Instructions to use Youssofal/Qwen3.6-35B-A3B-MTPLX-Optimized-Speed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Youssofal/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-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

How to use Youssofal/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-MTPLX-Optimized-Speed"

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-Speed"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Youssofal/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-MTPLX-Optimized-Speed

Run Hermes

hermes

MLX LM

How to use Youssofal/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-MTPLX-Optimized-Speed"

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-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/Qwen3.6-35B-A3B-MTPLX-Optimized-Speed",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Qwen3.6-35B-A3B MTPLX Optimized Speed

Fast local 35B-A3B inference for Apple Silicon, packaged for MTPLX native Multi-Token-Prediction speculative decoding.

This is the speed-focused 35B checkpoint: a compact 4-bit MLX body with calibrated INT4 MTP heads, tuned so MTPLX can draft more confidently and verify less work per generated token.

Run It

brew install youssofal/mtplx/mtplx
mtplx start
mtplx run "hello" --model Youssofal/Qwen3.6-35B-A3B-MTPLX-Optimized-Speed

For an OpenAI-compatible local server:

mtplx serve --model Youssofal/Qwen3.6-35B-A3B-MTPLX-Optimized-Speed --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 using a separate drafter model.

This checkpoint is optimized for that path. MTPLX reads mtplx_runtime.json and selects the measured speed defaults automatically.

Recommended Runtime Defaults

Setting	Value
Backend	`qwen3-next-mtp`
Default depth	`D1`
Verifier strategy	`target_prefix`
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 and a 32k token response allowance. All recorded runs finished naturally without a length stop.

Mode	TPS	Verify time	Acceptance
AR baseline	94.46	-	-
D1 promoted default	138.39	69.30s	0.8858
D2 comparison	135.66	49.23s	0.8701, 0.6409
D3 comparison	107.67	46.45s	0.8291, 0.5414, 0.2783

D1 is the promoted default because it gives the best real-use balance: strong TPS, high acceptance, and lower verify cost than the earlier 35B speed baseline.

Model Build

Component	Format
Main body	4-bit MLX affine, group size 64
Router and gate tensors	8-bit where recorded by config
MTP numbered-expert weights	calibrated INT4, group size 32
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 4-bit body shards
mtp.safetensors: calibrated INT4 MTP sidecar
mtplx_runtime.json: MTPLX runtime contract and measured defaults
MTPLX_PUBLISH_MANIFEST.json: file sizes and benchmark summary
tokenizer and config files for local loading

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Safetensors

Model size

6B params

Tensor type

BF16

U32

MLX

Hardware compatibility

4-bit