Instructions to use zecanard/gemma-4-E2B-it-uncensored-abliterix-MLX-8bit-int8-affine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zecanard/gemma-4-E2B-it-uncensored-abliterix-MLX-8bit-int8-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-E2B-it-uncensored-abliterix-MLX-8bit-int8-affine")
config = load_config("zecanard/gemma-4-E2B-it-uncensored-abliterix-MLX-8bit-int8-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-E2B-it-uncensored-abliterix-MLX-8bit-int8-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-E2B-it-uncensored-abliterix-MLX-8bit-int8-affine"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/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-E2B-it-uncensored-abliterix-MLX-8bit-int8-affine"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use zecanard/gemma-4-E2B-it-uncensored-abliterix-MLX-8bit-int8-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-E2B-it-uncensored-abliterix-MLX-8bit-int8-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-E2B-it-uncensored-abliterix-MLX-8bit-int8-affine

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use zecanard/gemma-4-E2B-it-uncensored-abliterix-MLX-8bit-int8-affine with OpenClaw:

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-E2B-it-uncensored-abliterix-MLX-8bit-int8-affine"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "zecanard/gemma-4-E2B-it-uncensored-abliterix-MLX-8bit-int8-affine" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

gemma-4-E2B-it-uncensored-abliterix-MLX-8bit-int8-affine / README.md

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	---
	license: gemma
	base_model: wangzhang/gemma-4-E2B-it-abliterated
	language: en
	pipeline_tag: image-text-to-text
	library_name: mlx
	tags:
	- mlx
	- abliterix
	- decensored
	- abliterated
	- uncensored
	- gemma4
	- direct-weight-editing
	- multimodal
	---
	# 🦆 zecanard/gemma-4-E2B-it-uncensored-abliterix-MLX-8bit-int8-affine

	[This model](https://huggingface.co/zecanard/gemma-4-E2B-it-uncensored-abliterix-MLX-8bit-int8-affine) was converted to MLX from [`wangzhang/gemma-4-E2B-it-abliterated`](https://huggingface.co/wangzhang/gemma-4-E2B-it-abliterated) using `mlx-vlm` version 0.6.3.
	Please refer to the [original model card](https://huggingface.co/wangzhang/gemma-4-E2B-it-abliterated) for more details.

	## 🌟 Quality

	Quantized vision language model with an effective 9.693 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`

	```bash
	pip install -U mlx-vlm
	```

	```bash
	mlx_vlm.generate --model zecanard/gemma-4-E2B-it-uncensored-abliterix-MLX-8bit-int8-affine --max-tokens 100 --temperature 0 --prompt "Describe this image." --image <path_to_image>
	```