Instructions to use genevera/XORTRON.CriminalComputing.2026.27B.Instruct-AWQ-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use genevera/XORTRON.CriminalComputing.2026.27B.Instruct-AWQ-4bit with Transformers:

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

pipe = pipeline("image-text-to-text", model="genevera/XORTRON.CriminalComputing.2026.27B.Instruct-AWQ-4bit")
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("genevera/XORTRON.CriminalComputing.2026.27B.Instruct-AWQ-4bit")
model = AutoModelForMultimodalLM.from_pretrained("genevera/XORTRON.CriminalComputing.2026.27B.Instruct-AWQ-4bit", 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

vLLM

How to use genevera/XORTRON.CriminalComputing.2026.27B.Instruct-AWQ-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "genevera/XORTRON.CriminalComputing.2026.27B.Instruct-AWQ-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "genevera/XORTRON.CriminalComputing.2026.27B.Instruct-AWQ-4bit",
		"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/genevera/XORTRON.CriminalComputing.2026.27B.Instruct-AWQ-4bit

SGLang

How to use genevera/XORTRON.CriminalComputing.2026.27B.Instruct-AWQ-4bit 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 "genevera/XORTRON.CriminalComputing.2026.27B.Instruct-AWQ-4bit" \
    --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": "genevera/XORTRON.CriminalComputing.2026.27B.Instruct-AWQ-4bit",
		"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 "genevera/XORTRON.CriminalComputing.2026.27B.Instruct-AWQ-4bit" \
        --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": "genevera/XORTRON.CriminalComputing.2026.27B.Instruct-AWQ-4bit",
		"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 genevera/XORTRON.CriminalComputing.2026.27B.Instruct-AWQ-4bit with Docker Model Runner:
```
docker model run hf.co/genevera/XORTRON.CriminalComputing.2026.27B.Instruct-AWQ-4bit
```

XORTRON CriminalComputing 2026 27B Instruct AWQ 4-bit

AWQ-quantized export of darkc0de/XORTRON.CriminalComputing.2026.27B.Instruct prepared for vLLM serving.

Quantization Summary

Quantization method: awq (AutoRound provider)
Weight precision: 4-bit (bits=4, group_size=128, sym=true, zero_point=false)
Quantized block scope: model.language_model.layers
Calibration config: nsamples=64, iters=0, batch_size=1
Format variant: gemm

Important Runtime Notes

For vLLM AWQ in this build, use --dtype float16.
--dtype auto may resolve to bfloat16 from model config and fail validation.

vLLM Example

vllm serve /path/to/XORTRON.CriminalComputing.2026.27B.Instruct-AWQ-4bit \
  --quantization awq \
  --dtype float16 \
  --trust-remote-code

Files

config.json
generation_config.json
quantization_config.json
model.safetensors.index.json
model-00001-of-00010.safetensors ... model-00010-of-00010.safetensors
tokenizer.json
tokenizer_config.json
processor_config.json
chat_template.jinja

Base Model Attribution

All rights and usage constraints for the base model remain with the original model publisher: darkc0de/XORTRON.CriminalComputing.2026.27B.Instruct.

Downloads last month: 13

Safetensors

Model size

7B params

Tensor type

I32

BF16

Model tree for genevera/XORTRON.CriminalComputing.2026.27B.Instruct-AWQ-4bit

Base model

Qwen/Qwen3.5-27B

Finetuned

darkc0de/XORTRON.CriminalComputing.2026.27B.Instruct

Quantized

(3)

this model