Instructions to use sanskar003/Qwen3.5-4B-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sanskar003/Qwen3.5-4B-AWQ with Transformers:

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

pipe = pipeline("image-text-to-text", model="sanskar003/Qwen3.5-4B-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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("sanskar003/Qwen3.5-4B-AWQ")
model = AutoModelForMultimodalLM.from_pretrained("sanskar003/Qwen3.5-4B-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?"}
        ]
    },
]
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 sanskar003/Qwen3.5-4B-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sanskar003/Qwen3.5-4B-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": "sanskar003/Qwen3.5-4B-AWQ",
		"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/sanskar003/Qwen3.5-4B-AWQ

SGLang

How to use sanskar003/Qwen3.5-4B-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 "sanskar003/Qwen3.5-4B-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": "sanskar003/Qwen3.5-4B-AWQ",
		"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 "sanskar003/Qwen3.5-4B-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": "sanskar003/Qwen3.5-4B-AWQ",
		"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 sanskar003/Qwen3.5-4B-AWQ with Docker Model Runner:
```
docker model run hf.co/sanskar003/Qwen3.5-4B-AWQ
```

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Qwen3.5-4B-AWQ (W4A16)

This is an AWQ W4A16 (4-bit weight) quantized version of Qwen/Qwen3.5-4B, packaged in the compressed-tensors format for efficient inference with vLLM.

The quantization reduces the on-disk and VRAM footprint while preserving the base model's vision-language capabilities, making it well-suited to a single consumer GPU (e.g. an RTX 5090 / Blackwell, sm_120).

About the base model

Qwen3.5-4B is a compact multimodal vision-language model from the Qwen team (Alibaba Group). It accepts interleaved image + text (and video) input and generates text, with a "thinking" reasoning mode enabled by default.


Base model	Qwen/Qwen3.5-4B
Parameters	~4B
Modality	Image-Text-to-Text (vision-language)
Architecture	Hybrid Gated DeltaNet + sparse MoE, with a vision encoder
Context length	262,144 tokens native (extensible with RoPE scaling)
Languages	201 languages and dialects
License	Apache 2.0

For full details on capabilities, benchmarks, and prompt formatting, see the base model card.

Quantization details


Method	AWQ (Activation-aware Weight Quantization)
Scheme	W4A16 (4-bit weights, 16-bit activations)
Format	`compressed-tensors` (`pack-quantized`)
Quantized modules	Linear layers of the language model
Kept in higher precision	Vision tower, router gates, and `lm_head`

The vision tower is intentionally left unquantized to preserve image understanding quality.

Usage

Serve with vLLM (OpenAI-compatible API)

docker run --rm --gpus all -p 8000:8000 \
  -v "$PWD:/models" \
  vllm/vllm-openai:latest \
  --model /models/Qwen3.5-4B-AWQ \
  --served-model-name Qwen3.5-4B-AWQ \
  --quantization compressed-tensors \
  --dtype float16 \
  --max-model-len 32768

Or directly from the Hub:

docker run --rm --gpus all -p 8000:8000 \
  vllm/vllm-openai:latest \
  --model sanskar003/Qwen3.5-4B-AWQ \
  --quantization compressed-tensors \
  --dtype float16

Query it (text)

curl -s http://localhost:8000/v1/chat/completions \
  -H 'Content-Type: application/json' -d '{
    "model": "Qwen3.5-4B-AWQ",
    "messages": [{"role": "user", "content": "Give me three uses for an RTX 5090."}]
  }'

Query it (image + text)

curl -s http://localhost:8000/v1/chat/completions \
  -H 'Content-Type: application/json' -d '{
    "model": "Qwen3.5-4B-AWQ",
    "messages": [{"role": "user", "content": [
      {"type": "text", "text": "Describe this image."},
      {"type": "image_url", "image_url": {"url": "http://images.cocodataset.org/train2017/000000231895.jpg"}}
    ]}]
  }'

Requirements

A GPU with INT4 Marlin kernel support (vLLM selects it automatically for compressed-tensors checkpoints). Verified on NVIDIA Blackwell (sm_120).
A recent vLLM build that supports the Qwen3_5ForConditionalGeneration architecture.

License

Released under Apache 2.0, inherited from the base model Qwen/Qwen3.5-4B. Please review and comply with the base model's license terms.

Citation

Please cite the original Qwen work — see the base model card for citation details.

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Safetensors

Model size

5B params

Tensor type

I64

I32

BF16

Model tree for sanskar003/Qwen3.5-4B-AWQ

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Quantized

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