# Shakespeare GPT Transformer

    A character-level GPT model trained on Shakespeare plays.

    ## Model Architecture
    - Character-level tokenization
    - Multi-head self-attention with causal masking
    - 6 transformer blocks
    - 6 attention heads
    - Embedding dimension: 384
    - Block size (context length): 512
    - Dropout: 0.2

    ## Files
    - `model.pt` - Model state dictionary
    - `config.json` - Model hyperparameters
    - `vocab.json` - Character to index and index to character mappings
    - `generated_output.txt` - Sample generated text

    ## Usage
    ```python
    import torch
    import json

    # Load config
    with open("config.json") as f:
        config = json.load(f)

    # Load vocab
    with open("vocab.json") as f:
        vocab = json.load(f)

    stoi = vocab["stoi"]
    itos = {int(k): v for k, v in vocab["itos"].items()}
    decoder = lambda s: "".join([itos[c] for c in s])
    encoder = lambda s: [stoi[c] for c in s]

    # Rebuild model (use same class definitions)
    model = gpt(config["vocab_size"])
    model.load_state_dict(torch.load("model.pt"))
    model.eval()

    # Generate text
    context = torch.zeros((1, 1), dtype=torch.long)
    print(decoder(model.generate(context, max_new_token=500)[0].tolist()))
    ```