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
```

Xavier Rey-Robert commited on Jun 16

Commit

2c59376

1 Parent(s): 82f2c89

Fix Qwen Terminal-Bench 2.0 comparison

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -359,8 +359,8 @@ Scope note: single-pass unrestricted generation exposed one pathological runaway
 </thead>
 <tbody>
 <tr style="background: rgba(16, 185, 129, 0.06);"><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 700; color: #047857;">This GPTQ-Pro ns256 artifact + Terminus-2</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); text-align: right; font-weight: 800; color: #047857;">44.94%</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">40 / 89, recovery-corrected local operational run</td></tr>
-<tr><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 600;">Qwen/Qwen3.6-27B</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); text-align: right;">N/A</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">No public official Terminal-Bench 2.0 leaderboard entry found for the 27B base model as of 2026-06-16.</td></tr>
-<tr><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 600;">little-coder + Qwen3.6-35B-A3B</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); text-align: right; font-weight: 700;">24.6% +/- 3.2</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">Closest public Qwen3.6 comparison on the official Terminal-Bench 2.0 leaderboard; not the 27B model.</td></tr>
 </tbody>
 </table>
 </div>

 </thead>
 <tbody>
 <tr style="background: rgba(16, 185, 129, 0.06);"><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 700; color: #047857;">This GPTQ-Pro ns256 artifact + Terminus-2</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); text-align: right; font-weight: 800; color: #047857;">44.94%</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">40 / 89, recovery-corrected local operational run</td></tr>
+<tr><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 600;">Qwen/Qwen3.6-27B</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); text-align: right; font-weight: 700;">59.3%</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">Published on the <a href="https://huggingface.co/Qwen/Qwen3.6-27B">Qwen model card</a>; Qwen protocol uses Harbor/Terminus-2, 3h timeout, 32 CPU/48 GB RAM, max_tokens 80K, 256K context, average of 5 runs.</td></tr>
+<tr><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 600;">little-coder + Qwen3.6-35B-A3B</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); text-align: right; font-weight: 700;">24.6% +/- 3.2</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">Official Terminal-Bench 2.0 leaderboard Qwen-family entry; different agent and different model.</td></tr>
 </tbody>
 </table>
 </div>