Instructions to use TensorFold/Qwen3.6-35B-A3B-MLX-4bit-MTP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TensorFold/Qwen3.6-35B-A3B-MLX-4bit-MTP with MLX:

# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("TensorFold/Qwen3.6-35B-A3B-MLX-4bit-MTP")
config = load_config("TensorFold/Qwen3.6-35B-A3B-MLX-4bit-MTP")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use TensorFold/Qwen3.6-35B-A3B-MLX-4bit-MTP with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "TensorFold/Qwen3.6-35B-A3B-MLX-4bit-MTP"

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": "TensorFold/Qwen3.6-35B-A3B-MLX-4bit-MTP"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use TensorFold/Qwen3.6-35B-A3B-MLX-4bit-MTP 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 "TensorFold/Qwen3.6-35B-A3B-MLX-4bit-MTP"

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 TensorFold/Qwen3.6-35B-A3B-MLX-4bit-MTP

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use TensorFold/Qwen3.6-35B-A3B-MLX-4bit-MTP with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "TensorFold/Qwen3.6-35B-A3B-MLX-4bit-MTP"

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 "TensorFold/Qwen3.6-35B-A3B-MLX-4bit-MTP" \
  --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: MLX 4-bit with native MTP

A 4-bit MLX checkpoint of Qwen/Qwen3.6-35B-A3B that keeps the model's native multi-token prediction (MTP) layer, so an engine that drafts with it gets everything from one repo.

What's in it

The four model-*.safetensors shards and every config and tokenizer file are byte-identical to mlx-community/Qwen3.6-35B-A3B-4bit at revision 38740b8: affine 4-bit, group size 64, routers at 8 bits.
mtp-4bit.safetensors adds the MTP layer that MLX conversions drop. It holds the official mtp.* weights from Qwen/Qwen3.6-35B-A3B at revision 995ad96, quantized the same way: affine 4-bit, group size 64, the router and shared-expert gate at 8 bits, norms in BF16. The tensors are named language_model.mtp.*.
model.safetensors.index.json doesn't list the MTP file, so loaders that don't draft ignore it and load exactly the mlx-community model.

Use

With mlx-vlm, the same as the mlx-community conversion:

pip install -U mlx-vlm
python -m mlx_vlm.generate --model TensorFold/Qwen3.6-35B-A3B-MLX-4bit-MTP --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>

TensorFold drafts with the MTP layer and verifies every drafted token against the model, so its output equals the model's own decoding. Support for this model there is in development.

License

Apache-2.0, from Qwen/Qwen3.6-35B-A3B. The model is by the Qwen team; the MLX conversion of the main weights is by mlx-community.

Downloads last month: 300

Safetensors

Model size

35B params

Tensor type

U32

BF16

MLX

Hardware compatibility

4-bit

Model tree for TensorFold/Qwen3.6-35B-A3B-MLX-4bit-MTP

Base model

Qwen/Qwen3.6-35B-A3B

Quantized

(858)

this model