Instructions to use WaveCut/Qwythos-9B-v2-Heretic-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WaveCut/Qwythos-9B-v2-Heretic-MLX-8bit 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("WaveCut/Qwythos-9B-v2-Heretic-MLX-8bit")

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 WaveCut/Qwythos-9B-v2-Heretic-MLX-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 "WaveCut/Qwythos-9B-v2-Heretic-MLX-8bit"

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": "WaveCut/Qwythos-9B-v2-Heretic-MLX-8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use WaveCut/Qwythos-9B-v2-Heretic-MLX-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 "WaveCut/Qwythos-9B-v2-Heretic-MLX-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 WaveCut/Qwythos-9B-v2-Heretic-MLX-8bit

Run Hermes

hermes

OpenClaw new

How to use WaveCut/Qwythos-9B-v2-Heretic-MLX-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 "WaveCut/Qwythos-9B-v2-Heretic-MLX-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 "WaveCut/Qwythos-9B-v2-Heretic-MLX-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"

MLX LM

How to use WaveCut/Qwythos-9B-v2-Heretic-MLX-8bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "WaveCut/Qwythos-9B-v2-Heretic-MLX-8bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "WaveCut/Qwythos-9B-v2-Heretic-MLX-8bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "WaveCut/Qwythos-9B-v2-Heretic-MLX-8bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Qwythos-9B-v2-Heretic-MLX-8bit

MLX 8-bit quantization of WaveCut/Qwythos-9B-v2-Heretic — the Heretic-decensored version of empero-ai/Qwythos-9B-v2. Built for Apple Silicon (M1/M2/M3/M4).

Specs

Field	Value
Bits/weight	8.501
File size	~9.5 GB
Minimum RAM	~11 GB unified memory

Quantization

Step	Tool	Version
Convert + quantize	`mlx_lm.convert`	mlx-lm 0.31.3 (mlx 0.31.x)
Quant mode	`affine` (default)	`-q --q-bits 8`

python -m mlx_lm.convert \
  --hf-path WaveCut/Qwythos-9B-v2-Heretic \
  -q --q-bits 8 \
  --upload-repo WaveCut/Qwythos-9B-v2-Heretic-MLX-8bit

Usage

from mlx_lm import load, generate
model, tokenizer = load("WaveCut/Qwythos-9B-v2-Heretic-MLX-8bit")
response = generate(model, tokenizer, prompt="Hello", max_tokens=256)
print(response)

# CLI
mlx_lm.generate --model WaveCut/Qwythos-9B-v2-Heretic-MLX-8bit --prompt "Hello"

Architecture

Qwen3.5 hybrid — 32 blocks mixing attention and SSM (Mamba-style) layers. Supported in mlx-lm ≥ 0.31.0.

Disclaimer

Uncensored (safety alignment removed via Heretic). The original empero-ai/Qwythos-9B-v2 maintainers are not affiliated with this derivative. Use responsibly.