Instructions to use Pilcothink/Qwen3.5-9B-MixedInt4-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Pilcothink/Qwen3.5-9B-MixedInt4-AutoRound with Transformers:

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

pipe = pipeline("image-text-to-text", model="Pilcothink/Qwen3.5-9B-MixedInt4-AutoRound")
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("Pilcothink/Qwen3.5-9B-MixedInt4-AutoRound")
model = AutoModelForMultimodalLM.from_pretrained("Pilcothink/Qwen3.5-9B-MixedInt4-AutoRound", 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 Pilcothink/Qwen3.5-9B-MixedInt4-AutoRound with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Pilcothink/Qwen3.5-9B-MixedInt4-AutoRound"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Pilcothink/Qwen3.5-9B-MixedInt4-AutoRound",
		"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/Pilcothink/Qwen3.5-9B-MixedInt4-AutoRound

SGLang

How to use Pilcothink/Qwen3.5-9B-MixedInt4-AutoRound 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 "Pilcothink/Qwen3.5-9B-MixedInt4-AutoRound" \
    --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": "Pilcothink/Qwen3.5-9B-MixedInt4-AutoRound",
		"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 "Pilcothink/Qwen3.5-9B-MixedInt4-AutoRound" \
        --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": "Pilcothink/Qwen3.5-9B-MixedInt4-AutoRound",
		"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 Pilcothink/Qwen3.5-9B-MixedInt4-AutoRound with Docker Model Runner:
```
docker model run hf.co/Pilcothink/Qwen3.5-9B-MixedInt4-AutoRound
```

Pilcothink commited on 19 days ago

Commit

dc10718

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Update README.md

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@@ -45,6 +45,28 @@ original model.
 No independent benchmark results are currently provided for this quantized
 version.
 ## Serving with vLLM
 If the installed vLLM version supports this model architecture and AutoRound

 No independent benchmark results are currently provided for this quantized
 version.
+## evaluation
+Evaluation was performed using AutoRound’s evaluation CLI, powered by LM Evaluation Harness.
+| Benchmark     |   Metric | Qwen3.5-9B | Qwen3.5-9B-MixedInt4-AutoRound |  Difference | Recovery Rate |
+| ------------- | -------: | ---------: | -----------------------------: | ----------: | ------------: |
+| MMLU          |      acc |     78.66% |                         77.62% |     -1.04%p |        98.68% |
+| ARC-Challenge | acc_norm |     55.80% |                         55.03% |     -0.77%p |        98.62% |
+| BoolQ         |      acc |     89.17% |                         86.91% |     -2.26%p |        97.47% |
+| HellaSwag     | acc_norm |     78.15% |                         77.45% |     -0.70%p |        99.10% |
+| PIQA          | acc_norm |     80.03% |                         80.20% |     +0.17%p |       100.21% |
+| WinoGrande    |      acc |     73.01% |                         71.82% |     -1.19%p |        98.37% |
+| **Average**   |        — | **75.80%** |                     **74.84%** | **-0.97%p** |    **98.73%** |
+| MMLU Category   | Qwen3.5-9B | Qwen3.5-9B-MixedInt4-AutoRound | Difference | Recovery Rate |
+| --------------- | ---------: | -----------------------------: | ---------: | ------------: |
+| Humanities      |     70.48% |                         68.93% |    -1.55%p |        97.80% |
+| Other           |     83.20% |                         82.52% |    -0.68%p |        99.18% |
+| Social Sciences |     86.90% |                         86.55% |    -0.35%p |        99.60% |
+| STEM            |     78.34% |                         77.07% |    -1.27%p |        98.38% |
 ## Serving with vLLM
 If the installed vLLM version supports this model architecture and AutoRound