Instructions to use QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ")
model = AutoModelForMultimodalLM.from_pretrained("QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ

SGLang

How to use QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ 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 "QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ" \
    --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": "QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ" \
        --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": "QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ with Docker Model Runner:
```
docker model run hf.co/QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ
```

tclf90 commited on Oct 8, 2025

Commit

a5ea107

verified ·

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@@ -13,18 +13,19 @@ base_model_relation: quantized
 Base Model: [Qwen/Qwen3-VL-30B-A3B-Instruct](https://www.modelscope.cn/models/Qwen/Qwen3-VL-30B-A3B-Instruct)
 ### 【Dependencies / Installation】
-As of **2025-10-04**, create a fresh Python environment and run:
 ```bash
-pip install -U pip uv
-# Install vLLM >= 0.11.0
 uv pip install -U vllm
 # Install Qwen-VL utility library (recommended for offline inference)
 uv pip install qwen-vl-utils==0.0.14
 ```
-[vLLM Installation Guide](https://docs.vllm.ai/projects/recipes/en/latest/Qwen/Qwen3-VL.html)
 ### 【vLLM Startup Command】

 Base Model: [Qwen/Qwen3-VL-30B-A3B-Instruct](https://www.modelscope.cn/models/Qwen/Qwen3-VL-30B-A3B-Instruct)
 ### 【Dependencies / Installation】
+As of **2025-10-08**, create a fresh Python environment and run:
 ```bash
+uv venv
+source .venv/bin/activate
+# Install vLLM >=0.11.0
 uv pip install -U vllm
 # Install Qwen-VL utility library (recommended for offline inference)
 uv pip install qwen-vl-utils==0.0.14
 ```
+For more details, refer to [vLLM Official Qwen3-VL Guide](https://docs.vllm.ai/projects/recipes/en/latest/Qwen/Qwen3-VL.html)
 ### 【vLLM Startup Command】