Instructions to use ToPo-ToPo/Qwythos-9B-Claude-Mythos-5-1M-mlx-8bit-noident with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ToPo-ToPo/Qwythos-9B-Claude-Mythos-5-1M-mlx-8bit-noident 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("ToPo-ToPo/Qwythos-9B-Claude-Mythos-5-1M-mlx-8bit-noident")
config = load_config("ToPo-ToPo/Qwythos-9B-Claude-Mythos-5-1M-mlx-8bit-noident")

# 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 ToPo-ToPo/Qwythos-9B-Claude-Mythos-5-1M-mlx-8bit-noident with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "ToPo-ToPo/Qwythos-9B-Claude-Mythos-5-1M-mlx-8bit-noident"

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": "ToPo-ToPo/Qwythos-9B-Claude-Mythos-5-1M-mlx-8bit-noident"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ToPo-ToPo/Qwythos-9B-Claude-Mythos-5-1M-mlx-8bit-noident 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 "ToPo-ToPo/Qwythos-9B-Claude-Mythos-5-1M-mlx-8bit-noident"

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 ToPo-ToPo/Qwythos-9B-Claude-Mythos-5-1M-mlx-8bit-noident

Run Hermes

hermes

OpenClaw new

How to use ToPo-ToPo/Qwythos-9B-Claude-Mythos-5-1M-mlx-8bit-noident with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "ToPo-ToPo/Qwythos-9B-Claude-Mythos-5-1M-mlx-8bit-noident"

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 "ToPo-ToPo/Qwythos-9B-Claude-Mythos-5-1M-mlx-8bit-noident" \
  --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"

Qwythos-9B-Claude-Mythos-5-1M MLX (8bit, noident)

A conversion of empero-ai/Qwythos-9B-Claude-Mythos-5-1M to Apple's MLX format, quantized to 8bit.

Conversion tool: mlx-maker / mlx-vlm
Source model: empero-ai/Qwythos-9B-Claude-Mythos-5-1M
Base model: Qwen/Qwen3.5-9B
License: Apache-2.0 (inherited from the source model)

About the chat template

Modified variant: both the forced identity and the default system prompt have been completely removed from chat_template.jinja. A system block is emitted only when the caller provides one. The weights are unchanged and identical to the same-quantization base variant.

Usage (mlx-vlm)

uv add mlx-vlm

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template

model, processor = load("ToPo-ToPo/Qwythos-9B-Claude-Mythos-5-1M-mlx-8bit-noident")
messages = [{"role": "user", "content": "Hello"}]
prompt = apply_chat_template(processor, model.config, messages)
print(generate(model, processor, prompt, max_tokens=256))