Instructions to use Youssofal/Qwen3.6-35B-A3B-MTPLX-Optimized-Balance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Youssofal/Qwen3.6-35B-A3B-MTPLX-Optimized-Balance 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/Qwen3.6-35B-A3B-MTPLX-Optimized-Balance") 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/Qwen3.6-35B-A3B-MTPLX-Optimized-Balance 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/Qwen3.6-35B-A3B-MTPLX-Optimized-Balance"
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": "Youssofal/Qwen3.6-35B-A3B-MTPLX-Optimized-Balance" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Youssofal/Qwen3.6-35B-A3B-MTPLX-Optimized-Balance 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/Qwen3.6-35B-A3B-MTPLX-Optimized-Balance"
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/Qwen3.6-35B-A3B-MTPLX-Optimized-Balance
Run Hermes
hermes
- MLX LM
How to use Youssofal/Qwen3.6-35B-A3B-MTPLX-Optimized-Balance 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/Qwen3.6-35B-A3B-MTPLX-Optimized-Balance"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "Youssofal/Qwen3.6-35B-A3B-MTPLX-Optimized-Balance" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Youssofal/Qwen3.6-35B-A3B-MTPLX-Optimized-Balance", "messages": [ {"role": "user", "content": "Hello"} ] }'
Qwen3.6-35B-A3B MTPLX Optimized Balance
Balanced local 35B-A3B inference for Apple Silicon, packaged for MTPLX native Multi-Token-Prediction speculative decoding.
This checkpoint is the balanced 35B release: a 6-bit MLX body paired with calibrated INT4 MTP heads. It is tuned for strong reasoning-on generation speed while keeping prompt processing and memory use practical across normal coding contexts.
Run It
brew install youssofal/mtplx/mtplx
mtplx start
mtplx run "hello" --model Youssofal/Qwen3.6-35B-A3B-MTPLX-Optimized-Balance
For an OpenAI-compatible local server:
mtplx serve --model Youssofal/Qwen3.6-35B-A3B-MTPLX-Optimized-Balance --profile sustained --max --port 8000 --no-stats-footer
Why This Exists
MTPLX uses the model's own MTP heads to generate draft tokens, then verifies them with the main model. When the draft heads are well-matched, you get higher throughput without running a separate drafter model.
Optimized Balance is built for that path. MTPLX reads mtplx_runtime.json and
selects the measured D2 defaults automatically.
Recommended Runtime Defaults
| Setting | Value |
|---|---|
| Backend | qwen3-next-mtp |
| Default depth | D2 |
| Target sampler | temp=0.60, top_p=0.95, top_k=20 |
| Draft sampler | temp=0.60, top_p=0.95, top_k=20 |
| Profile | sustained |
| Benchmark fan mode | max |
Performance
Measured in MTPLX Sustained Max on Apple Silicon with reasoning enabled.
Generation
| Mode | TPS | Verify time | Acceptance |
|---|---|---|---|
| AR baseline | 86.30 | - | - |
| D1 comparison | 123.00 | 7.24s | 0.8329 |
| D2 promoted default | 126.43 | 6.62s | 0.8134, 0.5048 |
| D3 comparison | 112.43 | 7.16s | 0.7802, 0.4709, 0.2514 |
D2 is the promoted default because it gives the best balance of throughput, acceptance, and verify cost. A three-run D2 repeat averaged 123.44 tok/s, with every run above 122 tok/s.
Prompt Processing
| Context | Prompt TPS |
|---|---|
| 512 | 1,756.8 |
| 1k | 3,339.2 |
| 2k | 4,109.8 |
| 4k | 4,048.7 |
| 8k | 3,872.0 |
| 16k | 3,162.3 |
| 32k | 2,761.3 |
| 64k | 1,834.2 |
Average prompt processing across the 512-to-64k ladder was 3,110.5 tok/s.
Model Build
| Component | Format |
|---|---|
| Main body | 6-bit MLX affine, group size 64 |
| Router and gate tensors | 8-bit where recorded by config |
| MTP numbered-expert weights | INT4 MLX affine, group size 64 |
| Norms, scales, biases, plain tensors | BF16 |
This is not a full-precision checkpoint. It is built for fast local use on Apple Silicon through MTPLX.
Files
model-*.safetensors: MLX 6-bit body shardsmtp.safetensors: calibrated INT4 MTP sidecarmtplx_runtime.json: MTPLX runtime contract and measured defaultsMTPLX_PUBLISH_MANIFEST.json: file sizes and benchmark summary- tokenizer and config files for local loading
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