Image-Text-to-Text
Transformers
Safetensors
qwen3_5
gsq
gumbel-softmax
quantization
ptq
qwen
vllm
humming
conversational
compressed-tensors
Instructions to use ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ") 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("ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ") model = AutoModelForMultimodalLM.from_pretrained("ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ", 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]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ", "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/ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ
- SGLang
How to use ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ 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 "ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ" \ --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": "ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ", "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 "ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ" \ --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": "ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ", "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 ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ with Docker Model Runner:
docker model run hf.co/ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ
Plans to upstream this patch in vLLM?
#2
by ibaldonl - opened
Are there any plans to upstream this patch into vLLM itself?
I searched issues and pull requests for "GSQ" which didn't found anything.
Thanks for bringing this to our attention. We don’t know why vllm has not added the support for embedding quantization. But I will open an issue there.