Downloads · 30 days
361
5% of all-time downloads
k050506koch/GPT3-dev-350m-2805
GPT3-dev-350m-2805 is a text generation model from k050506koch. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as mit.
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…
Downloads · 30 days
361
5% of all-time downloads
All-time downloads
7.1K
Public
Parameters
356M
4.5 GB on disk
Likes
0
Public
Click a slice to open those files.
.gguf3.7 GB · 84%
From the Hugging Face model README
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.
This is a model with 350 million parameters (attempt to replicate GPT-3 Medium). (it's very undertrained.)
from transformers import AutoTokenizer, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained('k050506koch/GPT3-dev-350m-2805', trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained('k050506koch/GPT3-dev-350m-2805')
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))
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.
This project is licensed under the MIT License. See the LICENSE file for details. Everyone can use and modify this code at their discretion.
Thanks OpenAI, HuggingFace and Pytorch for making this project possible!