Instructions to use Lucebox/gemma-4-26B-A4B-it-DFlash-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Lucebox/gemma-4-26B-A4B-it-DFlash-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Lucebox/gemma-4-26B-A4B-it-DFlash-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf Lucebox/gemma-4-26B-A4B-it-DFlash-GGUF:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Lucebox/gemma-4-26B-A4B-it-DFlash-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf Lucebox/gemma-4-26B-A4B-it-DFlash-GGUF:Q8_0
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf Lucebox/gemma-4-26B-A4B-it-DFlash-GGUF:Q8_0 # Run inference directly in the terminal: ./build/bin/llama cli -hf Lucebox/gemma-4-26B-A4B-it-DFlash-GGUF:Q8_0
Use Docker
docker model run hf.co/Lucebox/gemma-4-26B-A4B-it-DFlash-GGUF:Q8_0
- Llama new
- LM Studio
- Jan
- Ollama
How to use Lucebox/gemma-4-26B-A4B-it-DFlash-GGUF with Ollama:
ollama run hf.co/Lucebox/gemma-4-26B-A4B-it-DFlash-GGUF:Q8_0
- Unsloth Desktop
- Docker Model Runner
How to use Lucebox/gemma-4-26B-A4B-it-DFlash-GGUF with Docker Model Runner:
docker model run hf.co/Lucebox/gemma-4-26B-A4B-it-DFlash-GGUF:Q8_0
- Lemonade
How to use Lucebox/gemma-4-26B-A4B-it-DFlash-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Lucebox/gemma-4-26B-A4B-it-DFlash-GGUF:Q8_0
Run and chat with the model
lemonade run user.gemma-4-26B-A4B-it-DFlash-GGUF-Q8_0
List all available models
lemonade list
- Atomic Chat
gemma-4-26B-A4B-it DFlash Draft β Q8_0 GGUF
Q8_0 GGUF quantization of the z-lab/gemma-4-26B-A4B-it-DFlash draft model, produced for the Lucebox dflash engine (speculative decoding for google/gemma-4-26B-A4B-it).
- Source: z-lab/gemma-4-26B-A4B-it-DFlash (BF16 safetensors)
- Tool:
quantize_gemma_dflash_q8.py(parameterized variant ofdflash/scripts/quantize_draft_q8.py) - Size: 0.46 GB (53% of BF16)
- Arch:
gemma4-dflash-draft - Layers: 5
- Hidden: 2816
- n_head / n_head_kv: 32 / 8
- head_dim: 128
- intermediate_size: 5632
- vocab_size: 262144
- rope_theta: 1e6, rms_eps: 1e-6
- sliding_window: 2048, final_logit_softcapping: 30.0
- DFlash:
n_target_layers=30,target_layer_ids=[1,6,11,17,22,27],block_size=16,mask_token_id=4 - Tensors: projection weights β Q8_0, norm weights β F32 (precision-critical, tiny)
Pairs with
- Target: google/gemma-4-26B-A4B-it (run as Q4_K_M GGUF via
unsloth/gemma-4-26B-A4B-it-GGUFor equivalent)
Notes
- The dflash-specific singletons
dflash.fc.weightanddflash.hidden_norm.weightbridge target hidden states into the draft. Do not re-quantize with stockllama-quantizeβ it strips these tensors. Use the script above. - Loader support for
gemma4-dflash-draftinlucebox-hubis the next step after PR #232 (gemma4 target adapter).
Usage
# With dflash_server (once gemma4-dflash-draft arch is wired in the loader)
dflash_server gemma-4-26B-A4B-it-UD-Q4_K_M.gguf --draft gemma-4-26B-A4B-it-DFlash-q8_0.gguf
Conversion command
PYTHONPATH=lucebox-hub/dflash/deps/llama.cpp/gguf-py \
python3 quantize_gemma_dflash_q8.py \
gemma-4-26B-A4B-it-DFlash/ \
gemma-4-26B-A4B-it-DFlash-q8_0.gguf \
--name gemma-4-26B-A4B-it-DFlash-Q8_0
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Base model
z-lab/gemma-4-26B-A4B-it-DFlash