Instructions to use mlx-community/XORTRON.CriminalComputing.2026.27B.Instruct.NEXT-4.5bit-msq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/XORTRON.CriminalComputing.2026.27B.Instruct.NEXT-4.5bit-msq with Transformers:

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

pipe = pipeline("image-text-to-text", model="mlx-community/XORTRON.CriminalComputing.2026.27B.Instruct.NEXT-4.5bit-msq")
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("mlx-community/XORTRON.CriminalComputing.2026.27B.Instruct.NEXT-4.5bit-msq")
model = AutoModelForImageTextToText.from_pretrained("mlx-community/XORTRON.CriminalComputing.2026.27B.Instruct.NEXT-4.5bit-msq")
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

vLLM

How to use mlx-community/XORTRON.CriminalComputing.2026.27B.Instruct.NEXT-4.5bit-msq with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mlx-community/XORTRON.CriminalComputing.2026.27B.Instruct.NEXT-4.5bit-msq"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlx-community/XORTRON.CriminalComputing.2026.27B.Instruct.NEXT-4.5bit-msq",
		"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/mlx-community/XORTRON.CriminalComputing.2026.27B.Instruct.NEXT-4.5bit-msq

SGLang

How to use mlx-community/XORTRON.CriminalComputing.2026.27B.Instruct.NEXT-4.5bit-msq 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 "mlx-community/XORTRON.CriminalComputing.2026.27B.Instruct.NEXT-4.5bit-msq" \
    --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": "mlx-community/XORTRON.CriminalComputing.2026.27B.Instruct.NEXT-4.5bit-msq",
		"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 "mlx-community/XORTRON.CriminalComputing.2026.27B.Instruct.NEXT-4.5bit-msq" \
        --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": "mlx-community/XORTRON.CriminalComputing.2026.27B.Instruct.NEXT-4.5bit-msq",
		"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"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use mlx-community/XORTRON.CriminalComputing.2026.27B.Instruct.NEXT-4.5bit-msq with Docker Model Runner:
```
docker model run hf.co/mlx-community/XORTRON.CriminalComputing.2026.27B.Instruct.NEXT-4.5bit-msq
```

XORTRON.CriminalComputing.2026.27B.Instruct.NEXT — MLX 4.5 BPW

Mixed-precision MLX quantization of darkc0de/XORTRON.CriminalComputing.2026.27B.Instruct.NEXT, quantized with MLX Smart Quantize (MSQ) — my own sensitivity-based mixed-precision quantization method for Apple Silicon. It measures per-layer NMSE and assigns optimal bit widths automatically, combining architecture knowledge with measured data.

Details

Type: Vision (VLM)
Average: 4.5 bits per weight
Method: MLX Smart Quantize (MSQ)

Downloads last month: 3

Safetensors

Model size

27B params

Tensor type

BF16

U32

Model tree for mlx-community/XORTRON.CriminalComputing.2026.27B.Instruct.NEXT-4.5bit-msq

Base model

Qwen/Qwen3.5-27B

Quantized

(195)

this model