Welcome to the GPT3 repository! This project is an attempt to recreate the architecture and approach from the original OpenAI GPT-3 paper. The repository includes scripts for training, fine-tuning, and inference of a GPT-3-like model using PyTorch and the Hugging Face Transformers library. Here are located weights of dev checkpoints of my models. You can always download a folder, paste it's path inside inference.py and chat with them.

You can find all code on GitHub

Note: This is a model with 125 million parameters. (it's very undertrained.)

Note 2: This is a model checkpoint released on 12/02 2025 (12 batch size, 4 grad accumulation, 512 tokens and 36500 steps under Lion optimizer). It scores 28.03% on MMLU which is slightly higher than 25% (random guess)

inference:

python

from transformers import AutoTokenizer, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained('k050506koch/GPT3-dev-125m-1202', trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained('k050506koch/GPT3-dev-125m-1202')
tokenizer.pad_token_id = tokenizer.eos_token_id
print("\n", tokenizer.decode(model.generate(tokenizer.encode("He is a doctor. His main goal is", return_tensors='pt'),
    max_length=128, temperature=0.7, top_p=0.9, repetition_penalty=1.2, no_repeat_ngram_size=3,
    num_return_sequences=1, do_sample=True)[0], skip_special_tokens=True))

Contributing

Contributions are welcome! I'm just a student who is interested in AI so my code may be incorrect or have logical issues. Please open an issue or submit a pull request for any improvements or bug fixes, I will be happy.

License

This project is licensed under the MIT License. See the LICENSE file for details. Everyone can use and modify this code at their discretion.

Acknowledgements

Thanks OpenAI, HuggingFace and Pytorch for making this project possible!