ji-farthing/gemma-4-26B-A4B-it-DFlash-SWA-ik-llama-GGUF

Gemma 4 26B-A4B DFlash SWA draft for ik_llama

This repo contains an ik_llama-compatible DFlash draft GGUF converted from z-lab/gemma-4-26B-A4B-it-DFlash, carrying the per-layer sliding-window attention (SWA) pattern.

This is not a standalone chat model. Use it as a --model-draft file next to a matching Gemma 4 26B-A4B IT target GGUF, with DFlash speculative decoding. Gemma 4 needs --jinja.

Sliding-window attention

The draft is sliding-window on every layer except a final full-attention (global) layer: sliding_window_pattern = [true, true, true, true, false], sliding_window = 2048.

Files

File	Quant	Draft window
gemma-4-26B-A4B-it-DFlash-SWA-ik_llama-Q8_0.gguf	Q8_0	2048 (4 sliding + 1 global)

Use

llama-server \
  -m /path/to/gemma-4-26B-A4B-it-<quant>.gguf \
  --model-draft /path/to/gemma-4-26B-A4B-it-DFlash-SWA-ik_llama-Q8_0.gguf \
  --spec-type dflash:n_max=4,cross_ctx=8192 \
  -c 8192 --jinja

SWA only engages once the DFlash cross-context exceeds the 2048 window, so set cross_ctx above the window for long-context prompts (the default 512 does not grow with -c).

Validation (RTX 4070, ik_llama DFlash SWA branch)

Draft acceptance and throughput versus the same draft run with full attention, as the prompt overflows the 2048 window, where clip = (prompt - 2048) / prompt:

prompt tok	clip	accept, full-attn	accept, SWA	acceptance gain	tok/s change
1413	0%	14.4%	15.2%	+0.7 pp	+1.9%
2751	26%	17.1%	17.1%	+0.0 pp	-1.1%
3923	48%	12.0%	15.9%	+3.9 pp	+7.9%
5426	62%	13.8%	14.7%	+0.9 pp	+0.5%
10673	81%	10.6%	18.1%	+7.5 pp	+16%

Conversion

Converted from z-lab/gemma-4-26B-A4B-it-DFlash with ik_llama's convert_hf_to_gguf.py DFlash draft converter (sliding-window support branch), then quantized to Q8_0. The per-layer SWA pattern is taken from the source layer_types. Conversion requires a --target-model-dir containing the target tokenizer merges.