Instructions to use Qwen/Qwen3.6-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qwen/Qwen3.6-27B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Qwen/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, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("Qwen/Qwen3.6-27B") model = AutoModelForImageTextToText.from_pretrained("Qwen/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
- HuggingChat
- Notebooks
- Google Colab
- Kaggle
- AMD Developer Cloud
- Local Apps Settings
- vLLM
How to use Qwen/Qwen3.6-27B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Qwen/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": "Qwen/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/Qwen/Qwen3.6-27B
- SGLang
How to use Qwen/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 "Qwen/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": "Qwen/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 "Qwen/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": "Qwen/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 Qwen/Qwen3.6-27B with Docker Model Runner:
docker model run hf.co/Qwen/Qwen3.6-27B
Add community evaluation results for AIME_2026, GPQA, HLE, HMMT_FEB_2026, MMLU-PRO, SWE-BENCH_PRO, SWE-BENCH_VERIFIED, TERMINAL-BENCH-2.0
#2
by nielsr HF Staff - opened
YAML Metadata Error:Invalid content in Eval Result file .eval_results/hle.yaml
Check out the documentation for more information.
Show details
Task ID "hle" does not match any task in dataset "cais/hle". Available: none
.eval_results/aime_2026.yaml
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id: MathArena/aime_2026
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task_id: MathArena/aime_2026
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value: 94.1
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source:
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url: https://huggingface.co/Qwen/Qwen3.6-27B
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name: Model Card
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.eval_results/gpqa.yaml
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- dataset:
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id: Idavidrein/gpqa
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task_id: diamond
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value: 87.8
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source:
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url: https://huggingface.co/Qwen/Qwen3.6-27B
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name: Model Card
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.eval_results/hle.yaml
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id: cais/hle
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task_id: hle
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value: 24
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source:
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url: https://huggingface.co/Qwen/Qwen3.6-27B
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name: Model Card
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.eval_results/hmmt_feb_2026.yaml
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id: MathArena/hmmt_feb_2026
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task_id: MathArena/hmmt_feb_2026
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value: 84.3
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source:
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url: https://huggingface.co/Qwen/Qwen3.6-27B
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name: Model Card
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.eval_results/mmlu-pro.yaml
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- dataset:
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id: TIGER-Lab/MMLU-Pro
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task_id: mmlu_pro
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value: 86.2
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source:
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url: https://huggingface.co/Qwen/Qwen3.6-27B
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name: Model Card
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.eval_results/swe-bench_pro.yaml
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id: ScaleAI/SWE-bench_Pro
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task_id: SWE_Bench_Pro
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value: 53.5
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source:
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url: https://huggingface.co/Qwen/Qwen3.6-27B
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name: Model Card
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.eval_results/swe-bench_verified.yaml
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id: SWE-bench/SWE-bench_Verified
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task_id: swe_bench_%_resolved
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value: 77.2
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source:
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url: https://huggingface.co/Qwen/Qwen3.6-27B
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name: Model Card
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.eval_results/terminal-bench-2.0.yaml
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id: harborframework/terminal-bench-2.0
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task_id: terminalbench_2
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value: 59.3
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source:
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url: https://huggingface.co/Qwen/Qwen3.6-27B
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name: Model Card
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