Instructions to use Xingyu-Zheng/Qwopus3.6-27B-v1-preview-INT4-FOEM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- vLLM
How to use Xingyu-Zheng/Qwopus3.6-27B-v1-preview-INT4-FOEM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Xingyu-Zheng/Qwopus3.6-27B-v1-preview-INT4-FOEM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Xingyu-Zheng/Qwopus3.6-27B-v1-preview-INT4-FOEM", "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/Xingyu-Zheng/Qwopus3.6-27B-v1-preview-INT4-FOEM
- SGLang
How to use Xingyu-Zheng/Qwopus3.6-27B-v1-preview-INT4-FOEM 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 "Xingyu-Zheng/Qwopus3.6-27B-v1-preview-INT4-FOEM" \ --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": "Xingyu-Zheng/Qwopus3.6-27B-v1-preview-INT4-FOEM", "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 "Xingyu-Zheng/Qwopus3.6-27B-v1-preview-INT4-FOEM" \ --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": "Xingyu-Zheng/Qwopus3.6-27B-v1-preview-INT4-FOEM", "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" } } ] } ] }' - Unsloth Studio
How to use Xingyu-Zheng/Qwopus3.6-27B-v1-preview-INT4-FOEM with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Xingyu-Zheng/Qwopus3.6-27B-v1-preview-INT4-FOEM to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Xingyu-Zheng/Qwopus3.6-27B-v1-preview-INT4-FOEM to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Xingyu-Zheng/Qwopus3.6-27B-v1-preview-INT4-FOEM to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Xingyu-Zheng/Qwopus3.6-27B-v1-preview-INT4-FOEM", max_seq_length=2048, ) - Docker Model Runner
How to use Xingyu-Zheng/Qwopus3.6-27B-v1-preview-INT4-FOEM with Docker Model Runner:
docker model run hf.co/Xingyu-Zheng/Qwopus3.6-27B-v1-preview-INT4-FOEM
Improved quality by changing the chat_template.jinja
I have changed the chat_template.jinja to the official one and the scores on the https://benchlocal.com/ bench packs went from an average of 60.4 points to 82 points (Qwen/Qwen3.6-27B-FP8 has 82.5). Afterwards it has the highest Hermesagent scores of all tested 27B models. Might be worth changing it here in the repo.
That’s surprising! Let me confirm: do you mean that we should use https://huggingface.co/Qwen/Qwen3.6-27B/blob/main/chat_template.jinja instead?
Yes. You can install benchlocal on your machine and run your model as it is, than replace the jinja file and try again. You will see a huge difference. The currenty version get 60.4 points across all benchmarks, which is less than Qwen 3.5 9B (76.9 points).
I sincerely appreciate your testing and feedback! I may not have enough time to reproduce the issue myself in the near future, so I directly updated the chat_template.jinja according to your suggestion and added a corresponding note to the Model Card.
