# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("HDTenEightyP/GPT-USENET-5", device_map="auto")Quick Links
GPT-Usenet-5
One of the largest LLMs possible to create in Google Colab. Trained using a corpus of 70GB of text, nearly twice that of OpenWebText. Requires 10GB of VRAM.
Technical Information
| Layers | 36 |
| Heads | 20 |
| Embeddings | 1280 |
| Context Window | 32768 tokens |
| Tokenizer | GPT-2 BPE |
| System Tokens | 💻🌀 |
| Input Tokens | 📋📄 |
| Thinking Tokens | 🧠💡 |
| Output Tokens | ✅❌ |
Place the first of each tokens before the text, and the second after the text.
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="HDTenEightyP/GPT-USENET-5")