Instructions to use DavidAU/Qwen3.5-9B-Claude-4.6-HighIQ-THINKING with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DavidAU/Qwen3.5-9B-Claude-4.6-HighIQ-THINKING with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="DavidAU/Qwen3.5-9B-Claude-4.6-HighIQ-THINKING") 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("DavidAU/Qwen3.5-9B-Claude-4.6-HighIQ-THINKING") model = AutoModelForMultimodalLM.from_pretrained("DavidAU/Qwen3.5-9B-Claude-4.6-HighIQ-THINKING", 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 DavidAU/Qwen3.5-9B-Claude-4.6-HighIQ-THINKING with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DavidAU/Qwen3.5-9B-Claude-4.6-HighIQ-THINKING" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DavidAU/Qwen3.5-9B-Claude-4.6-HighIQ-THINKING", "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/DavidAU/Qwen3.5-9B-Claude-4.6-HighIQ-THINKING
- SGLang
How to use DavidAU/Qwen3.5-9B-Claude-4.6-HighIQ-THINKING 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 "DavidAU/Qwen3.5-9B-Claude-4.6-HighIQ-THINKING" \ --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": "DavidAU/Qwen3.5-9B-Claude-4.6-HighIQ-THINKING", "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 "DavidAU/Qwen3.5-9B-Claude-4.6-HighIQ-THINKING" \ --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": "DavidAU/Qwen3.5-9B-Claude-4.6-HighIQ-THINKING", "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 DavidAU/Qwen3.5-9B-Claude-4.6-HighIQ-THINKING 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 DavidAU/Qwen3.5-9B-Claude-4.6-HighIQ-THINKING 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 DavidAU/Qwen3.5-9B-Claude-4.6-HighIQ-THINKING to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for DavidAU/Qwen3.5-9B-Claude-4.6-HighIQ-THINKING to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="DavidAU/Qwen3.5-9B-Claude-4.6-HighIQ-THINKING", max_seq_length=2048, ) - Docker Model Runner
How to use DavidAU/Qwen3.5-9B-Claude-4.6-HighIQ-THINKING with Docker Model Runner:
docker model run hf.co/DavidAU/Qwen3.5-9B-Claude-4.6-HighIQ-THINKING
vs Instruct
Tested both, Thinking is performing far better as coding agent and delivering more complex results.
Also did several tricky and logical math questions to test abilities were done correctly on first try with thinking model.
Kinda making me doubt benchmark results vs instruct model which didnt work as well
Some uses cases work better with thinking VS instruct.
Frankly we were just as surprised here with INSTRUCT vs THINKING benchmarks too.
It maybe looping issues and/or length of thinking which is resulting in poor "thinking" benchmarks (IE too long thinking = fail).
However, fine tunes of "thinking" are improving the thinking benchmarks (the 7 ones we measure).
The difference between thinking/instruct benchmarks is consistent with different sized models.
And oddly ; larger model benchmarks are relatively close to smaller ones.
Another odd finding.
There are other issues with the Qwen3.5s also; which are being testing/tweaked - including base/root models.
These are under testing atm.
SIDE NOTE: we noticed this same issue with Qwen 3 Instruct VS Thinking.