Instructions to use useful-quants/InternVL3_5-4B-Instruct-W4A16-BF16Vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use useful-quants/InternVL3_5-4B-Instruct-W4A16-BF16Vision with Transformers:

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

pipe = pipeline("image-text-to-text", model="useful-quants/InternVL3_5-4B-Instruct-W4A16-BF16Vision", trust_remote_code=True)
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 AutoModel
model = AutoModel.from_pretrained("useful-quants/InternVL3_5-4B-Instruct-W4A16-BF16Vision", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use useful-quants/InternVL3_5-4B-Instruct-W4A16-BF16Vision with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "useful-quants/InternVL3_5-4B-Instruct-W4A16-BF16Vision"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "useful-quants/InternVL3_5-4B-Instruct-W4A16-BF16Vision",
		"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/useful-quants/InternVL3_5-4B-Instruct-W4A16-BF16Vision

SGLang

How to use useful-quants/InternVL3_5-4B-Instruct-W4A16-BF16Vision 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 "useful-quants/InternVL3_5-4B-Instruct-W4A16-BF16Vision" \
    --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": "useful-quants/InternVL3_5-4B-Instruct-W4A16-BF16Vision",
		"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 "useful-quants/InternVL3_5-4B-Instruct-W4A16-BF16Vision" \
        --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": "useful-quants/InternVL3_5-4B-Instruct-W4A16-BF16Vision",
		"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 useful-quants/InternVL3_5-4B-Instruct-W4A16-BF16Vision with Docker Model Runner:
```
docker model run hf.co/useful-quants/InternVL3_5-4B-Instruct-W4A16-BF16Vision
```

InternVL3_5-4B-Instruct W4A16 BF16-Vision

Ampere-friendly serving build of OpenGVLab/InternVL3_5-4B-Instruct. Text-side linears are compressed-tensors W4A16. The vision tower and multimodal connector stay BF16.

Stock proof

docker run --rm -it \
  --gpus all \
  --ipc=host \
  -p 8001:8000 \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  vllm/vllm-openai:latest \
  OpenGVLab/InternVL3_5-4B-Instruct \
  --served-model-name InternVL3_5-4B-Instruct-stock \
  --trust-remote-code \
  --dtype bfloat16 \
  --max-model-len 4096

Use

docker run --rm -it \
  --gpus all \
  --ipc=host \
  -p 8001:8000 \
  -v /path/to/InternVL3_5-4B-Instruct-W4A16-BF16Vision:/model \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  vllm/vllm-openai:latest \
  --model /model \
  --served-model-name InternVL3_5-4B-Instruct-W4A16-BF16Vision \
  --trust-remote-code \
  --dtype bfloat16 \
  --quantization compressed-tensors \
  --max-model-len 4096 \
  --limit-mm-per-prompt '{"image":1}' \
  --allowed-local-media-path /

Smoke test

python verify.py --url http://localhost:8001/v1/chat/completions

Notes

Built for RTX 30xx/40xx Ampere cards.
Vision stays BF16 on disk and at runtime.
The repo includes the calibration corpus and local smoke_image.png asset used for verification; the image test uses that file directly.

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Safetensors

Model size

5B params

Tensor type

I64

I32

BF16

Model tree for useful-quants/InternVL3_5-4B-Instruct-W4A16-BF16Vision

Base model

OpenGVLab/InternVL3_5-4B-Pretrained

Finetuned

OpenGVLab/InternVL3_5-4B-Instruct

Quantized

(1)

this model