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: 5,647 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 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 | from __future__ import annotations
import hashlib
import json
import re
from collections import Counter
from pathlib import Path
from typing import Any
PROJECT_ROOT = Path(__file__).resolve().parents[1]
LOCKED_PROTOCOL = PROJECT_ROOT / "configs/evaluation/recent4_full_v1.json"
class ProtocolValidationError(ValueError):
"""Raised when the locked evaluation protocol or its exports drift."""
def load_locked_protocol(path: Path = LOCKED_PROTOCOL) -> dict[str, Any]:
protocol = json.loads(path.read_text(encoding="utf-8"))
if protocol.get("status") != "locked":
raise ProtocolValidationError(f"{path}: protocol is not locked")
if protocol.get("protocol_id") != "recent4_full_v1":
raise ProtocolValidationError(f"{path}: unexpected protocol_id")
inference = protocol.get("inference") or {}
required_inference = {
"causal_prefix_only": True,
"sampling_fps": 1.0,
"context_policy": "recent_n",
"recent_frames": 4,
"allow_future_frames": False,
"allow_global_frames": False,
"allow_extra_frame_inspection": False,
"allow_persistent_visual_or_text_memory": False,
}
for key, expected in required_inference.items():
if inference.get(key) != expected:
raise ProtocolValidationError(
f"{path}: inference.{key}={inference.get(key)!r}, expected {expected!r}"
)
processor = inference.get("processor_contract") or {}
if not re.fullmatch(r"[0-9a-f]{64}", str(processor.get("preprocessor_config_sha256", ""))):
raise ProtocolValidationError("Configure the protocol with your processor identity first")
required_processor = {
"image_processor_type": "Qwen2VLImageProcessorFast",
"patch_size": 16,
"merge_size": 2,
"temporal_patch_size": 2,
"shortest_edge": 65536,
"longest_edge": 16777216,
"reference_visual_tokens_per_frame": 576,
}
for key, expected in required_processor.items():
if processor.get(key) != expected:
raise ProtocolValidationError(
f"{path}: inference.processor_contract.{key}="
f"{processor.get(key)!r}, expected {expected!r}"
)
return protocol
def validate_locked_protocol(path: Path = LOCKED_PROTOCOL) -> dict[str, Any]:
protocol = load_locked_protocol(path)
root = path.resolve().parents[2]
reports: dict[str, Any] = {}
for benchmark, benchmark_config in protocol["benchmarks"].items():
export = root / benchmark_config["export"]
rows = _load_jsonl(export)
expected_count = int(benchmark_config["expected_queries"])
if len(rows) != expected_count:
raise ProtocolValidationError(
f"{export}: {len(rows)} rows, expected {expected_count}"
)
digest = _sha256(export)
if digest != benchmark_config["export_sha256"]:
raise ProtocolValidationError(
f"{export}: sha256 {digest}, expected {benchmark_config['export_sha256']}"
)
uids = [str(row.get("uid") or "") for row in rows]
if any(not uid for uid in uids) or len(set(uids)) != len(uids):
raise ProtocolValidationError(f"{export}: missing or duplicate uid")
labels = Counter(str(row.get("benchmark") or "") for row in rows)
if labels != Counter({benchmark: expected_count}):
raise ProtocolValidationError(f"{export}: benchmark labels {dict(labels)}")
report: dict[str, Any] = {
"export": str(export.relative_to(root)),
"sha256": digest,
"queries": len(rows),
}
expected_groups = benchmark_config.get("expected_groups")
if expected_groups is not None:
actual_groups = Counter(
str((row.get("metadata") or {}).get("group") or "") for row in rows
)
if dict(actual_groups) != expected_groups:
raise ProtocolValidationError(
f"{export}: groups {dict(actual_groups)}, expected {expected_groups}"
)
report["groups"] = dict(sorted(actual_groups.items()))
expected_tasks = benchmark_config.get("expected_tasks")
if expected_tasks is not None:
actual_tasks = Counter(
str((row.get("metadata") or {}).get("task") or "") for row in rows
)
if dict(sorted(actual_tasks.items())) != expected_tasks:
raise ProtocolValidationError(
f"{export}: tasks {dict(actual_tasks)}, expected {expected_tasks}"
)
report["tasks"] = dict(sorted(actual_tasks.items()))
reports[benchmark] = report
return {
"status": "locked_protocol_valid",
"protocol_id": protocol["protocol_id"],
"protocol_path": str(path.resolve().relative_to(root)),
"benchmarks": reports,
}
def _load_jsonl(path: Path) -> list[dict[str, Any]]:
rows = []
with path.open(encoding="utf-8") as handle:
for line_number, line in enumerate(handle, start=1):
if not line.strip():
continue
value = json.loads(line)
if not isinstance(value, dict):
raise ProtocolValidationError(f"{path}:{line_number}: expected object")
rows.append(value)
return rows
def _sha256(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as handle:
for block in iter(lambda: handle.read(8 << 20), b""):
digest.update(block)
return digest.hexdigest()
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