Instructions to use RedHatAI/Qwen3.5-9B-quantized.w8a8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/Qwen3.5-9B-quantized.w8a8 with Transformers:

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

pipe = pipeline("image-text-to-text", model="RedHatAI/Qwen3.5-9B-quantized.w8a8")
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("RedHatAI/Qwen3.5-9B-quantized.w8a8")
model = AutoModelForImageTextToText.from_pretrained("RedHatAI/Qwen3.5-9B-quantized.w8a8")
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

vLLM

How to use RedHatAI/Qwen3.5-9B-quantized.w8a8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RedHatAI/Qwen3.5-9B-quantized.w8a8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/Qwen3.5-9B-quantized.w8a8",
		"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/RedHatAI/Qwen3.5-9B-quantized.w8a8

SGLang

How to use RedHatAI/Qwen3.5-9B-quantized.w8a8 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 "RedHatAI/Qwen3.5-9B-quantized.w8a8" \
    --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": "RedHatAI/Qwen3.5-9B-quantized.w8a8",
		"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 "RedHatAI/Qwen3.5-9B-quantized.w8a8" \
        --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": "RedHatAI/Qwen3.5-9B-quantized.w8a8",
		"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 RedHatAI/Qwen3.5-9B-quantized.w8a8 with Docker Model Runner:
```
docker model run hf.co/RedHatAI/Qwen3.5-9B-quantized.w8a8
```

reed-meyerson commited on Apr 16

Commit

4257ccf

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1 Parent(s): 0e6061f

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@@ -31,9 +31,9 @@ This model is a quantized version of [Qwen/Qwen3.5-9B](https://huggingface.co/Qw
 This model was obtained by quantizing the weights and activations of [Qwen/Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B) to INT8 data type, ready for inference with vLLM.
-This optimization reduces the model weights from 19.3 GB to 14.0 GB on disk (~27% reduction). The reduction is less than the theoretical 50% because the vision encoder remains in BF16.
-Only the weights and activations of the linear operators within transformer blocks are quantized using [LLM Compressor](https://github.com/vllm-project/llm-compressor). The vision encoder is not quantized.
 ## Deployment

 This model was obtained by quantizing the weights and activations of [Qwen/Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B) to INT8 data type, ready for inference with vLLM.
+This optimization reduces the model weights from 19.3 GB to 14.0 GB on disk (~27% reduction). The reduction is less than the theoretical 50% because the vision encoder, token embeddings, and linear attention layers remain in BF16.
+Only the weights and activations of the linear operators within transformer blocks are quantized using [LLM Compressor](https://github.com/vllm-project/llm-compressor). The vision encoder, token embeddings, and linear attention layers are not quantized.
 ## Deployment