Image-Text-to-Text
MLX
Safetensors
gemma4
rotorquant
kv-cache-quantization
gemma
multimodal
quantized
2bit
2-bit
Instructions to use majentik/gemma-4-E4B-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-E4B-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-E4B-RotorQuant-MLX-2bit") config = load_config("majentik/gemma-4-E4B-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 Settings
- LM Studio
- Atomic Chat
Commit History
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Remove links to retired duplicate brand variants
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Fix quantization metadata: top-level bits 2 (matches tensor data)
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Card accuracy pass 2: remove unmeasured speed claims, honest brand labels
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Card accuracy sweep: honest brand labeling, remove dead links, upstream KV tip
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docs: upstream-first KV-cache guidance (q8_0/q4_0, mainline Hadamard rotation); fork demoted to experimental
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docs: remove AWQ variants from family table (AWQ lane paused, repos private)
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fix: family model links majentik/gemma4-* -> majentik/gemma-4-* (reported by @shramee in gemma-4-31B-TurboQuant-MLX-4bit#1)
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docs: Tier 2 polish — variant matrix + quant trade-off
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chore(card): enrich YAML frontmatter (pipeline_tag, language, library_name, inference)
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Add MLX quantized model with KV cache compression
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initial commit
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