---
license: mit
datasets:
- neural-bridge/rag-dataset-12000
language:
- en
---

# RAGPT: Fine-tuned GPT-2 for Context-Based Question Answering

## Model Description

RAGPT is a fine-tuned version of GPT-2 small, specifically adapted for context-based question answering tasks. This model has been trained to generate relevant answers based on a given context and question, similar to a Retrieval-Augmented Generation (RAG) system.

### Key Features

- Based on the GPT-2 small architecture (124M parameters)
- Fine-tuned on the "neural-bridge/rag-dataset-12000" dataset from Hugging Face
- Capable of generating answers based on provided context and questions
- Suitable for various question-answering applications

## Training Data

The model was fine-tuned using the "neural-bridge/rag-dataset-12000" dataset, which contains:
- Context passages
- Questions related to the context
- Corresponding answers

## Fine-tuning Process

The fine-tuning process involved:
1. Loading the pre-trained GPT-2 small model
2. Preprocessing the dataset to combine context, question, and answer into a single text
3. Training the model to predict the next token given the context and question

### Hyperparameters

- Base model: GPT-2 small
- Number of training epochs: 3
- Batch size: 4
- Learning rate: Default AdamW optimizer settings
- Max sequence length: 512 tokens

## Usage

To use the model:

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "BueormLLC/RAGPT"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Prepare input
context = "Your context here"
question = "Your question here"
input_text = f"Contexto: {context}\nPregunta: {question}\nRespuesta:"

# Generate answer
input_ids = tokenizer.encode(input_text, return_tensors="pt")
output = model.generate(input_ids, max_length=150, num_return_sequences=1)
answer = tokenizer.decode(output[0], skip_special_tokens=True)
```

## Limitations

- The model's knowledge is limited to its training data and the base GPT-2 model.
- It may sometimes generate irrelevant or incorrect answers, especially for topics outside its training domain.
- The model does not have access to external information or real-time data.

## Ethical Considerations

Users should be aware that this model, like all language models, may reflect biases present in its training data. It should not be used as a sole source of information for critical decisions.

## Future Improvements

- Fine-tuning on a larger and more diverse dataset
- Experimenting with larger base models (e.g., GPT-2 medium or large)
- Implementing techniques to improve factual accuracy and reduce hallucinations

## Support us

- [Paypal](https://paypal.me/bueorm)
- [Patreon](https://patreon.com/bueorm)
### We appreciate your support, without you we could not do what we do.

## Citation

If you use this model in your research, please cite:

```
@misc{RAGPT,
  author = {Your Name or Organization},
  title = {RAGPT: Fine-tuned GPT-2 for Context-Based Question Answering},
  year = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://huggingface.co/BueormLLC/RAGPT}}
}
```