BueormLLC_-_RAGPT-2_unfunctional-gguf

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RAGPT-2_unfunctional - GGUF

• Model creator: https://huggingface.co/BueormLLC/
• Original model: https://huggingface.co/BueormLLC/RAGPT-2_unfunctional/

Original model description:

license: mit datasets:

• neural-bridge/rag-dataset-12000
• neural-bridge/rag-dataset-1200 language:
• en

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VERY IMPORTANT

• This model is in alpha phase and is NOT yet recommended for use.
• This model is obsolete today, recommended model here.

RAGPT-2 (unfunctional): Fine-tuned GPT-2 for Context-Based Question Answering

Model Description

RAGPT-2 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" and others 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" and "neural-bridge/rag-dataset-1200" 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: 8
• Batch size: 4
• Learning rate: Default AdamW optimizer settings
• Max sequence length: 512 tokens

Usage

To use the model:

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BueormLLC/RAGPT-2_unfunctional")
model = AutoModelForCausalLM.from_pretrained("BueormLLC/RAGPT-2_unfunctional")

context = "Mount Everest is the highest mountain in the world, with a height of 8,848 meters."
question = "What is the height of Mount Everest?"
input_text = f"Context: {context}\nquestion: {question}\nanswer:"

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)

print(f"Respuesta generada: {answer}")

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

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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 = {Bueorm},
  title = {RAGPT-2: Fine-tuned GPT-2 for Context-Based Question Answering},
  year = {2024},
  publisher = {GitHub},
  journal = {None},
  howpublished = {\url{https://huggingface.co/BueormLLC/RAGPT-2_unfunctional}}
}