Instructions to use bottlecapai/ThinkingCap-Qwen3.6-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bottlecapai/ThinkingCap-Qwen3.6-27B with Transformers:

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

pipe = pipeline("image-text-to-text", model="bottlecapai/ThinkingCap-Qwen3.6-27B")
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("bottlecapai/ThinkingCap-Qwen3.6-27B")
model = AutoModelForMultimodalLM.from_pretrained("bottlecapai/ThinkingCap-Qwen3.6-27B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bottlecapai/ThinkingCap-Qwen3.6-27B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bottlecapai/ThinkingCap-Qwen3.6-27B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bottlecapai/ThinkingCap-Qwen3.6-27B",
		"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/bottlecapai/ThinkingCap-Qwen3.6-27B

SGLang

How to use bottlecapai/ThinkingCap-Qwen3.6-27B 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 "bottlecapai/ThinkingCap-Qwen3.6-27B" \
    --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": "bottlecapai/ThinkingCap-Qwen3.6-27B",
		"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 "bottlecapai/ThinkingCap-Qwen3.6-27B" \
        --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": "bottlecapai/ThinkingCap-Qwen3.6-27B",
		"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 bottlecapai/ThinkingCap-Qwen3.6-27B with Docker Model Runner:
```
docker model run hf.co/bottlecapai/ThinkingCap-Qwen3.6-27B
```

some benchmarks

#13

by owao - opened 4 days ago

Discussion

owao

4 days ago

Qwen3.6-27B-MoQ-4.5bpw vs ThinkingCap-Qwen3.6-27B-Q4_K_M

I like this test I found somewhere in the past on reddit, that's testing multiple abilities at the same time.
The quants are not absolute apple to apple but that's all I can run.

PROMPT = "Reverse this string: '.DefaultCellStyle'"
GROUND_TRUTH = "elytSlleCtluafeD."
RUNS_PER_MODEL = 50
max_tokens: 6000

==================================================
Qwen3.6-27B-MTP-MoQ-4.5_110k
==================================================

All 50 runs already completed
Results: 17/50 correct (34.0%)
Tokens: sum=133493, avg=2669.9, min=1345, max=6000

==================================================
ThinkingCap-Qwen3.6-27B-Q4_K_M_100k
==================================================

All 50 runs already completed
Results: 18/50 correct (36.0%)
Tokens: sum=69059, avg=1381.2, min=487, max=6000

Not a large number of runs, but seems in line with the promise :)
Great job!

owao

2 days ago

Oh here is the python script for who is interested I forgot to include it: https://paste.sh/HO480lkt#5PIGj6qtxijHiOk8Wj7rWZJl

klasocki

BottleCapAI org 1 day ago

Thanks so much for contributing the scores, it's amazing when the community validates the results!

armin5000

about 3 hours ago

Wow, thank you very much for your work klasocki.

==================================================

Evaluating model: ThinkingCap-Qwen3.6-27B-MTP-Q6_K-128k

==================================================

Running 50 evaluation run(s)...

Prompt: Reverse this string: '.DefaultCellStyle'

Ground truth: elytSlleCtluafeD.

Results will be saved to results\ThinkingCap-Qwen3.6-27B-MTP-Q6_K-128k.json

Run 1: [FAIL] (extracted: None)

Run 2: [FAIL] (extracted: None)

Run 3: [OK] (extracted: elytSlleCtluafeD.)

Run 4: [FAIL] (extracted: None)

Run 5: [OK] (extracted: elytSlleCtluafeD.)

Run 6: [OK] (extracted: elytSlleCtluafeD.)

Run 7: [OK] (extracted: elytSlleCtluafeD.)

Run 8: [OK] (extracted: elytSlleCtluafeD.)

Run 9: [OK] (extracted: elytSlleCtluafeD.)

Run 10: [OK] (extracted: elytSlleCtluafeD.)

Run 11: [FAIL] (extracted: None)

Run 12: [OK] (extracted: elytSlleCtluafeD.)

Run 13: [OK] (extracted: elytSlleCtluafeD.)

Run 14: [FAIL] (extracted: None)

Run 15: [OK] (extracted: elytSlleCtluafeD.)

Run 16: [OK] (extracted: elytSlleCtluafeD.)

Run 17: [FAIL] (extracted: None)

Run 18: [OK] (extracted: elytSlleCtluafeD.)

Run 19: [FAIL] (extracted: None)

Run 20: [FAIL] (extracted: None)

Run 21: [FAIL] (extracted: None)

Run 22: [FAIL] (extracted: None)

Run 23: [FAIL] (extracted: None)

Run 24: [OK] (extracted: elytSlleCtluafeD.)

Run 25: [FAIL] (extracted: None)

Run 26: [OK] (extracted: elytSlleCtluafeD.)

Run 27: [OK] (extracted: elytSlleCtluafeD.)

Run 28: [FAIL] (extracted: None)

Run 29: [FAIL] (extracted: None)

Run 30: [OK] (extracted: elytSlleCtluafeD.)

Run 31: [FAIL] (extracted: None)

Run 32: [OK] (extracted: elytSlleCtluafeD.)

Run 33: [FAIL] (extracted: None)

Run 34: [OK] (extracted: elytSlleCtluafeD.)

Run 35: [OK] (extracted: elytSlleCtluafeD.)

Run 36: [OK] (extracted: elytSlleCtluafeD.)

Run 37: [OK] (extracted: elytSlleCtluafeD.)

Run 38: [OK] (extracted: elytSlleCtluafeD.)

Run 39: [OK] (extracted: elytSlleCtluafeD.)

Run 40: [OK] (extracted: elytSlleCtluafeD.)

Run 41: [OK] (extracted: elytSlleCtluafeD.)

Run 42: [FAIL] (extracted: None)

Run 43: [OK] (extracted: elytSlleCtluafeD.)

Run 44: [OK] (extracted: elytSlleCtluafeD.)

Run 45: [OK] (extracted: elytSlleCtluafeD.)

Run 46: [FAIL] (extracted: None)

Run 47: [FAIL] (extracted: None)

Run 48: [OK] (extracted: elytSlleCtluafeD.)

Run 49: [FAIL] (extracted: None)

Run 50: [OK] (extracted: elytSlleCtluafeD.)

==================================================

Results: 30/50 correct (60.0%)

Tokens: sum=49503, avg=990.1, min=498, max=6000

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