shakespeare-GPT

Shakespeare GPT

A small, self-contained character-level GPT trained on Tiny Shakespeare.

• Parameters: 4.78M
• Tokenizer: character-level (65 tokens)
• Training corpus: Tiny Shakespeare
• Context length: 128 tokens
• Training steps: 10,000

Architecture

• 6 transformer blocks
• 256-dimensional embeddings
• 8 attention heads
• Learned token + positional embeddings
• Pre-LayerNorm residual transformer with a fused QKV projection and a 4x MLP expansion
• Weight-tied LM head and token embedding

This is a plain PyTorch model. It does not use the transformers, accelerate, peft, or trl libraries.

Quick start

git clone <model-repository>
cd shakespeare-GPT
pip install -r requirements.txt
python inference.py --prompt "ROMEO:"

Install

pip install -r requirements.txt

Requirements:

• torch
• safetensors
• huggingface_hub

Usage

Run inference with a prompt (the model continues the text):

python inference.py --prompt "ROMEO:"

Optional sampling flags:

python inference.py \
    --prompt "ROMEO:" \
    --max-new-tokens 200 \
    --temperature 0.8 \
    --top-k 40

Supported generation parameters:

• --max-new-tokens — number of tokens to generate
• --temperature — sampling temperature (0.0 = greedy)
• --top-k — top-k sampling filter
• --top-p — nucleus sampling filter
• --seed — seed for reproducible sampling

CPU

python inference.py \
    --device cpu \
    --prompt "ROMEO:"

The model runs on CPU with no special setup. It is slower than GPU but works on any machine.

GPU (CUDA)

python inference.py \
    --device cuda \
    --prompt "ROMEO:"

CUDA is used automatically when available; the model never requires it.

Download from the Hub

The weights are stored as a Git LFS object. If you git clone this repo, install git-lfs first or you will only get small LFS pointer files:

git lfs install
git clone https://huggingface.co/Deepjyoti/shakespeare-GPT
cd shakespeare-GPT
pip install -r requirements.txt
python inference.py --prompt "ROMEO:"

Alternatively, download the files with the hf / huggingface_hub tooling (no git-lfs needed):

pip install -r requirements.txt

# hf CLI
hf download Deepjyoti/shakespeare-GPT --local-dir .

# or Python
python -c "from huggingface_hub import snapshot_download; snapshot_download('Deepjyoti/shakespeare-GPT', local_dir='.')"

You can also let inference.py fetch the weights automatically:

python inference.py \
    --model-id Deepjyoti/shakespeare-GPT \
    --prompt "ROMEO:"

A convenience script that downloads everything into a local cache and runs inference:

python download_and_run.py \
    --model-id Deepjyoti/shakespeare-GPT \
    --prompt "ROMEO:"

Repository layout

README.md
config.json          # model hyper-parameters
metadata.json        # training metadata
model.safetensors     # inference weights
model.py              # standalone model implementation
tokenizer.py          # tokenizer implementation
tokenizer.json        # token-to-id mapping
inference.py          # inference entry point
download_and_run.py   # download-and-infer convenience script
requirements.txt
tests/                # tokenizer, loading, generation, and CPU tests

Tests

pip install pytest
python -m pytest tests -q

Limitations

This is an experimental research model. Please keep expectations appropriate:

• It is a small GPT (4.78M parameters) trained on ~100K lines of Shakespeare for 10,000 steps.
• It is not a general-purpose language model.
• It is not a coding model and cannot write or reason about code.
• Output is often grammatically broken or nonsensical, especially beyond the first few sentences.
• It is primarily a demonstration of a from-scratch GPT — useful for learning and experimentation, not production use.

Do not rely on it for factual or creative writing.