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
| base_model: google/gemma-4-12B | |
| license: gemma | |
| library_name: mlx | |
| pipeline_tag: image-text-to-text | |
| tags: | |
| - mlx | |
| - quantized | |
| - gemma | |
| - gemma4 | |
| - image-text-to-text | |
| - apple-silicon | |
| - rotorquant | |
| # gemma-4-12B — RotorQuant MLX 8bit | |
| [`google/gemma-4-12B`](https://huggingface.co/google/gemma-4-12B) @ `023679ed352de9bb66cc873c9009ce3482585c08` quantized pack, published as `majentik/gemma-4-12B-RotorQuant-MLX-8bit`. | |
| ## Method | |
| MLX quantization via mlx-vlm 0.6.3 (8bit, group_size 64); vision + audio towers retained in BF16 (not quantized). | |
| ## Release line | |
| Released under the **RotorQuant** line. RotorQuant and TurboQuant are this project's release labels for this pack, not distinct quantization algorithms — both brand repos for a given tier carry byte-identical weights, produced once and published under two names. No brand-specific speedup is claimed or measured for either label. | |
| ## Modality | |
| This pack is `image-text-to-text` capable: the vision and audio towers ship in BF16 alongside the quantized text tower, so image (and audio) inputs are supported end to end via `mlx-vlm`. | |
| **Base-model note:** this is the raw completion model (not instruction-tuned). It is not instruction-aligned for multimodal Q&A — expect free-form continuation behavior rather than chat-style image description, even though the vision/audio towers are present. | |
| **mlx-vlm suppress-tokens note:** the base model's `generation_config.json` carries `suppress_tokens` for six multimodal placeholder token ids so that `transformers` generation is clean. `mlx-vlm` does not honor `suppress_tokens` automatically, so text-only generation with `mlx-vlm` can leak placeholder tokens (observed as a degenerate `A<image|>A<image|>...` loop in smoke testing) unless you suppress them yourself: | |
| ```python | |
| from mlx_vlm import load, generate | |
| model, processor = load("majentik/gemma-4-12B-RotorQuant-MLX-8bit") | |
| suppress = {255999: -1e9, 256000: -1e9, 258880: -1e9, 258881: -1e9, 258882: -1e9, 258883: -1e9} | |
| output = generate( | |
| model, processor, prompt, | |
| max_tokens=256, | |
| logit_bias=suppress, | |
| ) | |
| ``` | |
| ## License | |
| Governed by the [Gemma Terms of Use](https://ai.google.dev/gemma/terms). See the upstream repo for the full license text. | |