Light-100M-untrainedv0.01

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

┌─────────────────────────────────────┐
│   TRANSFORMER ENCODER LAYERS        │
│   (Self-Attention + FFN)            │
└─────────────────────────────────────┘
        ↓
┌─────────────────────────────────────┐
│   FIELD DYNAMICS CORE               │
│   (Sparse Activation + Evolution)   │
└─────────────────────────────────────┘
        ↓
┌─────────────────────────────────────┐
│   OUTPUT PROJECTION                 │
│   (Pattern → Token Logits)          │
└─────────────────────────────────────┘

Model Files

Model Stats

• Original Size: 721.28 MB
• File Size: 721.30 MB
• Compression Ratio: 1.00x
• Number of Tensors: 200

Usage

With LIGHTBRAIN Library

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

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

# 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

@misc{lightbrain2024,
  title={LIGHTBRAIN: Hybrid Field Dynamics for Efficient LLMs},
  year={2024},
  publisher={HuggingFace}
}