Instructions to use XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-MTP-BF16 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-MTP-BF16 with Transformers:

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

pipe = pipeline("image-text-to-text", model="XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-MTP-BF16")
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("XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-MTP-BF16")
model = AutoModelForMultimodalLM.from_pretrained("XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-MTP-BF16", 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 XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-MTP-BF16 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-MTP-BF16"
# 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-MTP-BF16",
		"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/XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-MTP-BF16

SGLang

How to use XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-MTP-BF16 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-MTP-BF16" \
    --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-MTP-BF16",
		"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 "XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-MTP-BF16" \
        --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-MTP-BF16",
		"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 XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-MTP-BF16 with Docker Model Runner:
```
docker model run hf.co/XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-MTP-BF16
```

Qwopus3.6-27B-v2-GPTQ-Pro-MTP-BF16

Experimental MTP BF16 patch for Jackrong/Qwopus3.6-27B-v2.

This artifact starts from XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1 and restores the original source mtp.* tensors from Jackrong/Qwopus3.6-27B-v2. The main language model remains the GPTQ-Pro parent artifact. The MTP tensors in this repository are BF16 source tensors, not GPTQ-quantized tensors.

What changed

Parent GPTQ-Pro artifact: XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1
Source for restored MTP tensors: Jackrong/Qwopus3.6-27B-v2
Restored mtp.* tensors: 15
Restored MTP dtype counts: {"bfloat16": 15}
Local tensor shard size after patch: 18.22 GiB total safetensors

Practical note

This is meant for loader and vLLM speculative-decoding experiments. Previous testing on 1x RTX 3090 showed that restoring MTP made draft acceptance work, but did not improve throughput versus the non-MTP GPTQ-Pro baseline. The likely bottleneck was vLLM/GPTQ-Marlin speculative path overhead rather than MTP tensor precision.

For practical long-context 1x RTX 3090 serving, the non-MTP baseline remains the recommended artifact:

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

Validation status

Structural checks performed during patch creation:

model.safetensors.index.json contains restored mtp.* keys.
All indexed shard files exist locally.
The added MTP shard is a standalone safetensors file.
config.json records mtp_num_hidden_layers >= 1.

Runtime serving validation is still required before treating this as a working MTP deployment artifact.

References

Source model: Jackrong/Qwopus3.6-27B-v2
Parent GPTQ-Pro artifact: XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1

Downloads last month: 10

Safetensors

Model size

28B params

Tensor type

BF16

I32

Model tree for XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-MTP-BF16

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Collection including XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-MTP-BF16

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4 items • Updated Sep 2