Instructions to use zecanard/Qwopus3.6-27B-v2-MLX-5bit-int5-affine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zecanard/Qwopus3.6-27B-v2-MLX-5bit-int5-affine 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-5bit-int5-affine")
config = load_config("zecanard/Qwopus3.6-27B-v2-MLX-5bit-int5-affine")

# 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-5bit-int5-affine 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-5bit-int5-affine")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("zecanard/Qwopus3.6-27B-v2-MLX-5bit-int5-affine")
model = AutoModelForMultimodalLM.from_pretrained("zecanard/Qwopus3.6-27B-v2-MLX-5bit-int5-affine")
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 Settings
LM Studio

vLLM

How to use zecanard/Qwopus3.6-27B-v2-MLX-5bit-int5-affine 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-5bit-int5-affine"
# 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-5bit-int5-affine",
		"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-5bit-int5-affine

SGLang

How to use zecanard/Qwopus3.6-27B-v2-MLX-5bit-int5-affine 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-5bit-int5-affine" \
    --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-5bit-int5-affine",
		"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-5bit-int5-affine" \
        --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-5bit-int5-affine",
		"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

How to use zecanard/Qwopus3.6-27B-v2-MLX-5bit-int5-affine 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-5bit-int5-affine 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-5bit-int5-affine 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-5bit-int5-affine 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-5bit-int5-affine",
    max_seq_length=2048,
)

How to use zecanard/Qwopus3.6-27B-v2-MLX-5bit-int5-affine 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-5bit-int5-affine"

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-5bit-int5-affine"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use zecanard/Qwopus3.6-27B-v2-MLX-5bit-int5-affine 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-5bit-int5-affine"

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-5bit-int5-affine

Run Hermes

hermes

Docker Model Runner
How to use zecanard/Qwopus3.6-27B-v2-MLX-5bit-int5-affine with Docker Model Runner:
```
docker model run hf.co/zecanard/Qwopus3.6-27B-v2-MLX-5bit-int5-affine
```

Qwopus3.6-27B-v2-MLX-5bit-int5-affine

File size: 4,683 Bytes

6b8e544

{
    "architectures": [
        "Qwen3_5ForConditionalGeneration"
    ],
    "image_token_id": 248056,
    "language_model_only": false,
    "model_name": "Unsloth/Qwen3.6-27B",
    "model_type": "qwen3_5",
    "mtp_num_hidden_layers": 1,
    "pad_token_id": 248055,
    "quantization": {
        "group_size": 32,
        "bits": 5,
        "mode": "affine"
    },
    "quantization_config": {
        "group_size": 32,
        "bits": 5,
        "mode": "affine"
    },
    "text_config": {
        "attention_bias": false,
        "attention_dropout": 0.0,
        "attn_output_gate": true,
        "bos_token_id": 248044,
        "torch_dtype": "bfloat16",
        "eos_token_id": 248044,
        "full_attention_interval": 4,
        "head_dim": 256,
        "hidden_act": "silu",
        "hidden_size": 5120,
        "initializer_range": 0.02,
        "intermediate_size": 17408,
        "layer_types": [
            "linear_attention",
            "linear_attention",
            "linear_attention",
            "full_attention",
            "linear_attention",
            "linear_attention",
            "linear_attention",
            "full_attention",
            "linear_attention",
            "linear_attention",
            "linear_attention",
            "full_attention",
            "linear_attention",
            "linear_attention",
            "linear_attention",
            "full_attention",
            "linear_attention",
            "linear_attention",
            "linear_attention",
            "full_attention",
            "linear_attention",
            "linear_attention",
            "linear_attention",
            "full_attention",
            "linear_attention",
            "linear_attention",
            "linear_attention",
            "full_attention",
            "linear_attention",
            "linear_attention",
            "linear_attention",
            "full_attention",
            "linear_attention",
            "linear_attention",
            "linear_attention",
            "full_attention",
            "linear_attention",
            "linear_attention",
            "linear_attention",
            "full_attention",
            "linear_attention",
            "linear_attention",
            "linear_attention",
            "full_attention",
            "linear_attention",
            "linear_attention",
            "linear_attention",
            "full_attention",
            "linear_attention",
            "linear_attention",
            "linear_attention",
            "full_attention",
            "linear_attention",
            "linear_attention",
            "linear_attention",
            "full_attention",
            "linear_attention",
            "linear_attention",
            "linear_attention",
            "full_attention",
            "linear_attention",
            "linear_attention",
            "linear_attention",
            "full_attention"
        ],
        "linear_conv_kernel_dim": 4,
        "linear_key_head_dim": 128,
        "linear_num_key_heads": 16,
        "linear_num_value_heads": 48,
        "linear_value_head_dim": 128,
        "mamba_ssm_dtype": "float32",
        "max_position_embeddings": 262144,
        "model_type": "qwen3_5_text",
        "mtp_num_hidden_layers": 1,
        "mtp_use_dedicated_embeddings": false,
        "num_attention_heads": 24,
        "num_hidden_layers": 64,
        "num_key_value_heads": 4,
        "output_gate_type": "swish",
        "pad_token_id": null,
        "partial_rotary_factor": 0.25,
        "rms_norm_eps": 1e-06,
        "rope_parameters": {
            "mrope_interleaved": true,
            "mrope_section": [
                11,
                11,
                10
            ],
            "partial_rotary_factor": 0.25,
            "rope_theta": 10000000,
            "rope_type": "default"
        },
        "tie_word_embeddings": false,
        "use_cache": true,
        "vocab_size": 248320
    },
    "tie_word_embeddings": false,
    "unsloth_fixed_mtp": true,
    "unsloth_version": "2026.5.2",
    "video_token_id": 248057,
    "vision_config": {
        "deepstack_visual_indexes": [],
        "depth": 27,
        "torch_dtype": "bfloat16",
        "hidden_act": "gelu_pytorch_tanh",
        "hidden_size": 1152,
        "in_channels": 3,
        "initializer_range": 0.02,
        "intermediate_size": 4304,
        "model_type": "qwen3_5",
        "num_heads": 16,
        "num_position_embeddings": 2304,
        "out_hidden_size": 5120,
        "patch_size": 16,
        "spatial_merge_size": 2,
        "temporal_patch_size": 2
    },
    "vision_end_token_id": 248054,
    "vision_start_token_id": 248053
}