Instructions to use spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit-vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit-vision 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("spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit-vision")
config = load_config("spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit-vision")

# 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 spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit-vision with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit-vision"

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": "spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit-vision"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit-vision 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 "spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit-vision"

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 spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit-vision

Run Hermes

hermes

OpenClaw new

How to use spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit-vision with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit-vision"

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 "spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit-vision" \
  --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 optimized for MLX.

4-bit baseline with important layers at 8-bit and BF16.
This quant supports image input and requires a vision-capable server.

Also comes in non-image versions: quality+, speed+.

Usage

# Start server at http://localhost:8080/v1/chat/completions
uvx --from mlx-vlm --with torchvision \
  mlx_vlm.server \
  --host 127.0.0.1 \
  --port 8080 \
  --model spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit-vision

Benchmarks

metric	mlx-community/ Qwen3.6-35B-A3B-4bit	mlx-community/ Qwen3.6-35B-A3B-4.4bit-msq	4.8 bit (this model)	5.4 bit
bpw	4.503	4.787	4.788	5.438
peak memory (1024/512)	20.683	21.922	21.928	24.741
prompt tok/s (1024)	2719.4470 ± 15.2250	2695.9370 ± 12.5260	2734.5260 ± 3.8810	2665.3060 ± 11.4520
gen tok/s (512)	108.4990 ± 0.4910	94.2940 ± 0.3650	97.2820 ± 0.0800	89.4920 ± 0.2610
kl divergence	0.0838 ± 0.0008	0.1689 ± 0.0015	0.0244 ± 0.0004	0.0189 ± 0.0003
perplexity	4.6150 ± 0.0320	4.2490 ± 0.0280	4.6410 ± 0.0320	4.6440 ± 0.0320
hellaswag	0.5560 ± 0.0220	0.5780 ± 0.0220	0.5440 ± 0.0220	0.5370 ± 0.0110
piqa	0.7940 ± 0.0180	0.7920 ± 0.0180	0.7920 ± 0.0180	0.7980 ± 0.0180
winogrande	0.7260 ± 0.0200	0.7400 ± 0.0200	0.7120 ± 0.0200	0.7100 ± 0.0200

I've moved over to using speed + KL divergence as my primary optimization metrics. Hellaswag, PIQA, Winogrande, and perplexity are kept as sanity checks, though these require high sample sizes to get usable signal.

Tested on a Mac Studio M3 Ultra with:

mlx_lm.convert --hf-path Qwen/Qwen3.6-35B-A3B --mlx-path ./mlx && mlx_lm.kld --baseline-model ./mlx
mlx_lm.perplexity --sequence-length 512 --seed 123
mlx_lm.benchmark --prompt-tokens 1024 --generation-tokens 512 --num-trials 5
mlx_lm.evaluate --tasks hellaswag --seed 123 --num-shots 0 --limit 500
mlx_lm.evaluate --tasks piqa --seed 123 --num-shots 0 --limit 500
mlx_lm.evaluate --tasks winogrande --seed 123 --num-shots 0 --limit 500

Required PRs:

Methodology

Quantized with a mlx-lm fork, drawing inspiration from Unsloth/AesSedai/ubergarm style mixed-precision GGUFs. MLX quantization options differ than llama.cpp, but the principles are the same:

Sensitive layers like MoE routing, attention, and output embeddings get higher precision
More tolerant layers like MoE experts get lower precision

Downloads last month: 240

Safetensors

Model size

6B params

Tensor type

BF16

U32

MLX

Hardware compatibility

4-bit

Model tree for spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit-vision

Base model

Qwen/Qwen3.6-35B-A3B

Quantized

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