Cactus-Compute / gemma4-e2b-grouped-k96

Gemma-4 E2B with grouped expert routing (K=96, d=0.50)

This model is a sparsified variant of google/gemma-4-e2b-it where each MLP's FFN dimension is partitioned into 96 groups via k-means on activation profiles. At inference, the top 48 of 96 groups fire per token (50% density), allowing entire groups of neurons to be skipped (and their corresponding rows of up_proj / gate_proj and columns of down_proj to be skipped from memory).

Group sizes (D_FFN / 96): 64 in early layers (D_FFN=6144), 128 in late layers (D_FFN=12288). 64-element groups in bf16 = exactly 128B = one L2 cache line on NVIDIA GPUs — memory-bandwidth-aligned routing .

Training recipe

1. Train Sw_gate_only_50 baseline: 5000 steps gate-only, density=0.50, lr=1e-4, tau anneal 1.0→0.01, int4 QAT, on gemma4_e2b_it_bulk_50k.jsonl .
2. Cluster activations: 4 sequences × 2048 tokens through baseline; k-means at K=96 per layer on |gate*up| activation profile.
3. Polish: install GroupedMaskedMLP (K=96, K_active=48), add LoRA r128 on up_proj / down_proj , train 1000 steps CE on gemma4_e2b_it_final_50k.jsonl .

Results (eval on gemma4_e2b_it_final_50k.jsonl )

Usage

git lfs install
git clone https://huggingface.co/Cactus-Compute/gemma4-e2b-grouped-k96
cd $(basename Cactus-Compute/gemma4-e2b-grouped-k96)
pip install torch transformers
python inference_k96.py --prompt "The capital of France is"

The inference script loads the base Gemma-4 weights from HuggingFace, installs the grouped routing wrappers (loading cluster assignments from groups/ ), then applies int4 QAT + LoRA + the trained state dict. verify_grouped_routing() asserts every layer is genuinely a GroupedMaskedMLP with K=96 before generation.

Citation

Internal research (Anthropic Claude + Noah Cylich, Cactus Compute), 2026-04.