Image-Text-to-Text
MLX
Safetensors
gemma4
rotorquant
kv-cache-quantization
gemma
multimodal
quantized
2bit
2-bit
Instructions to use majentik/gemma-4-31B-RotorQuant-MLX-2bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use majentik/gemma-4-31B-RotorQuant-MLX-2bit 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("majentik/gemma-4-31B-RotorQuant-MLX-2bit") config = load_config("majentik/gemma-4-31B-RotorQuant-MLX-2bit") # 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
- LM Studio
chore(card): enrich YAML frontmatter (pipeline_tag, language, library_name, inference)
Browse files
README.md
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base_model: google/gemma-4-31B
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library_name: mlx
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tags:
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license: apache-2.0
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pipeline_tag: image-text-to-text
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---
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# Gemma 4 31B - RotorQuant MLX 2-bit
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base_model: google/gemma-4-31B
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library_name: mlx
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tags:
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- rotorquant
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- kv-cache-quantization
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- gemma
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- gemma4
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- multimodal
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- quantized
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- mlx
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- 2bit
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license: apache-2.0
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pipeline_tag: image-text-to-text
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language:
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- en
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---
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# Gemma 4 31B - RotorQuant MLX 2-bit
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