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
Question: Is the Qwen 3.6 chat template "broken" as reported by others
Greetings bottlecapai team,
Do you know if the reported qwen 3.6 chat template issue exists with your project? One popular fix claims their fix "fixes rendering errors, KV cache invalidation, token waste, and fatal agentic stalling in the official Qwen chat templates."
See: https://huggingface.co/froggeric/Qwen-Fixed-Chat-Templates
Any thoughts?
Hello, i tried this template, because iam still facing of premature stoping on vLLM with this model, but it gets worse.
I'm using newest vLLM 0.26.0 with
--tool-call-parser qwen3-coder
--speculative-config {\"method\":\"mtp\",\"num_speculative_tokens\":2}
--enable-chunked-prefill"
--max-num-batched-tokens 8192"
--reasoning-parser qwen3"
Hello, i tried this template, because iam still facing of premature stoping on vLLM with this model, but it gets worse.
I'm using newest vLLM 0.26.0 with--tool-call-parser qwen3-coder --speculative-config {\"method\":\"mtp\",\"num_speculative_tokens\":2} --enable-chunked-prefill" --max-num-batched-tokens 8192" --reasoning-parser qwen3"
I had similar issues. Someone mentioned that thinking should be disabled for instruct mode. I haven't tested yet but it sounds promising.
Do u mean ?
--default-chat-template-kwargs { "preserve_thinkig": true/false}
At my case it doesnt help any of bool variants, now I'm trying to tweak chat_template, if I will found something I will let u know.
Do u mean ?
--default-chat-template-kwargs { "preserve_thinkig": true/false}At my case it doesnt help any of bool variants, now I'm trying to tweak chat_template, if I will found something I will let u know.
Yes.
Thank you for checking.