Instructions to use zecanard/gemma-4-26B-A4B-it-uncensored-abliterix-MLX-4bit-nvfp4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use zecanard/gemma-4-26B-A4B-it-uncensored-abliterix-MLX-4bit-nvfp4 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-4-26B-A4B-it-uncensored-abliterix-MLX-4bit-nvfp4") config = load_config("zecanard/gemma-4-26B-A4B-it-uncensored-abliterix-MLX-4bit-nvfp4") # 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-4-26B-A4B-it-uncensored-abliterix-MLX-4bit-nvfp4 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-4-26B-A4B-it-uncensored-abliterix-MLX-4bit-nvfp4"
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-4-26B-A4B-it-uncensored-abliterix-MLX-4bit-nvfp4" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use zecanard/gemma-4-26B-A4B-it-uncensored-abliterix-MLX-4bit-nvfp4 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-4-26B-A4B-it-uncensored-abliterix-MLX-4bit-nvfp4"
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-4-26B-A4B-it-uncensored-abliterix-MLX-4bit-nvfp4
Run Hermes
hermes
🦆 zecanard/gemma-4-26B-A4B-it-uncensored-abliterix-MLX-4bit-nvfp4
This model was converted to MLX from wangzhang/gemma-4-26B-A4B-it-abliterix using mlx-vlm version 0.4.4.
Please refer to the original model card for more details.
🌟 Quality
Quantized vision language model with 4.843 bits per weight.
mlx_vlm.convert --quantize --q-bits 4 --q-group-size 16 --q-mode nvfp4
🛠️ 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:
{%- 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-4-26B-A4B-it-uncensored-abliterix-MLX-4bit-nvfp4 --max-tokens 100 --temperature 0 --prompt "Describe this image." --image <path_to_image>
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4-bit
Model tree for zecanard/gemma-4-26B-A4B-it-uncensored-abliterix-MLX-4bit-nvfp4
Base model
google/gemma-4-26B-A4B