Image-Text-to-Text
MLX
Safetensors
gemma4_unified
quantized
gemma
gemma4
apple-silicon
rotorquant
8-bit precision
Instructions to use majentik/gemma-4-12B-RotorQuant-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use majentik/gemma-4-12B-RotorQuant-MLX-8bit 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-4-12B-RotorQuant-MLX-8bit") config = load_config("majentik/gemma-4-12B-RotorQuant-MLX-8bit") # 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
File size: 280 Bytes
8fd6024 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"bos_token_id": 2,
"do_sample": true,
"eos_token_id": 1,
"pad_token_id": 0,
"suppress_tokens": [
255999,
256000,
258880,
258881,
258882,
258883
],
"temperature": 1.0,
"top_k": 64,
"top_p": 0.95,
"transformers_version": "5.10.0.dev0"
} |