---
language:
- en
license: mit
library_name: transformers
tags:
- generated
- numerical-generation
- weight-space
- text-generation
inference: true
model_creator: LCDev-LLMGen
widget:
- text: "The nature of mathematics is"
  example_title: "Nature of mathematics"
- text: "Once upon a time"
  example_title: "Story"
---

<div align="center">

![Numera Banner](Numera.png)

# Numera: The Numerically Generated Model

</div>

Numera-v1 is a 3 Billion parameter transformer-based causal decoder model designed for high-quality text generation and conceptual coherence. It is part of the Numera series focusing on structural integrity and advanced architectural optimization.

## Model Details

- **Model Name:** Numera (Gen-1) 3B
- **Generated By:** LCDev-Numera
- **Base Architecture:** Qwen2.5
- **Parameters**: 3 Billion
- **Type:** Statistical Weight Generation
- **Date Generated:** 2026-02-20


### Model Technical Specifications
Here are the details for Numera (Gen-1) 3b :

- Total Parameters: ~3.09 Billion
- Architecture: Qwen2.5 Family
  - Layers: 36
  - Attention Heads: 16 (Query) / 2 (KV - Grouped Query Attention)
  - Hidden Size: 2048
  - Intermediate Size (MLP): 11008
- Vocab Size: 151,936 tokens
- Context Window: 32,768 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.

## Usage

You can use this model with the `transformers` library:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "luigicfilho/Numera-v1-3b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "The nature of mathematics is"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## License

This model is released under the MIT License.