Instructions to use rayray916/Qwen3.6-35B-A3B-uncensored-heretic-Native-MTP-Preserved-oQ3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rayray916/Qwen3.6-35B-A3B-uncensored-heretic-Native-MTP-Preserved-oQ3 with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("rayray916/Qwen3.6-35B-A3B-uncensored-heretic-Native-MTP-Preserved-oQ3")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use rayray916/Qwen3.6-35B-A3B-uncensored-heretic-Native-MTP-Preserved-oQ3 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "rayray916/Qwen3.6-35B-A3B-uncensored-heretic-Native-MTP-Preserved-oQ3"

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": "rayray916/Qwen3.6-35B-A3B-uncensored-heretic-Native-MTP-Preserved-oQ3"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use rayray916/Qwen3.6-35B-A3B-uncensored-heretic-Native-MTP-Preserved-oQ3 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "rayray916/Qwen3.6-35B-A3B-uncensored-heretic-Native-MTP-Preserved-oQ3"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "rayray916/Qwen3.6-35B-A3B-uncensored-heretic-Native-MTP-Preserved-oQ3"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "rayray916/Qwen3.6-35B-A3B-uncensored-heretic-Native-MTP-Preserved-oQ3",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use rayray916/Qwen3.6-35B-A3B-uncensored-heretic-Native-MTP-Preserved-oQ3 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 "rayray916/Qwen3.6-35B-A3B-uncensored-heretic-Native-MTP-Preserved-oQ3"

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 rayray916/Qwen3.6-35B-A3B-uncensored-heretic-Native-MTP-Preserved-oQ3

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use rayray916/Qwen3.6-35B-A3B-uncensored-heretic-Native-MTP-Preserved-oQ3 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "rayray916/Qwen3.6-35B-A3B-uncensored-heretic-Native-MTP-Preserved-oQ3"

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 "rayray916/Qwen3.6-35B-A3B-uncensored-heretic-Native-MTP-Preserved-oQ3" \
  --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"

Qwen3.6-35B-A3B Heretic — oQ3 (3-bit, MLX)

A sensitivity-guided ~3-bit (oQ3) quant of the abliterated Qwen3.6-35B-A3B "Heretic" (Native-MTP-Preserved) model, built on-device with omlx's mixed-precision oQ quantizer. MoE arch qwen3_5_moe (35B total / ~3B active). Apple-Silicon MLX format.

Effective precision: ~3.6 bpw (≈16 GB weights) — oQ keeps sensitive layers higher-bit, so it punches above its nominal bit-width.
Abliterated / uncensored. Use responsibly; you are accountable for your outputs.

Why this quant

Despite being the smallest/fastest quant of the family, it matched the higher-bit builds on every benchmark tried (M4 Max, thinking modes as noted):

Test	Score
Hard reasoning + code (8 tasks)	8/8
Harder quality (multi-digit math, DP) (6)	6/6
General knowledge (20 facts)	20/20
Multi-step agentic tool-use (5)	5/5
Agentic "gauntlet" — flaky-tool retry, traps, branch (7, thinking-OFF)	7/7
Throughput	~100+ tok/s

Run it

Serve with omlx (or any MLX-LM runtime) on Apple Silicon:

omlx serve --port 8000
# then request model "Qwen3.6-35B-A3B-uncensored-heretic-Native-MTP-Preserved-oQ3"

Best as an agent default with thinking OFF (cleanest tool-discipline); flip thinking ON for hard multi-step reasoning.

Private quant for personal use. License inherits from the base model.

Downloads last month: 277

Safetensors

Model size

35B params

Tensor type

U32

BF16

MLX

Hardware compatibility

3-bit