Non-record: hybrid spiking Transformer (SNN)with a multi-step spiking MLP

[tsbiosky]

Hybrid Spiking Neural Networks (SNNs) MLP

val_bpb: 1.2982 | 15.78 MB | 8×H100 SXM

A contest-friendly hybrid SNN submission built from the train_gpt.py baseline: keep dense GQA attention and the original training/eval/compression pipeline, but replace the standard feed-forward block with a small multi-step leaky integrate-and-fire (LIF-style) spiking MLP.

Reference :https://arxiv.org/pdf/2203.14679

Why this is interesting

This is not a fully spiking language model. It is a hybrid Transformer + SNN-MLP design:

• embeddings, attention, residual path, and logits remain standard dense LM components
• only the feed-forward block is replaced by a spiking mechanism
• the original Parameter Golf training and export path stays intact

That makes the experiment meaningful for the contest setting because it isolates one question:

Can spike neural network achieves good performance in a tiny language model under a strict size budget?