Instructions to use zecanard/Qwopus3.6-35B-A3B-v1-MLX-2bit-mixed_2_6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zecanard/Qwopus3.6-35B-A3B-v1-MLX-2bit-mixed_2_6 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("zecanard/Qwopus3.6-35B-A3B-v1-MLX-2bit-mixed_2_6")
config = load_config("zecanard/Qwopus3.6-35B-A3B-v1-MLX-2bit-mixed_2_6")

# 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)

Transformers

How to use zecanard/Qwopus3.6-35B-A3B-v1-MLX-2bit-mixed_2_6 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="zecanard/Qwopus3.6-35B-A3B-v1-MLX-2bit-mixed_2_6")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("zecanard/Qwopus3.6-35B-A3B-v1-MLX-2bit-mixed_2_6")
model = AutoModelForMultimodalLM.from_pretrained("zecanard/Qwopus3.6-35B-A3B-v1-MLX-2bit-mixed_2_6", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

vLLM

How to use zecanard/Qwopus3.6-35B-A3B-v1-MLX-2bit-mixed_2_6 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zecanard/Qwopus3.6-35B-A3B-v1-MLX-2bit-mixed_2_6"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zecanard/Qwopus3.6-35B-A3B-v1-MLX-2bit-mixed_2_6",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/zecanard/Qwopus3.6-35B-A3B-v1-MLX-2bit-mixed_2_6

SGLang

How to use zecanard/Qwopus3.6-35B-A3B-v1-MLX-2bit-mixed_2_6 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "zecanard/Qwopus3.6-35B-A3B-v1-MLX-2bit-mixed_2_6" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zecanard/Qwopus3.6-35B-A3B-v1-MLX-2bit-mixed_2_6",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "zecanard/Qwopus3.6-35B-A3B-v1-MLX-2bit-mixed_2_6" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zecanard/Qwopus3.6-35B-A3B-v1-MLX-2bit-mixed_2_6",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Unsloth Desktop

How to use zecanard/Qwopus3.6-35B-A3B-v1-MLX-2bit-mixed_2_6 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "zecanard/Qwopus3.6-35B-A3B-v1-MLX-2bit-mixed_2_6"

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": "zecanard/Qwopus3.6-35B-A3B-v1-MLX-2bit-mixed_2_6"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use zecanard/Qwopus3.6-35B-A3B-v1-MLX-2bit-mixed_2_6 with Docker Model Runner:
```
docker model run hf.co/zecanard/Qwopus3.6-35B-A3B-v1-MLX-2bit-mixed_2_6
```

Hermes Agent

How to use zecanard/Qwopus3.6-35B-A3B-v1-MLX-2bit-mixed_2_6 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 "zecanard/Qwopus3.6-35B-A3B-v1-MLX-2bit-mixed_2_6"

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 zecanard/Qwopus3.6-35B-A3B-v1-MLX-2bit-mixed_2_6

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use zecanard/Qwopus3.6-35B-A3B-v1-MLX-2bit-mixed_2_6 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "zecanard/Qwopus3.6-35B-A3B-v1-MLX-2bit-mixed_2_6"

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 "zecanard/Qwopus3.6-35B-A3B-v1-MLX-2bit-mixed_2_6" \
  --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"

Qwopus3.6-35B-A3B-v1-MLX-2bit-mixed_2_6 / README.md

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metadata

base_model: Jackrong/Qwopus3.6-35B-A3B-v1
library_name: mlx
tags:
  - mlx
  - 2-bit
  - text-generation-inference
  - transformers
  - unsloth
  - qwen3_6
  - moe
  - reasoning
  - chain-of-thought
  - lora
  - sft
  - multimodal
  - vision
  - tool-use
  - function-calling
  - long-context
license: apache-2.0
language:
  - en
  - zh
  - es
  - ru
  - ja
pipeline_tag: image-text-to-text

🦆 zecanard/Qwopus3.6-35B-A3B-v1-MLX-2bit-mixed_2_6

This model was converted to MLX from Jackrong/Qwopus3.6-35B-A3B-v1 using mlx-vlm version 0.6.3. Please refer to the original model card for more details.

🌟 Quality

Mixed-precision quantized vision language model with an effective 3.403 bits per weight. Combines the size and speed benefits of a 2-bit quant with higher precision where it matters most.

mlx_vlm.convert --quantize --q-group-size 32 --quant-predicate mixed_2_6

🛠️ Customizations

This quant is aware of the current date, and also enables thinking (if available). You may disable this behavior by deleting the following line from the chat template, or changing true to false:

{%- set enable_thinking = true %}

A fix is also included for a thinking-related performance issue in Qwen 3.6.

🖥️ Use with `mlx`

pip install -U mlx-vlm

mlx_vlm.generate --model zecanard/Qwopus3.6-35B-A3B-v1-MLX-2bit-mixed_2_6 --max-tokens 100 --temperature 0 --prompt "Describe this image." --image <path_to_image>

🦆 zecanard/Qwopus3.6-35B-A3B-v1-MLX-2bit-mixed_2_6

🌟 Quality

🛠️ Customizations

🖥️ Use with mlx

🖥️ Use with `mlx`