Instructions to use zecanard/gemma-4-12b-it-uncensored-heretic-MLX-8bit-int8-affine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use zecanard/gemma-4-12b-it-uncensored-heretic-MLX-8bit-int8-affine with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir gemma-4-12b-it-uncensored-heretic-MLX-8bit-int8-affine zecanard/gemma-4-12b-it-uncensored-heretic-MLX-8bit-int8-affine
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
🦆 zecanard/gemma-4-12b-it-uncensored-heretic-MLX-8bit-int8-affine
This model was converted to MLX from trohrbaugh/gemma-4-12b-it-heretic using mlx-vlm version 0.6.3.
Please refer to the original model card for more details.
🌟 Quality
Quantized vision language model with an effective 9.012 bits per weight.
mlx_vlm.convert --quantize --q-group-size 32 --q-bits 8 --q-mode affine
🛠️ Customizations
This quant includes a bugfix for tools calling. It is aware of the current date, and also enables thinking (if available). You may disable this behavior by deleting the following line from the chat template, or changing true to false:
{%- set enable_thinking = true %}
You may need to adjust your environment’s Reasoning Section Parsing to recognize <|channel>thought as the Start String, and <channel|> as the End String.
🖥️ Use with mlx
pip install -U mlx-vlm
mlx_vlm.generate --model zecanard/gemma-4-12b-it-uncensored-heretic-MLX-8bit-int8-affine --max-tokens 100 --temperature 0 --prompt "Describe this image." --image <path_to_image>
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