Instructions to use bottlecapai/ThinkingCap-Qwen3.6-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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", 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]:])) - 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
Exceptional Model
Just wanted to congratulate on this incredible finetune.
I have tested a lot of models that can work on a 16+8 gb vram setup that I have, nothing comes close to the base models EXCEPT this. It surpassed the base model in my opinion. This does what it says caps the thinking and thus produces better results.
Same here, best model i ever used local.
It works so much better for me than stock qwen 27b, mostly in stopping looping. I hardly ever now get thinking loops, which is my main critique of stock qwen3.6. Also, appears that it doesn't compromise on quality, as it looks about on the same level of capability.
It works so much better for me than stock qwen 27b, mostly in stopping looping. I hardly ever now get thinking loops, which is my main critique of stock qwen3.6. Also, appears that it doesn't compromise on quality, as it looks about on the same level of capability.
too bad i found it so late , 3.8 will be out in a week or so.
It works so much better for me than stock qwen 27b, mostly in stopping looping. I hardly ever now get thinking loops, which is my main critique of stock qwen3.6. Also, appears that it doesn't compromise on quality, as it looks about on the same level of capability.
too bad i found it so late , 3.8 will be out in a week or so.
3.8 will need its own "ThinkingCap", yes? π
3.8 will need its own "ThinkingCap", yes? π
This reminds me of The Wandering Inn. If you don't know what I am talking about, then just forget that I said anything.