wave-density-130m

Wave-Density Attention (WDA) — 130M Parameter Language Model

This repository contains a 130M parameter causal language model built with Wave-Density Attention (WDA), a novel alternative to standard dot-product self-attention.

WDA reframes attention as a wave-interference and density-rendering process, replacing the traditional $QK^\top$ similarity computation with learned frequency-based interactions. This allows attention patterns to emerge from constructive and destructive interference rather than explicit pairwise dot products.

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Model Overview

• Architecture: Decoder-only Transformer with Wave-Density Attention
• Parameters: ~130M
• Context Length: 256 tokens
• Attention Mechanism: Wave-Density Attention (Mixture-of-Masks via learned wave bases)
• Training Regime: From scratch

Training Data

• Primary: UltraChat 200k (instruction-style supervision)
• Initialization / Mixing: Streaming C4 (broad web text)

This combination provides both general language coverage and instruction-following coherence, while allowing the WDA mechanism to learn stable long-range structure.

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Performance

• Validation Loss (UltraChat): ~2.86
• Equivalent Perplexity: ~17.5–20 (best checkpoints)
• Model Size: 130M parameters

Despite using a fundamentally different attention formulation, WDA achieves competitive perplexity and strong qualitative coherence at this scale.

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Usage

To use this model, install or clone the reference implementation from the official repository:

👉 Wave-Density Attention code

Example loading snippet:

from wave_dencity import WaveCharLM
import torch
import json

# Load model configuration
with open("config.json", "r") as f:
    config = json.load(f)

model = WaveCharLM(**config)
# Load weights from model.safetensors
# model.load_state_dict(...)
model.eval()

Note: This model is intended for research and experimentation with alternative attention mechanisms. The codebase exposes WDA internals for inspection and modification.

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Why Wave-Density Attention?

Traditional attention relies on sharp token-to-token similarity. WDA instead:

• Uses frequencies as a representational tool
• Produces attention surfaces via interference patterns
• Selects among multiple learned attention masks dynamically (Mixture-of-Masks / “MoM”)

This approach avoids explicit dot-product similarity while still supporting coherent, causal language modeling.

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Citation

If you use this model or the Wave-Density Attention mechanism in your work, please cite the official repository and paper.