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# Pythia Quantized Model for Question/Answer
This repository hosts a quantized version of the Pythia model, fine-tuned for question/answer tasks. The model has been optimized for efficient deployment while maintaining high accuracy, making it suitable for resource-constrained environments.
## Model Details
- **Model Architecture:** Pythia-410m
- **Task:** Chatbot
- **Dataset:** sewon/ambig_qa
- **Quantization:** Float16
- **Fine-tuning Framework:** Hugging Face Transformers
## Usage
### Installation
```sh
pip install transformers torch
```
### Loading the Model
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("AventIQ-AI/pythia-410m-chatbot")
model = AutoModelForCausalLM.from_pretrained("AventIQ-AI/pythia-410m-chatbot")
tokenizer.pad_token = tokenizer.eos_token
def chat_with_model(model, tokenizer, question, max_length=256):
"""Generate response to a question"""
input_text = question
inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True, max_length=512)
with torch.no_grad():
outputs = model.generate(
inputs["input_ids"],
attention_mask=inputs["attention_mask"],
max_length=max_length,
num_return_sequences=1,
temperature=1.0,
do_sample=True,
pad_token_id=tokenizer.pad_token_id
)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# Example usage
test_question = "What is the capital of France?"
response = chat_with_model(model, tokenizer, test_question)
print("Answer", response)
```
## Performance Metrics
- **Accuracy:** 0.56
- **F1 Score:** 0.56
- **Precision:** 0.68
- **Recall:** 0.56
## Fine-Tuning Details
### Dataset
The Hugging Face's `ambig_qa` dataset was used, containing both question and answer examples.
### Training
- Number of epochs: 3
- Batch size: 4
- Evaluation strategy: epoch
- Learning rate: 2e-5
### Quantization
Post-training quantization was applied using PyTorch's built-in quantization framework to reduce the model size and improve inference efficiency.
## Repository Structure
```
.
├── model/ # Contains the quantized model files
├── tokenizer/ # Tokenizer configuration and vocabulary files
├── model.safensors/ # Fine Tuned Model
├── README.md # Model documentation
```
## Limitations
- The model may not generalize well to domains outside the fine-tuning dataset.
- Quantization may result in minor accuracy degradation compared to full-precision models.
## Contributing
Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements.
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