README.md

---
language: en
license: mit
tags:
- spiking-neural-networks
- language-modeling
- neuromorphic
- energy-efficient
- biological-ai
datasets:
- fineweb-5B
pipeline_tag: text-generation
---

# 🧠 Spiking Neural Network Language Model - Training Checkpoint

**Live training checkpoint from the world's first large-scale spiking language model!**

## Current Training Status

- **Training Step**: 554,000
- **Tokens Processed**: 5.67B tokens
- **Current Loss**: 4.5783
- **Spike Rate**: 0.0508
- **Learning Rate**: 8.15e-05

## Model Architecture

- **Parameters**: ~54M
- **Architecture**: 12-layer Spiking LTC Network
- **Hidden Size**: 768
- **Sequence Length**: 1024
- **Multi-timescale Processing**: Fast → Medium → Slow layers

## Training Details

- **Dataset**: PatrickHaller/fineweb-5B
- **Target**: 3 epochs (~15B tokens total)
- **Biological Dynamics**: Adaptive thresholds, refractory periods
- **Energy Efficiency**: ~5% neuron activation vs 100% in Transformers

## Scientific Significance

This represents ongoing training of the first large-scale spiking neural network for language modeling, demonstrating:

1. **Biological neural dynamics** can learn language at scale
2. **Energy efficiency** through sparse neural firing
3. **Multi-timescale processing** for hierarchical understanding

## Usage

```python
# Download this checkpoint
from huggingface_hub import hf_hub_download
checkpoint = hf_hub_download(
    repo_id="rootxhacker/piking-llm-5b-3epochs-exp",
    filename="checkpoint_554000.pt"
)

# Load with custom spiking model code
# (See full implementation for complete usage)
```

---

**🔬 This is live research in progress! Check back for updates as training continues.**

**Training Progress**: 37.8% complete towards 15B tokens