Modelpublic

dervig/m51Lab-NorskGemma4-31B-GGUF

sourceHugging Faceapache-2.0updated 6mo agoView on Hugging Face

0likes121downloads

Model Card

m51Lab-NorskGemma4-31B-GGUF

GGUF quantized versions of m51Lab-NorskGemma4-31B for local inference with llama.cpp, Ollama, LM Studio, and other GGUF-compatible tools.

NorEval score: 0.836 — Norway's top-scoring open-source model.

Model	Params	NorEval Avg
m51Lab-NorskGemma4-31B	31B	0.836
m51Lab-NorskMistral-119B	119B MoE	0.764
NorMistral-11B-thinking	11B	0.731

See full model card for complete benchmark details and training methodology.

Available Files

File	Quant	Size	RAM needed	Description
`NorskGemma4-31B-Q4_K_M.gguf`	Q4KM	18 GB	~22 GB	Recommended — good balance of quality and speed
`NorskGemma4-31B-Q8_0.gguf`	Q8_0	31 GB	~35 GB	High quality, near-lossless
`NorskGemma4-31B-F16.gguf`	F16	58 GB	~62 GB	Full precision, not quantized

Usage

llama.cpp

bash

# Download
huggingface-cli download dervig/m51Lab-NorskGemma4-31B-GGUF \
  NorskGemma4-31B-Q4_K_M.gguf --local-dir .

# Run (GPU accelerated)
./llama-cli -m NorskGemma4-31B-Q4_K_M.gguf \
  -p "Kva er hovudstaden i Noreg?" \
  -n 256 -ngl 99 -c 4096 --jinja

Ollama

bash

# Create Modelfile
cat > Modelfile << 'EOF'
FROM ./NorskGemma4-31B-Q4_K_M.gguf
PARAMETER temperature 0.7
PARAMETER num_ctx 4096
SYSTEM Du er ein hjelpsom norsk AI-assistent.
EOF

# Create and run
ollama create norskgemma4 -f Modelfile
ollama run norskgemma4 "Forklar kva Stortinget er."

LM Studio

1.Download the Q4KM or Q8_0 file
2.Place in your LM Studio models directory
3.Select the model and start chatting in Norwegian

Performance

Tested with llama.cpp on 1x NVIDIA H100:

Quant	Prompt speed	Generation speed
Q4KM	138 t/s	66 t/s
Q8_0	202 t/s	56 t/s
F16	7 t/s	40 t/s

Credits

Built by m51.ai Lab. Based on Google Gemma 4 31B-it, fine-tuned with data from NbAiLab, evaluated on NorEval by LTG, University of Oslo.