Instructions to use wbkou/gemma-4-31B-8bit-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use wbkou/gemma-4-31B-8bit-MLX with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("wbkou/gemma-4-31B-8bit-MLX") config = load_config("wbkou/gemma-4-31B-8bit-MLX") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
mlx-community/gemma-4-31b-8bit
This model was converted to MLX format from google/gemma-4-31b
using mlx-vlm version 0.4.3.
Refer to the original model card for more details on the model.
Use with mlx
pip install -U mlx-vlm
python -m mlx_vlm.generate --model mlx-community/gemma-4-31b-8bit --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
- Downloads last month
- 18
Model size
9B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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8-bit
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