luigicfilho/Numera-v1

Numera: The Numerically Generated Model

This model was automatically generated using LCDev-Numera, a proprietary tool for numerical model generation.

Model Details

• Model Name: Numera (Gen-1)
• Generated By: LCDev-Numera
• Base Architecture: GPT-2
• Type: Statistical Weight Generation
• Date Generated: 2026-01-29

Model Technical Specifications

Here are the details for Numera (Gen-1) :

• Total Parameters: ~82 Million ( 81,912,576 )
• Architecture: GPT-2 Family (6 Layers, 12 Heads, 768 Hidden Size)
• Vocab Size: 50,257 tokens
• Format: SafeTensors (Universal, safe serialization)
• Nature: Numerically Generated (Non-trained, statistical approximation)

Intended Use

This model is intended for research into:

• Weight space analysis of Large Language Models.
• Statistical properties of model weights.
• Experimental initialization checkpoints.

Note: This model is a statistical approximation and not a trained model. It may exhibit repetitive behaviors or lack specific factual knowledge.

How to Use

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "./Numera-v1"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "The future of AI is"
inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

License

MIT