Instructions to use wang-yang/Qwythos-9B-Claude-Mythos-5-MTPLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wang-yang/Qwythos-9B-Claude-Mythos-5-MTPLX 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("wang-yang/Qwythos-9B-Claude-Mythos-5-MTPLX")

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 wang-yang/Qwythos-9B-Claude-Mythos-5-MTPLX with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "wang-yang/Qwythos-9B-Claude-Mythos-5-MTPLX"

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": "wang-yang/Qwythos-9B-Claude-Mythos-5-MTPLX"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use wang-yang/Qwythos-9B-Claude-Mythos-5-MTPLX 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 "wang-yang/Qwythos-9B-Claude-Mythos-5-MTPLX"

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 wang-yang/Qwythos-9B-Claude-Mythos-5-MTPLX

Run Hermes

hermes

OpenClaw new

How to use wang-yang/Qwythos-9B-Claude-Mythos-5-MTPLX with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "wang-yang/Qwythos-9B-Claude-Mythos-5-MTPLX"

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 "wang-yang/Qwythos-9B-Claude-Mythos-5-MTPLX" \
  --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 wang-yang/Qwythos-9B-Claude-Mythos-5-MTPLX with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "wang-yang/Qwythos-9B-Claude-Mythos-5-MTPLX"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "wang-yang/Qwythos-9B-Claude-Mythos-5-MTPLX"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "wang-yang/Qwythos-9B-Claude-Mythos-5-MTPLX",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Qwythos-9B-Claude-Mythos-5-MTPLX / README.md

wang-yang

Publish MTPLX forged model

0183d1e verified 21 days ago

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metadata

language: en
pipeline_tag: text-generation
library_name: mlx
license: apache-2.0
base_model: empero-ai/Qwythos-9B-Claude-Mythos-5-1M
tags:
  - mlx
  - mtplx
  - mtp
  - multi-token-prediction
  - qwen3_5
  - apple-silicon

Qwythos-9B-Claude-Mythos-5-MTPLX

An MTPLX-ready build of empero-ai/Qwythos-9B-Claude-Mythos-5-1M with a working multi-token-prediction (MTP) head, for faster local decoding on Apple Silicon with exact rejection-sampling.

Body: converted to MLX and quantized to 4-bit (affine, group size 64).
MTP head: kept in bf16 (small but drives draft acceptance).
On disk: ~5.2 GB.

Why this exists

The original empero-ai/Qwythos-9B-Claude-Mythos-5-1M safetensors release ships without an MTP head — only the companion ...-1M-GGUF repo carries a restored Qwen3.5-compatible MTP head (for llama.cpp --spec-type draft-mtp). MTPLX runs MLX, not GGUF, and its Forge tool only carries over an existing MTP head rather than training one.

This build bridges the gap: the bf16 MTP head was extracted from the GGUF's Qwythos-9B-Claude-Mythos-5-1M-MTP-BF16.gguf (blk.32 nextn tensors), remapped to MLX/MTPLX key names, paired with the full-precision body, and run through mtplx forge build. MTP-contract calibration reached exact agreement (1.0) at depths 1–3, confirming the head matches the trunk.

Architecture


Base	Qwen3.5-9B (hybrid: 3:1 linear-attention to full-attention)
Layers	32 + 1 MTP layer
Hidden / heads	4096 / 16 attn, 4 KV, head_dim 256
Vocab	248320
Max context	1,048,576 (YaRN)
MTPLX arch_id	`qwen3-next-mtp`

Usage (MTPLX)

mtplx serve --model <this-model-dir> \
  --chat-template-profile tokenizer \
  --reasoning-parser qwen3

Notes:

This is a reasoning model — it thinks before answering (<think> block), so allow a generous max_tokens (>=2048).
Use --chat-template-profile tokenizer so the model's own chat template (with <think> handling) is used, and --reasoning-parser qwen3 to fold the thinking segment.
Default sampling: temperature=0.6, top_p=0.95, top_k=20.

Provenance & license

Body: empero-ai/Qwythos-9B-Claude-Mythos-5-1M
MTP head: empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF
Forged locally with MTPLX Forge (see mtplx_runtime.json for the contract and verification details).
License: Apache-2.0, inherited from the base model. All credit for the base model and the restored MTP head belongs to Empero AI.