config = NonuConfig( total_params=7_000_000_000, active_threshold=1e-7, # The "100 Nonu" magic number hidden_size=1024, num_layers=48, num_heads=16, use_multiplicative_residuals=True )
with torch.no_grad(): outputs = model(input_ids) # shape: (4, 128, 50000) logits = outputs.logits 100 nonu model
Word embeddings are stored as 100-dimensional vectors, each element quantized to one of (10^7) discrete levels. This results in an ultra-low memory footprint: a 50k vocabulary requires just 50k × 100 × (log2(1e7) bits) ≈ 500 MB – small enough for mobile. config = NonuConfig( total_params=7_000_000_000