Instructions to use Lucebox/gemma-4-31B-it-DFlash-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lucebox/gemma-4-31B-it-DFlash-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Lucebox/gemma-4-31B-it-DFlash-GGUF",
	filename="gemma-4-31B-it-DFlash-q8_0.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Lucebox/gemma-4-31B-it-DFlash-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Lucebox/gemma-4-31B-it-DFlash-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Lucebox/gemma-4-31B-it-DFlash-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Lucebox/gemma-4-31B-it-DFlash-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Lucebox/gemma-4-31B-it-DFlash-GGUF:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Lucebox/gemma-4-31B-it-DFlash-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Lucebox/gemma-4-31B-it-DFlash-GGUF:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Lucebox/gemma-4-31B-it-DFlash-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Lucebox/gemma-4-31B-it-DFlash-GGUF:Q8_0

Use Docker

docker model run hf.co/Lucebox/gemma-4-31B-it-DFlash-GGUF:Q8_0

LM Studio
Jan
Ollama
How to use Lucebox/gemma-4-31B-it-DFlash-GGUF with Ollama:
```
ollama run hf.co/Lucebox/gemma-4-31B-it-DFlash-GGUF:Q8_0
```

Unsloth Studio new

How to use Lucebox/gemma-4-31B-it-DFlash-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Lucebox/gemma-4-31B-it-DFlash-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Lucebox/gemma-4-31B-it-DFlash-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Lucebox/gemma-4-31B-it-DFlash-GGUF to start chatting

Docker Model Runner
How to use Lucebox/gemma-4-31B-it-DFlash-GGUF with Docker Model Runner:
```
docker model run hf.co/Lucebox/gemma-4-31B-it-DFlash-GGUF:Q8_0
```

Lemonade

How to use Lucebox/gemma-4-31B-it-DFlash-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Lucebox/gemma-4-31B-it-DFlash-GGUF:Q8_0

Run and chat with the model

lemonade run user.gemma-4-31B-it-DFlash-GGUF-Q8_0

List all available models

lemonade list

gemma-4-31B-it DFlash Draft — Q8_0 GGUF

Q8_0 GGUF quantization of the z-lab/gemma-4-31B-it-DFlash draft model, produced for the Lucebox dflash engine (speculative decoding for google/gemma-4-31B-it).

Source: z-lab/gemma-4-31B-it-DFlash (BF16 safetensors)
Tool: quantize_gemma_dflash_q8.py (parameterized variant of dflash/scripts/quantize_draft_q8.py)
Size: 1.63 GB (53% of BF16)
Arch: gemma4-dflash-draft
Layers: 5
Hidden: 5376
n_head / n_head_kv: 64 / 8
head_dim: 128
intermediate_size: 10752
vocab_size: 262144
rope_theta: 1e6, rms_eps: 1e-6
sliding_window: 2048, final_logit_softcapping: 30.0
DFlash: n_target_layers=60, target_layer_ids=[1,12,23,35,46,57], block_size=16, mask_token_id=4
Tensors: projection weights → Q8_0, norm weights → F32 (precision-critical, tiny)

Pairs with

Target: google/gemma-4-31B-it (run as Q4_K_M GGUF via unsloth/gemma-4-31B-it-GGUF or bartowski/google_gemma-4-31B-it-GGUF)

Notes

The dflash-specific singletons dflash.fc.weight and dflash.hidden_norm.weight bridge target hidden states into the draft. Do not re-quantize with stock llama-quantize — it strips these tensors. Use the script above.
Loader support for gemma4-dflash-draft in lucebox-hub is 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-4-31B-it-Q4_K_M.gguf --draft gemma-4-31B-it-DFlash-q8_0.gguf

Conversion command

PYTHONPATH=lucebox-hub/dflash/deps/llama.cpp/gguf-py \
python3 quantize_gemma_dflash_q8.py \
  gemma-4-31B-it-DFlash/ \
  gemma-4-31B-it-DFlash-q8_0.gguf \
  --name gemma-4-31B-it-DFlash-Q8_0

Downloads last month: -

GGUF

Model size

2B params

Architecture

gemma4-dflash-draft

Hardware compatibility

8-bit

Inference Providers NEW

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Model tree for Lucebox/gemma-4-31B-it-DFlash-GGUF

Base model

z-lab/gemma-4-31B-it-DFlash

Quantized

(4)

this model