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

Libraries

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

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

pipe = pipeline("text-generation", model="XReyRobert/Qwopus3.6-27B-Coder-GPTQ-Pro")
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-Coder-GPTQ-Pro")
model = AutoModelForMultimodalLM.from_pretrained("XReyRobert/Qwopus3.6-27B-Coder-GPTQ-Pro")
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-Coder-GPTQ-Pro 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-Coder-GPTQ-Pro"
# 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-Coder-GPTQ-Pro",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

Qwopus3.6-27B-Coder-GPTQ-Pro / quantize_config.json

XReyRobert

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	{
	"bits": 4,
	"checkpoint_format": "gptq",
	"desc_act": false,
	"dynamic": {
	"-:.embed_tokens.": {},
	"-:.lm_head.": {},
	"-:.norm.": {},
	"-:.vision.": {},
	"-:.visual.": {}
	},
	"format": "gptq",
	"group_size": 128,
	"lm_head": false,
	"meta": {
	"act_group_aware": true,
	"activation_weighted_mse": true,
	"auto_forward_data_parallel": true,
	"calibration_dataset": "wikitext",
	"damp_auto_increment": 0.01,
	"damp_percent": 0.05,
	"dense_vram_strategy": "exclusive",
	"dense_vram_strategy_devices": null,
	"fallback": {
	"smooth": {
	"group_size_threshold": 128,
	"maxshrink": 0.9,
	"steps": 32,
	"type": "mse"
	},
	"strategy": "rtn",
	"threshold": "0.5%"
	},
	"foem": {
	"alpha": 0.25,
	"beta": 0.2,
	"device": "auto"
	},
	"gc_mode": "interval",
	"gptaq": null,
	"hessian": {
	"chunk_bytes": null,
	"chunk_size": null,
	"staging_dtype": "float32"
	},
	"mock_quantization": false,
	"moe_vram_strategy": "exclusive",
	"moe_vram_strategy_devices": null,
	"mse": 2.0,
	"mtp_patch": {
	"bits": 4,
	"foem_note": "Main model remains the base FOEM ns256 artifact; MTP tensors are quantized by direct MTP-aware GPTQ-Pro core capture.",
	"group_size": 128,
	"nsamples": 256,
	"quantized_key_count": 32,
	"quantized_modules": [
	"mtp.fc",
	"mtp.layers.0.self_attn.q_proj",
	"mtp.layers.0.self_attn.k_proj",
	"mtp.layers.0.self_attn.v_proj",
	"mtp.layers.0.self_attn.o_proj",
	"mtp.layers.0.mlp.gate_proj",
	"mtp.layers.0.mlp.up_proj",
	"mtp.layers.0.mlp.down_proj"
	],
	"seqlen": 2048,
	"source_model": "Jackrong/Qwopus3.6-27B-Coder",
	"type": "mtp-aware-gptq-pro-core"
	},
	"nsamples": 256,
	"offload_to_disk": true,
	"offload_to_disk_path": "/workspace/disk_offload_qwopus36_27b_coder",
	"pack_impl": "cpu",
	"quantizer": [
	"gptqmodel:6.1.0-dev"
	],
	"reference_recipe": "groxaxo/Qwen3.6-27B-GPTQ-Pro-4bit",
	"seqlen": 2048,
	"source_model": "Jackrong/Qwopus3.6-27B-Coder",
	"static_groups": false,
	"true_sequential": true,
	"uri": "https://github.com/modelcloud/gptqmodel",
	"wait_for_submodule_finalizers": false
	},
	"method": "gptq",
	"pack_dtype": "int32",
	"quant_method": "gptq",
	"sym": true
	}