Instructions to use Youssofal/Qwen3.6-35B-A3B-MTPLX-Optimized-Balance-FP16 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-Balance-FP16 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-FP16")

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-Balance-FP16 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-FP16"

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/Qwen3.6-35B-A3B-MTPLX-Optimized-Balance-FP16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use Youssofal/Qwen3.6-35B-A3B-MTPLX-Optimized-Balance-FP16 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-FP16"

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-FP16"
# 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-FP16",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use Youssofal/Qwen3.6-35B-A3B-MTPLX-Optimized-Balance-FP16 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-FP16"

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-FP16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Youssofal/Qwen3.6-35B-A3B-MTPLX-Optimized-Balance-FP16 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/Qwen3.6-35B-A3B-MTPLX-Optimized-Balance-FP16"

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/Qwen3.6-35B-A3B-MTPLX-Optimized-Balance-FP16" \
  --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"

Qwen3.6-35B-A3B MTPLX Optimized Balance FP16

FP16 compatibility sibling for Youssofal/Qwen3.6-35B-A3B-MTPLX-Optimized-Balance, packaged for MTPLX native Multi-Token-Prediction speculative decoding on older Apple Silicon.

This variant keeps the same release model family as Qwen3.6-35B-A3B Optimized Balance. Packed quantized tensors stay packed; BF16 floating tensors are converted to FP16 so M1 and M2 Macs can use the FP16-friendly path without changing the artifact's intended speed/quality tier.

Run It

brew install youssofal/mtplx/mtplx
mtplx start
mtplx run "hello" --model Youssofal/Qwen3.6-35B-A3B-MTPLX-Optimized-Balance-FP16

For an OpenAI-compatible local server:

mtplx serve --model Youssofal/Qwen3.6-35B-A3B-MTPLX-Optimized-Balance-FP16 --profile sustained --max --port 8000 --no-stats-footer

Device Routing

M1/M2 Apple Silicon: MTPLX may prefer this FP16 sibling.
M3/M4/M5 Apple Silicon: MTPLX keeps the normal optimized artifact by default.
Explicit --model always wins.

Recommended Runtime Defaults

Setting	Value
Backend	`qwen3-next-mtp`
Default depth	`D2`
Profile	`sustained`
Precision policy	preserve packed tensors; convert BF16 floats to FP16

Source Performance Baseline

These are the source artifact numbers used as the regression baseline. The FP16 variant should stay close, but it is primarily a device-compatibility release.

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

Model Build

Component	Format
Main body	6-bit MLX affine body with BF16 float leaves converted to FP16
MTP sidecar	same MTP policy as source; BF16 float leaves converted to FP16
Packed quantized tensors	preserved without requantization
Manifest	`MTPLX_FP16_CONVERSION_MANIFEST.json` records tensor-level conversions

This is not a full-precision checkpoint. It is built for fast local use on Apple Silicon through MTPLX.

Downloads last month: 601

Safetensors

Model size

35B params

Tensor type

U32

F16

MLX

Hardware compatibility

6-bit