Image-Text-to-Text
Transformers
Safetensors
qwen3_vl
qwen3-vl
viss
streaming-video
visual-question-answering
causal-video
conversational
Instructions to use mis2/ViSS-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Download code/experiments/tvs_correctness.py from mis2/ViSS-8B: direct link, hf CLI and curl.
- Browser
- Download file 2.87 kB
-
https://huggingface.co/mis2/ViSS-8B/resolve/main/code/experiments/tvs_correctness.py
- Command line
-
hf download hf://mis2/ViSS-8B/code/experiments/tvs_correctness.py
-
curl -L -o tvs_correctness.py https://huggingface.co/mis2/ViSS-8B/resolve/main/code/experiments/tvs_correctness.py
2.87 kB
| """Official-protocol per-query correctness helpers for TVS analyses.""" | |
| from __future__ import annotations | |
| import json | |
| import re | |
| from pathlib import Path | |
| from typing import Any | |
| OVO_FORWARD_TASKS = {"REC", "SSR", "CRR"} | |
| def _mcq_letter(value: Any) -> str | None: | |
| text = str(value or "").strip() | |
| match = re.match(r"^[\(\[]?([A-D])(?:[\)\].:\s-]|$)", text, re.IGNORECASE) | |
| return match.group(1).upper() if match else None | |
| def load_gold(benchmark: str, annotation: Path) -> dict[str, dict[str, Any]]: | |
| data = json.loads(annotation.read_text()) | |
| gold: dict[str, dict[str, Any]] = {} | |
| if benchmark == "streamingbench": | |
| for video_index, entry in enumerate(data): | |
| for question_index, question in enumerate(entry.get("questions") or []): | |
| uid = f"streaming_{video_index}_{question_index}" | |
| gold[uid] = { | |
| "kind": "streaming_mcq", | |
| "answer": str(question["answer"]), | |
| "task": str(question.get("task_type") or ""), | |
| } | |
| return gold | |
| if benchmark != "ovo": | |
| raise ValueError(f"unsupported benchmark: {benchmark}") | |
| for item in data: | |
| task = str(item.get("task") or "").upper() | |
| if task not in OVO_FORWARD_TASKS: | |
| uid = f"ovo_{item['id']}" | |
| gold[uid] = { | |
| "kind": "ovo_mcq", | |
| "answer": chr(ord("A") + int(item["gt"])), | |
| "task": task, | |
| } | |
| continue | |
| for index, test_info in enumerate(item.get("test_info") or []): | |
| uid = f"ovo_{item['id']}_{index}" | |
| if task == "REC": | |
| gold[uid] = { | |
| "kind": "ovo_rec", | |
| "answer": str(test_info["count"]), | |
| "task": task, | |
| } | |
| else: | |
| gold[uid] = { | |
| "kind": "ovo_binary", | |
| "answer": int(test_info["type"]), | |
| "task": task, | |
| } | |
| return gold | |
| def is_correct(prediction: Any, gold: dict[str, Any]) -> bool: | |
| text = str(prediction or "") | |
| kind = gold["kind"] | |
| if kind == "streaming_mcq": | |
| letter = _mcq_letter(text) | |
| response = letter if letter is not None else text | |
| return response == str(gold["answer"]) | |
| if kind == "ovo_mcq": | |
| letter = _mcq_letter(text) | |
| response = letter if letter is not None else text | |
| return str(gold["answer"]) in response | |
| if kind == "ovo_rec": | |
| return "".join(re.findall(r"\d+", text)) == str(gold["answer"]) | |
| if kind == "ovo_binary": | |
| positive = int(gold["answer"]) == 1 | |
| if (text == "Y" and positive) or (text == "N" and not positive): | |
| return True | |
| return ("Yes" if positive else "No") in text | |
| raise ValueError(f"unknown gold kind: {kind}") | |