AlexAtomic/diffusiongemma-26B-A4B-it-GGUF
Atomic Chat · DiffusionGemma 26B-A4B (GGUF)
GGUF quantizations of `google/diffusiongemma-26B-A4B-it`, self-quantized by Atomic Chat from Google's original weights.
This is a discrete diffusion language model. It does not generate token by token. It denoises a block of tokens (a "canvas") in parallel using block-autoregressive multi-canvas sampling. It is also a sparse MoE: 25.2B total parameters, 3.8B active (8 of 128 experts).
[!WARNING] These run only with the DiffusionGemma build of llama.cpp, via the dedicatedllama-diffusion-clirunner. The standardllama-cli/llama-server, Ollama, LM Studio and Jan cannot run these yet. Diffusion support is an open draft PR (ggml-org/llama.cpp#24423), not yet merged to master.
Quants
Quantized without an importance matrix (imatrix tooling does not yet cover diffusion decoding), matching the upstream approach.
How to run
Build the DiffusionGemma branch of llama.cpp:
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
git fetch origin pull/24423/head:diffusiongemma
git checkout diffusiongemma
cmake -B build -DGGML_CUDA=ON
cmake --build build -j --config Release --target llama-diffusion-cliGenerate:
./build/bin/llama-diffusion-cli \
-hf AlexAtomic/diffusiongemma-26B-A4B-it-GGUF:Q4_K_M \
-p "Explain what a neural network is in two sentences." \
--diffusion-steps 128 --diffusion-visualSet -DGGML_CUDA=OFF for CPU or Metal builds. Add -ngl N to offload N layers to GPU.
Useful diffusion flags:
--diffusion-steps Ndenoising steps (default 128, fewer is faster).--diffusion-eb auto|on|offentropy-bound decoder tuned for DiffusionGemma.--diffusion-visualwatch the canvas fill in progressively.
Model Overview
How these were made
- Download
google/diffusiongemma-26B-A4B-it. - Convert to f16 GGUF with the DiffusionGemma build of llama.cpp.
- Verify generation with
llama-diffusion-cli. - Quantize the ladder with
llama-quantize.
License
These weights are derived from Gemma and stay governed by the Gemma Terms of Use. By downloading you agree to those terms. Original model by Google DeepMind. Quantized by Atomic Chat.
