Instructions to use XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1 with Transformers:

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

pipe = pipeline("text-generation", model="XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1")
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("XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1")
model = AutoModelForMultimodalLM.from_pretrained("XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1

SGLang

How to use XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1 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 "XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1" \
    --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": "XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1" \
        --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": "XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1 with Docker Model Runner:
```
docker model run hf.co/XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1
```

Qwopus3.6-27B-v2-GPTQ-Pro-v1

File size: 1,737 Bytes

de5bd59

{
  "bits": 4,
  "dynamic": {
    "-:.*visual.*": {},
    "-:.*vision.*": {},
    "-:.*mtp.*": {},
    "-:.*lm_head.*": {},
    "-:.*embed_tokens.*": {},
    "-:.*norm.*": {}
  },
  "group_size": 128,
  "desc_act": false,
  "lm_head": false,
  "method": "gptq",
  "quant_method": "gptq",
  "format": "gptq",
  "checkpoint_format": "gptq",
  "pack_dtype": "int32",
  "meta": {
    "act_group_aware": true,
    "activation_weighted_mse": true,
    "damp_auto_increment": 0.01,
    "damp_percent": 0.05,
    "fallback": {
      "strategy": "rtn",
      "threshold": "0.5%",
      "smooth": {
        "type": "mse",
        "group_size_threshold": 128,
        "steps": 32,
        "maxshrink": 0.9
      }
    },
    "foem": {
      "alpha": 0.25,
      "beta": 0.2,
      "device": "auto"
    },
    "mse": 2.0,
    "pack_impl": "cpu",
    "reference_recipe": "groxaxo/Qwen3.6-27B-GPTQ-Pro-4bit",
    "source_model": "Jackrong/Qwopus3.6-27B-v2",
    "calibration_dataset": "wikitext",
    "nsamples": 256,
    "seqlen": 2048,
    "offload_to_disk": true,
    "offload_to_disk_path": "./gptqmodel_offload/ixgdjlqx-esuvlqbb/",
    "quantizer": [
      "gptqmodel:6.1.0-dev"
    ],
    "uri": "https://github.com/modelcloud/gptqmodel",
    "static_groups": false,
    "true_sequential": true,
    "gptaq": null,
    "gc_mode": "interval",
    "wait_for_submodule_finalizers": false,
    "auto_forward_data_parallel": true,
    "dense_vram_strategy": "exclusive",
    "dense_vram_strategy_devices": null,
    "moe_vram_strategy": "exclusive",
    "moe_vram_strategy_devices": null,
    "mock_quantization": false,
    "hessian": {
      "chunk_size": null,
      "chunk_bytes": null,
      "staging_dtype": "float32"
    }
  },
  "sym": true
}