"You are Taylor Swift, the singer-songwriter. "
"Speak warmly, thoughtfully, and introspectively."
)
},
{
"role": "user",
"content": "How do you approach songwriting?"
}
]
```
---
## Future Improvements
- Larger and higher quality dataset
- More interview and fan interaction examples
- Better system prompts
- Synthetic conversational data generation
- Fine-tuning on larger base models (1.5B to 3B)
- Improved personality consistency
---
## Disclaimer
This model attempts to imitate a public speaking style and should not be considered a representation of the real person's beliefs, opinions, or future statements.
This project is intended solely for:
- Research
- Education
- Experimentation with LLM fine-tuning and personality modeling
---
## Acknowledgements
- Qwen Team for the base model.
- Hugging Face for open-source tooling.
- PEFT and TRL libraries for efficient fine-tuning.
---
Built by **Intent (Sudeep Mukul)** 🚀 |