Instructions to use TheCluster/Gemma-4-26B-A4B-MLX-mixed-7bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use TheCluster/Gemma-4-26B-A4B-MLX-mixed-7bit 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("TheCluster/Gemma-4-26B-A4B-MLX-mixed-7bit") config = load_config("TheCluster/Gemma-4-26B-A4B-MLX-mixed-7bit") # 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
Gemma-4-26B-A4B
Quality: quantized (mixed quants per tensor, group size: 32, 7.551 bpw)
The various layers use 6-, 7-, or 8-bit affine quantization with a group size 32; embeddings are saved in bf16.
Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on small models) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages.
Source
This model was converted to MLX format from google/gemma-4-26B-A4B-it using mlx-vlm version 0.4.4.
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Model size
27B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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7-bit
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