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---
license: mit
language:
- en
tags:
- lightbrain
- field-dynamics
- sparse-activation
- text-generation
library_name: lightbrain
pipeline_tag: text-generation
model-index:
- name: lightbrain-100m
results: []
---
# lightbrain-100m
## Model Description
LIGHTBRAIN is a novel neural architecture based on **Hybrid Field Transformer** paradigm.
### Key Features
- **Sparse Activation**: Only ~0.1-10% of field regions active during inference
- **Field Dynamics**: Pattern resonance for knowledge retrieval
- **Transformer Integration**: Self-attention for sequence modeling (hybrid)
- **OpenAI-Compatible API**: Drop-in replacement for chat completions
## Architecture
| Component | Value |
|-----------|-------|
| Hidden Size | 768 |
| Layers | 12 |
| Attention Heads | 12 |
| Field Regions | 128 |
| Field Size | 128 |
| Field Depth | 64 |
```
┌─────────────────────────────────────┐
│ TRANSFORMER ENCODER LAYERS │
│ (Self-Attention + FFN) │
└─────────────────────────────────────┘
↓
┌─────────────────────────────────────┐
│ FIELD DYNAMICS CORE │
│ (Sparse Activation + Evolution) │
└─────────────────────────────────────┘
↓
┌─────────────────────────────────────┐
│ OUTPUT PROJECTION │
│ (Pattern → Token Logits) │
└─────────────────────────────────────┘
```
## Model Files
| File | Description |
|------|-------------|
| `Model-001.safetensors` | Model weights (721.30 MB) |
| `config.json` | Model configuration |
| `tokenizer.json` | Tokenizer vocabulary |
| `tokenizer_config.json` | Tokenizer configuration |
| `generation_config.json` | Generation parameters |
| `params.json` | LIGHTBRAIN parameters |
## Model Stats
- **Original Size**: 721.28 MB
- **File Size**: 721.30 MB
- **Compression Ratio**: 1.00x
- **Number of Tensors**: 200
## Usage
### With LIGHTBRAIN Library
```python
from lightbrain.model import HybridFieldTransformer
from lightbrain.inference import InferenceEngine
# Load model
model = HybridFieldTransformer.load("path/to/model")
engine = InferenceEngine(model=model)
# Generate
result = engine.generate("Hello, how are you?")
print(result.text)
```
### Loading from Safetensors
```python
from safetensors.numpy import load_file
import json
# Load weights
weights = load_file("Model-001.safetensors")
# Load config
with open("config.json") as f:
config = json.load(f)
# Reconstruct model from weights
```
### In Google Colab
```python
# Install
!pip install safetensors
# Download
from huggingface_hub import snapshot_download
model_path = snapshot_download(repo_id="lightbrain-100m")
# Load and use
from safetensors.numpy import load_file
weights = load_file(f"{model_path}/Model-001.safetensors")
```
## Training
Trained using LIGHTBRAIN framework with:
- Resonance Alignment (Hebbian learning)
- Gradient-based fine-tuning for transformer layers
- Field topology optimization
## License
MIT License
## Citation
```bibtex
@misc{lightbrain2024,
title={LIGHTBRAIN: Hybrid Field Dynamics for Efficient LLMs},
year={2024},
publisher={HuggingFace}
}
```
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