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
File size: 2,874 Bytes
78ac017 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | """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}")
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