Instructions to use zecanard/Qwopus3.6-27B-v2-MLX-2bit-mixed_2_6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zecanard/Qwopus3.6-27B-v2-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/Qwopus3.6-27B-v2-MLX-2bit-mixed_2_6")
config = load_config("zecanard/Qwopus3.6-27B-v2-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/Qwopus3.6-27B-v2-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/Qwopus3.6-27B-v2-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)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("zecanard/Qwopus3.6-27B-v2-MLX-2bit-mixed_2_6")
model = AutoModelForImageTextToText.from_pretrained("zecanard/Qwopus3.6-27B-v2-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?"}
        ]
    },
]
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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps
LM Studio

vLLM

How to use zecanard/Qwopus3.6-27B-v2-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/Qwopus3.6-27B-v2-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/Qwopus3.6-27B-v2-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/Qwopus3.6-27B-v2-MLX-2bit-mixed_2_6

SGLang

How to use zecanard/Qwopus3.6-27B-v2-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/Qwopus3.6-27B-v2-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/Qwopus3.6-27B-v2-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/Qwopus3.6-27B-v2-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/Qwopus3.6-27B-v2-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 Studio new

How to use zecanard/Qwopus3.6-27B-v2-MLX-2bit-mixed_2_6 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for zecanard/Qwopus3.6-27B-v2-MLX-2bit-mixed_2_6 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for zecanard/Qwopus3.6-27B-v2-MLX-2bit-mixed_2_6 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for zecanard/Qwopus3.6-27B-v2-MLX-2bit-mixed_2_6 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="zecanard/Qwopus3.6-27B-v2-MLX-2bit-mixed_2_6",
    max_seq_length=2048,
)

Pi new

How to use zecanard/Qwopus3.6-27B-v2-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/Qwopus3.6-27B-v2-MLX-2bit-mixed_2_6"

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": "zecanard/Qwopus3.6-27B-v2-MLX-2bit-mixed_2_6"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use zecanard/Qwopus3.6-27B-v2-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/Qwopus3.6-27B-v2-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/Qwopus3.6-27B-v2-MLX-2bit-mixed_2_6

Run Hermes

hermes

Docker Model Runner
How to use zecanard/Qwopus3.6-27B-v2-MLX-2bit-mixed_2_6 with Docker Model Runner:
```
docker model run hf.co/zecanard/Qwopus3.6-27B-v2-MLX-2bit-mixed_2_6
```

🦆 zecanard/Qwopus3.6-27B-v2-MLX-2bit-mixed_2_6

This model was converted to MLX from Jackrong/Qwopus3.6-27B-v2 using mlx-vlm version 0.5.0. Please refer to the original model card for more details.

🌟 Quality

Mixed-precision quantized vision language model with an effective 3.524 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/Qwopus3.6-27B-v2-MLX-2bit-mixed_2_6 --max-tokens 100 --temperature 0 --prompt "Describe this image." --image <path_to_image>

Downloads last month: -

Safetensors

Model size

4B params

Tensor type

BF16

U32

MLX

Hardware compatibility

4-bit

Model tree for zecanard/Qwopus3.6-27B-v2-MLX-2bit-mixed_2_6

Base model

Jackrong/Qwopus3.6-27B-v2

Adapter

(9)

this model

zecanard
/

Qwopus3.6-27B-v2-MLX-2bit-mixed_2_6

🦆 zecanard/Qwopus3.6-27B-v2-MLX-2bit-mixed_2_6

🌟 Quality

🛠️ Customizations

🖥️ Use with `mlx`

Model tree for zecanard/Qwopus3.6-27B-v2-MLX-2bit-mixed_2_6

Datasets used to train zecanard/Qwopus3.6-27B-v2-MLX-2bit-mixed_2_6

🦆 zecanard/Qwopus3.6-27B-v2-MLX-2bit-mixed_2_6

🌟 Quality

🛠️ Customizations

🖥️ Use with mlx

Model tree for zecanard/Qwopus3.6-27B-v2-MLX-2bit-mixed_2_6

Datasets used to train zecanard/Qwopus3.6-27B-v2-MLX-2bit-mixed_2_6

🖥️ Use with `mlx`