---

license: apache-2.0

base_model:

- z-lab/gemma-4-26B-A4B-it-DFlash

- google/gemma-4-26B-A4B-it

tags:

- gguf

- gemma4

- dflash

- speculative-decoding

- sliding-window-attention

- ik_llama

---

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.