Instructions to use mis2/ViSS-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mis2/ViSS-8B with Transformers:

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

pipe = pipeline("image-text-to-text", model="mis2/ViSS-8B")
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("mis2/ViSS-8B")
model = AutoModelForMultimodalLM.from_pretrained("mis2/ViSS-8B", 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 mis2/ViSS-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mis2/ViSS-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mis2/ViSS-8B",
		"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/mis2/ViSS-8B

SGLang

How to use mis2/ViSS-8B 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 "mis2/ViSS-8B" \
    --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": "mis2/ViSS-8B",
		"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 "mis2/ViSS-8B" \
        --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": "mis2/ViSS-8B",
		"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 mis2/ViSS-8B with Docker Model Runner:
```
docker model run hf.co/mis2/ViSS-8B
```

ViSS-8B / code /docs /INSTALL.md

mis2

Release ViSS-8B weights, processors and source code

78ac017 verified 21 days ago

preview code

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2.03 kB

# Installation

Use Python 3.10 or later. Run commands from the source root; editable
installation keeps the configuration and script paths available.

## CPU source checks

```bash
python -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -e ".[test]"
python -m pytest -q tests
```

These tests exercise code contracts with synthetic inputs. They do not
validate a trained checkpoint or execute a full benchmark.

## GPU inference

Install a CUDA-enabled PyTorch build appropriate for the machine first,
then install the inference extras:

```bash
python -m pip install -e ".[inference]"
```

The inference extras pin PyTorch 2.7.0 and Transformers 4.57.3.
The PyTorch version follows the repository runtime requirements; the
Transformers version follows the retained ViSS model configuration.
The extras declare Accelerate, qwen-vl-utils, and Decord as well.
They specify an installation recipe, not a claim that every GPU/driver
combination has been validated.

Install FFmpeg and ensure `ffprobe` is on `PATH`. Multi-GPU shell launchers
also use Bash 4 or newer, `jq`, `sha256sum`, and `nvidia-smi`.

## Training and attention

```bash
python -m pip install -e ".[inference,training]"
python -m pip install flash-attn==2.7.4.post1 --no-build-isolation
```

FlashAttention installation needs a compatible CUDA toolkit and compiler.
The trainer defaults to `flash_attention_2`. Use `--attn-implementation sdpa`
for a development run when FlashAttention is unavailable; this changes the
runtime configuration.

The package includes no model weights. Before a GPU run, confirm that the
selected checkpoint loads with `AutoModelForImageTextToText` and that the
processor matches the intended evaluation contract.

## Environment records

Save the installed package versions and launch command alongside your own
runs. Generated outputs belong under `outputs/` or `checkpoints/` and are
excluded from the source archive. Do not commit credentials, benchmark media,
or local machine paths.

	# Installation

	Use Python 3.10 or later. Run commands from the source root; editable
	installation keeps the configuration and script paths available.

	## CPU source checks

	```bash
	python -m venv .venv
	source .venv/bin/activate
	python -m pip install --upgrade pip
	python -m pip install -e ".[test]"
	python -m pytest -q tests
	```

	These tests exercise code contracts with synthetic inputs. They do not
	validate a trained checkpoint or execute a full benchmark.

	## GPU inference

	Install a CUDA-enabled PyTorch build appropriate for the machine first,
	then install the inference extras:

	```bash
	python -m pip install -e ".[inference]"
	```

	The inference extras pin PyTorch 2.7.0 and Transformers 4.57.3.
	The PyTorch version follows the repository runtime requirements; the
	Transformers version follows the retained ViSS model configuration.
	The extras declare Accelerate, qwen-vl-utils, and Decord as well.
	They specify an installation recipe, not a claim that every GPU/driver
	combination has been validated.

	Install FFmpeg and ensure `ffprobe` is on `PATH`. Multi-GPU shell launchers
	also use Bash 4 or newer, `jq`, `sha256sum`, and `nvidia-smi`.

	## Training and attention

	```bash
	python -m pip install -e ".[inference,training]"
	python -m pip install flash-attn==2.7.4.post1 --no-build-isolation
	```

	FlashAttention installation needs a compatible CUDA toolkit and compiler.
	The trainer defaults to `flash_attention_2`. Use `--attn-implementation sdpa`
	for a development run when FlashAttention is unavailable; this changes the
	runtime configuration.

	The package includes no model weights. Before a GPU run, confirm that the
	selected checkpoint loads with `AutoModelForImageTextToText` and that the
	processor matches the intended evaluation contract.

	## Environment records

	Save the installed package versions and launch command alongside your own
	runs. Generated outputs belong under `outputs/` or `checkpoints/` and are
	excluded from the source archive. Do not commit credentials, benchmark media,
	or local machine paths.