# EpsteinGPT - Minimal GPT Model

This repository contains a Minimal GPT (MVT) model trained on the Epstein email threads dataset.

## Model Details

This is a custom-built Causal Transformer model (`MinimalGPT`) inspired by nanoGPT/minGPT architectures. It was trained from scratch using a custom Byte-Pair Encoding (BPE) tokenizer.

### Configuration (`config.json`)
```json
{
  "vocab_size": 5000,
  "block_size": 256,
  "n_layer": 8,
  "n_head": 8,
  "n_embd": 512,
  "batch_size": 16,
  "dropout": 0.1,
  "bias": false
}
```

## Files Included

*   `epsteingpt_tokenizer.json`: The custom BPE tokenizer used for encoding and decoding text.
*   `EpsteinGPT.pt`: The PyTorch checkpoint containing the trained model weights.
*   `EpsteinGPT.ptl`: The TorchScript Lite version of the trained model, optimized for deployment.
*   `model.py`: Defines the `MVTConfig` class and the `MinimalGPT` model architecture.
*   `config.json`: Model configuration in JSON format.
*   `README.md`: This file.

## How to Use

To use this model, you would typically:

1.  Load the tokenizer:
    ```python
    from tokenizers import Tokenizer
    tokenizer = Tokenizer.from_file("epsteingpt_tokenizer.json")
    ```
2.  Load the model architecture and configuration (from `model.py` and `config.json`).
3.  Load the trained weights from `EpsteinGPT.pt` into the model.
4.  Use the model for text generation or other tasks.

For generation, you can refer to the `generate.py` script used during development.

## Training

The model was trained on a dataset of Epstein email threads. The training process involved:

1.  **Tokenizer Training:** A BPE tokenizer was trained on the raw text data.
2.  **Data Preparation:** The text data was tokenized and converted into a numerical format.
3.  **Model Training:** The `MinimalGPT` model was trained using a custom training loop.

## Further Information

For more details on the model architecture and training process, refer to the `model.py` and `train.py` scripts.