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Numera-v1 – AI Model by luigicfilho | AlphaNeural AI

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luigicfilho/
Numera-v1

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text-generation

numerical-generation

text-generation-inference

endpoints_compatible

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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

python

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