Instructions to use Youssofal/MiMo-V2.6-Qwen-9B-MTPLX-Optimized-Speed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Youssofal/MiMo-V2.6-Qwen-9B-MTPLX-Optimized-Speed 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("Youssofal/MiMo-V2.6-Qwen-9B-MTPLX-Optimized-Speed") 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 Youssofal/MiMo-V2.6-Qwen-9B-MTPLX-Optimized-Speed with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Youssofal/MiMo-V2.6-Qwen-9B-MTPLX-Optimized-Speed"
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": "Youssofal/MiMo-V2.6-Qwen-9B-MTPLX-Optimized-Speed" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use Youssofal/MiMo-V2.6-Qwen-9B-MTPLX-Optimized-Speed with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "Youssofal/MiMo-V2.6-Qwen-9B-MTPLX-Optimized-Speed"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "Youssofal/MiMo-V2.6-Qwen-9B-MTPLX-Optimized-Speed" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Youssofal/MiMo-V2.6-Qwen-9B-MTPLX-Optimized-Speed", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use Youssofal/MiMo-V2.6-Qwen-9B-MTPLX-Optimized-Speed 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 "Youssofal/MiMo-V2.6-Qwen-9B-MTPLX-Optimized-Speed"
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 Youssofal/MiMo-V2.6-Qwen-9B-MTPLX-Optimized-Speed
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Youssofal/MiMo-V2.6-Qwen-9B-MTPLX-Optimized-Speed with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Youssofal/MiMo-V2.6-Qwen-9B-MTPLX-Optimized-Speed"
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 "Youssofal/MiMo-V2.6-Qwen-9B-MTPLX-Optimized-Speed" \ --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"
MTPLX: the fastest way to run MiMo-V2.6-Distill-Qwen-9B on a Mac. Native multi-token-prediction speculative decoding on Apple Silicon, exact at any temperature.
MiMo V2.6 Qwen 9B Optimized Speed
6-bit quantization. Xiaomi's agentic coding distill of Qwen 3.5 9B.
Xiaomi MiMo retrained Qwen 3.5 9B for coding, agent work, visual coding and security tasks, and the result is a big step up from the model it started from. This pack runs it on MTPLX with a multi-token-prediction head drafting ahead, on any Apple Silicon Mac with 16 GB or more.
Available from MTPLX 2.12.0. The Mac app lists it, and on M3, M4 and M5 Macs with 16 to 31 GB it is the second suggestion, right after Bonsai 2 27B.
Why this model
This is the MiMo-V2.6-Distill-Qwen-9B MLX pack for MTPLX, the fastest way to run MiMo V2.6 on a Mac. Xiaomi's results against the Qwen 3.5 9B it started from, from Xiaomi's model card:
| Benchmark | Qwen3.5-9B | MiMo-V2.6-Distill-Qwen-9B |
|---|---|---|
| SWE Pro | 32.0 | 44.6 |
| Terminal Bench 2.1 | 27.0 | 37.1 |
| Toolathlon-Verified | 25.9 | 35.2 |
| SWE Verified | 60.0 | 61.1 |
Same size as Qwen 3.5 9B, a much stronger coding and agent model.
How it is built
- 6-bit quantization with 64-weight groups across the model, the recipe of our Qwen 3.5 9B Optimized Speed. Against the full-precision model it keeps 97.3 percent top-1 agreement, with a KL divergence of 0.0054.
- The Qwen 3.5 9B multi-token-prediction head drafts ahead, and the full model verifies its drafts in one pass.
| Download | 8.7 GB |
| Peak while serving | 8.7 GiB at a 15K-token context |
| Recommended Macs | 16 GB+ unified memory (18 GB+ for agent clients such as OpenCode) |
| Context window | 20,480 tokens on 16 GB, 45,056 on 18 GB, 192,512 on 24 GB, up to 262,144 |
| MTP depth | 2 |
| Sampling | temperature 0.6, top-p 0.95, top-k 20 (Xiaomi's settings) |
The serving contract ships inside mtplx_runtime.json. MTPLX reads it on
load. Drafts are accepted with the probability-ratio rule plus residual
resampling, so the output follows the model's own distribution at any
temperature.
Use it
Mac app (MTPLX 2.12.0 or later): download at mtplx.com, pick "MiMo V2.6 Qwen 9B Optimized Speed".
Command line (MTPLX 2.12.0 or later):
pip install mtplx
mtplx serve --model Youssofal/MiMo-V2.6-Qwen-9B-MTPLX-Optimized-Speed
Siblings: Qwen 3.5 9B Optimized Speed and Bonsai 2 27B Optimized Speed (a 27B-class model in about the same memory).
Base model: XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B (MIT), a fine-tune of Qwen/Qwen3.5-9B (Apache 2.0), whose draft head this pack uses.
- Downloads last month
- 698
6-bit
Model tree for Youssofal/MiMo-V2.6-Qwen-9B-MTPLX-Optimized-Speed
Base model
Qwen/Qwen3.5-9B-Base