Instructions to use TheCluster/Qwen3.5-122B-A10B-Heretic-v2-MLX-mixed-3.8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TheCluster/Qwen3.5-122B-A10B-Heretic-v2-MLX-mixed-3.8bit 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("TheCluster/Qwen3.5-122B-A10B-Heretic-v2-MLX-mixed-3.8bit")
config = load_config("TheCluster/Qwen3.5-122B-A10B-Heretic-v2-MLX-mixed-3.8bit")

# 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 TheCluster/Qwen3.5-122B-A10B-Heretic-v2-MLX-mixed-3.8bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "TheCluster/Qwen3.5-122B-A10B-Heretic-v2-MLX-mixed-3.8bit"

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": "TheCluster/Qwen3.5-122B-A10B-Heretic-v2-MLX-mixed-3.8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use TheCluster/Qwen3.5-122B-A10B-Heretic-v2-MLX-mixed-3.8bit 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 "TheCluster/Qwen3.5-122B-A10B-Heretic-v2-MLX-mixed-3.8bit"

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 TheCluster/Qwen3.5-122B-A10B-Heretic-v2-MLX-mixed-3.8bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use TheCluster/Qwen3.5-122B-A10B-Heretic-v2-MLX-mixed-3.8bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "TheCluster/Qwen3.5-122B-A10B-Heretic-v2-MLX-mixed-3.8bit"

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 "TheCluster/Qwen3.5-122B-A10B-Heretic-v2-MLX-mixed-3.8bit" \
  --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"

If you like my work, you can support me

Qwen3.5-122B-A10B Heretic V2

Quality: quantized (mixed quants per tensor, group size: 32, 3.81 bpw)

Quantization: 3 bit for experts, 4 bit for shared experts and attention layers, 6 bit for embeddings & head.

This is an uncensored version of Qwen/Qwen3.5-122B-A10B, made using Heretic v1.2.0 with Multi-directional refusal supression

Abliteration metrics

Metric	This model	Original model (Qwen/Qwen3.5-122B-A10B)
KL divergence	0.0646	0 (by definition)
Refusals	16/100	84/100

Sampling Parameters:

I suggest using the following sets of sampling parameters depending on the mode and task type:
- Thinking mode for general tasks:
  temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0
- Thinking mode for precise coding tasks (e.g., WebDev):
  temperature=0.6, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0
- Instruct (or non-thinking) mode for general tasks:
  temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0
- Instruct (or non-thinking) mode for reasoning tasks:
  temperature=1.0, top_p=1.0, top_k=40, min_p=0.0, presence_penalty=2.0, repetition_penalty=1.0
For supported frameworks, you can adjust the presence_penalty parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.