Instructions to use xunkutech-ai/Qwythos-9B-Claude-Mythos-5-1M-MLX-oQ4-mtp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use xunkutech-ai/Qwythos-9B-Claude-Mythos-5-1M-MLX-oQ4-mtp 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("xunkutech-ai/Qwythos-9B-Claude-Mythos-5-1M-MLX-oQ4-mtp") 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
- Pi
How to use xunkutech-ai/Qwythos-9B-Claude-Mythos-5-1M-MLX-oQ4-mtp with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "xunkutech-ai/Qwythos-9B-Claude-Mythos-5-1M-MLX-oQ4-mtp"
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": "xunkutech-ai/Qwythos-9B-Claude-Mythos-5-1M-MLX-oQ4-mtp" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use xunkutech-ai/Qwythos-9B-Claude-Mythos-5-1M-MLX-oQ4-mtp 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 "xunkutech-ai/Qwythos-9B-Claude-Mythos-5-1M-MLX-oQ4-mtp"
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 xunkutech-ai/Qwythos-9B-Claude-Mythos-5-1M-MLX-oQ4-mtp
Run Hermes
hermes
- OpenClaw new
How to use xunkutech-ai/Qwythos-9B-Claude-Mythos-5-1M-MLX-oQ4-mtp with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "xunkutech-ai/Qwythos-9B-Claude-Mythos-5-1M-MLX-oQ4-mtp"
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 "xunkutech-ai/Qwythos-9B-Claude-Mythos-5-1M-MLX-oQ4-mtp" \ --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 xunkutech-ai/Qwythos-9B-Claude-Mythos-5-1M-MLX-oQ4-mtp with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "xunkutech-ai/Qwythos-9B-Claude-Mythos-5-1M-MLX-oQ4-mtp"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "xunkutech-ai/Qwythos-9B-Claude-Mythos-5-1M-MLX-oQ4-mtp" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "xunkutech-ai/Qwythos-9B-Claude-Mythos-5-1M-MLX-oQ4-mtp", "messages": [ {"role": "user", "content": "Hello"} ] }'
Qwythos-9B-Claude-Mythos-5-1M-MLX-oQ4-mtp
An oQ4 quantized MLX version of Qwythos-9B-Claude-Mythos-5-1M-MLX-bf16-mtp with integrated Multi-Token Prediction (MTP) head for accelerated speculative decoding on Apple Silicon.
Quantized with oMLX oQ4 — a smart mixed-precision quantization that selectively preserves critical layers while compressing the rest for significant memory savings with minimal quality loss.
Model Summary
- Format: MLX safetensors, oQ4 quantized
- Precision: Mixed (oQ4 smart quantization)
- Parameters: ~9B total
- Context length: 1,048,576 tokens
- Architecture: Qwen3.5-style hybrid attention text model with MTP head
- MTP layers: 1 (embedded,
mtp_num_hidden_layers: 1) - Primary use: local text generation and reasoning on Apple Silicon with MTP-accelerated decoding
- Base model:
xunkutech-ai/Qwythos-9B-Claude-Mythos-5-1M-MLX-bf16-mtp
Compatibility Notice
oMLX is required to load this model with MTP support. Standard
mlx-vlm,mlx-lm, and LM Studio do NOT support MTP and will fail with:ValueError: Received 15 parameters not in model: language_model.mtp.*.
Installation
pip install -U git+https://github.com/jundot/omlx.git
Usage with oMLX
# Start server with MTP draft-mode
omlx serve --model /path/to/Qwythos-9B-Claude-Mythos-5-1M-MLX-oQ4-mtp
from omlx import load, generate
model_id = "/path/to/Qwythos-9B-Claude-Mythos-5-1M-MLX-oQ4-mtp"
model, tokenizer = load(model_id)
result = generate(
model=model,
tokenizer=tokenizer,
prompt="Explain speculative decoding with MTP.",
max_tokens=2048,
temperature=0.6,
)
print(result)
Files
| File | Size | Description |
|---|---|---|
config.json |
~22 KB | oQ4 quantized config with mtp_num_hidden_layers: 1 |
model-00001-of-00002.safetensors |
~5.0 GB | Quantized shard 1/2 |
model-00002-of-00002.safetensors |
~1.2 GB | Quantized shard 2/2 (+ MTP head) |
tokenizer.json |
~20 MB | Qwen3.5 tokenizer |
chat_template.jinja |
~8 KB | Qwythos chat template |
Recommended Sampling
generation_kwargs = {
"temperature": 0.6,
"top_p": 0.95,
"top_k": 20,
"repetition_penalty": 1.05,
"max_tokens": 4096,
}
License
Released under the same license as the upstream model: Apache-2.0.
Acknowledgements
- Original model: empero-ai/Qwythos-9B-Claude-Mythos-5-1M
- Original developers: Empero AI
- Base family: Qwen3.5
- MTP support: oMLX
- MLX ecosystem: Apple MLX
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Model tree for xunkutech-ai/Qwythos-9B-Claude-Mythos-5-1M-MLX-oQ4-mtp
Base model
Qwen/Qwen3.5-9B-Base