Instructions to use IHateCrickets/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-MLX-6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IHateCrickets/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-MLX-6bit 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("IHateCrickets/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-MLX-6bit")
config = load_config("IHateCrickets/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-MLX-6bit")

# 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 IHateCrickets/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-MLX-6bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "IHateCrickets/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-MLX-6bit"

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": "IHateCrickets/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-MLX-6bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use IHateCrickets/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-MLX-6bit 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 "IHateCrickets/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-MLX-6bit"

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 IHateCrickets/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-MLX-6bit

Run Hermes

hermes

IHateCrickets/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-MLX-6bit

A 6-bit MLX quantization of huihui-ai/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated.

This is an uncensored version of lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled created with abliteration (see remove-refusals-with-transformers to know more about it).

mlx-vlm

Please use the latest version of mlx-vlm.

Usage Warnings

Risk of Sensitive or Controversial Outputs: This model's safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs.
Not Suitable for All Audiences: Due to limited content filtering, the model's outputs may be inappropriate for public settings, underage users, or applications requiring high security.
Legal and Ethical Responsibilities: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences.
Research and Experimental Use: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications.
Monitoring and Review Recommendations: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content.
No Default Safety Guarantees: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.