Instructions to use zecanard/gemma-4-26B-A4B-it-uncensored-abliterix-MLX-2bit-int2-affine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zecanard/gemma-4-26B-A4B-it-uncensored-abliterix-MLX-2bit-int2-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-4-26B-A4B-it-uncensored-abliterix-MLX-2bit-int2-affine")
config = load_config("zecanard/gemma-4-26B-A4B-it-uncensored-abliterix-MLX-2bit-int2-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

How to use zecanard/gemma-4-26B-A4B-it-uncensored-abliterix-MLX-2bit-int2-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-4-26B-A4B-it-uncensored-abliterix-MLX-2bit-int2-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-4-26B-A4B-it-uncensored-abliterix-MLX-2bit-int2-affine"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use zecanard/gemma-4-26B-A4B-it-uncensored-abliterix-MLX-2bit-int2-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-4-26B-A4B-it-uncensored-abliterix-MLX-2bit-int2-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-4-26B-A4B-it-uncensored-abliterix-MLX-2bit-int2-affine

Run Hermes

hermes

🦆 zecanard/gemma-4-26B-A4B-it-uncensored-abliterix-MLX-2bit-affine

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 3.407 bits per weight.

mlx_vlm.convert --quantize --q-bits 2 --q-group-size 32 --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:

{%- 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-2bit-affine --max-tokens 100 --temperature 0 --prompt "Describe this image." --image <path_to_image>

Downloads last month: 1,405

Safetensors

Model size

4B params

Tensor type

BF16

U32

MLX

Hardware compatibility

2-bit

Model tree for zecanard/gemma-4-26B-A4B-it-uncensored-abliterix-MLX-2bit-int2-affine

Base model

google/gemma-4-26B-A4B

Finetuned

google/gemma-4-26B-A4B-it

Quantized

wangzhang/gemma-4-26B-A4B-it-abliterix

Quantized

(14)

this model

🦆 zecanard/gemma-4-26B-A4B-it-uncensored-abliterix-MLX-2bit-affine

🌟 Quality

🛠️ Customizations

🖥️ Use with mlx

Model tree for zecanard/gemma-4-26B-A4B-it-uncensored-abliterix-MLX-2bit-int2-affine

🖥️ Use with `mlx`