Instructions to use zecanard/gemma-4-31B-it-uncensored-abliterix-MLX-6bit-int6-affine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use zecanard/gemma-4-31B-it-uncensored-abliterix-MLX-6bit-int6-affine 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("zecanard/gemma-4-31B-it-uncensored-abliterix-MLX-6bit-int6-affine") config = load_config("zecanard/gemma-4-31B-it-uncensored-abliterix-MLX-6bit-int6-affine") # 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
- Pi
How to use zecanard/gemma-4-31B-it-uncensored-abliterix-MLX-6bit-int6-affine with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "zecanard/gemma-4-31B-it-uncensored-abliterix-MLX-6bit-int6-affine"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "zecanard/gemma-4-31B-it-uncensored-abliterix-MLX-6bit-int6-affine" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use zecanard/gemma-4-31B-it-uncensored-abliterix-MLX-6bit-int6-affine with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "zecanard/gemma-4-31B-it-uncensored-abliterix-MLX-6bit-int6-affine"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default zecanard/gemma-4-31B-it-uncensored-abliterix-MLX-6bit-int6-affine
Run Hermes
hermes
🦆 zecanard/gemma-4-31B-it-uncensored-abliterix-MLX-6bit-int6-affine
This model was converted to MLX from wangzhang/gemma-4-31B-it-abliterated using mlx-vlm version 0.5.0.
Please refer to the original model card for more details.
🌟 Quality
Quantized vision language model with an effective 8.169 bits per weight.
mlx_vlm.convert --quantize --q-group-size 32 --q-bits 6 --q-mode affine
🛠️ Customizations
This quant 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 also 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-4-31B-it-uncensored-abliterix-MLX-6bit-int6-affine --max-tokens 100 --temperature 0 --prompt "Describe this image." --image <path_to_image>
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6-bit
Model tree for zecanard/gemma-4-31B-it-uncensored-abliterix-MLX-6bit-int6-affine
Base model
google/gemma-4-31B