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: 4,864 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 | from pathlib import Path
from data_pipeline.temporal_scope.build_source_aligned_sft_release import (
SourceAlignedSFTError,
_canonical_media_path,
_semantic_identity,
_teacher_used_for_labels,
)
import pytest
def _row(image: str = "media/one.jpg", question: str = "Which one?") -> dict:
return {
"messages": [
{
"role": "user",
"loss": False,
"content": [
{"type": "image", "image": image},
{"type": "text", "text": question},
],
},
{
"role": "assistant",
"loss": True,
"content": [{"type": "text", "text": "A"}],
},
]
}
def test_semantic_identity_binds_pixels_question_and_answer() -> None:
inventory = {"media/one.jpg": {"sha256": "a" * 64}}
baseline = _semantic_identity(_row(), inventory)
assert baseline == _semantic_identity(_row(question=" Which one? "), inventory)
assert baseline != _semantic_identity(_row(question="Which event?"), inventory)
changed = {"media/one.jpg": {"sha256": "b" * 64}}
assert baseline != _semantic_identity(_row(), changed)
def test_canonical_media_path_is_content_addressed() -> None:
digest = "ab" + "1" * 62
assert _canonical_media_path("legacy/frame.JPG", digest) == (
f"media/sha256/ab/{digest}.jpg"
)
def test_teacher_label_provenance_is_strict_and_propagatable() -> None:
assert _teacher_used_for_labels(
{"contracts": {"teacher_used_for_labels": True}}
)
assert not _teacher_used_for_labels(
{"contracts": {"teacher_used_for_labels": False}}
)
with pytest.raises(SourceAlignedSFTError):
_teacher_used_for_labels({"contracts": {}})
def test_legacy_v62di_loader_defaults_narrow_but_supports_committed_full_pool() -> None:
from data_pipeline.temporal_scope.build_source_aligned_sft_release import _load_legacy_v62di_source
with pytest.raises(SourceAlignedSFTError, match="Private legacy adapters"):
_load_legacy_v62di_source(Path("synthetic-unused-source"))
def test_legacy_repcount_normalization_is_hash_pinned_and_field_narrow() -> None:
from data_pipeline.temporal_scope.build_source_aligned_sft_release import _load_legacy_repcount_source
with pytest.raises(SourceAlignedSFTError, match="Private legacy adapters"):
_load_legacy_repcount_source(Path("synthetic-unused-source"))
def test_pinned_legacy_inputs_count_as_sealed_sources() -> None:
text = Path(
"data_pipeline/temporal_scope/build_source_aligned_sft_release.py"
).read_text()
assert "sealed_source_count" in text
assert 'getattr(args, "legacy_v62di_source", None)' in text
assert 'getattr(args, "legacy_repcount_source", None)' in text
def test_hash_bound_parent_selection_allows_one_source_filtering_only() -> None:
text = Path(
"data_pipeline/temporal_scope/build_source_aligned_sft_release.py"
).read_text()
assert 'getattr(args, "include_parent_selection", None) is None' in text
assert "unless a parent selection is supplied" in text
def test_composite_source_stage_filter_is_range_and_manifest_bound() -> None:
text = Path(
"data_pipeline/temporal_scope/build_source_aligned_sft_release.py"
).read_text()
assert "--include-source-stage-release-id" in text
assert "requested source stages are absent" in text
assert "source curriculum stage row count diverged" in text
assert '"included_source_stage_release_ids"' in text
def test_capability_parent_selection_is_hash_bound_and_fail_closed() -> None:
text = Path(
"data_pipeline/temporal_scope/build_source_aligned_sft_release.py"
).read_text()
assert "--include-parent-selection" in text
assert "iclr_video.capability_parent_selection.v0" in text
assert "iclr_video.capability_parent_selection.v1" in text
assert "source_release_membership_verified" in text
assert "selected parent IDs were absent" in text
assert '"parent_selection": parent_selection_record' in text
def test_ordered_curriculum_is_one_run_and_preserves_source_order() -> None:
text = Path(
"data_pipeline/temporal_scope/build_source_aligned_sft_release.py"
).read_text()
assert '"training_runs_required": 1' in text
assert '"source_stage_then_source_row"' in text
assert "if not ordered_curriculum" in text
def test_additive_composition_can_retain_exposures_without_curriculum_order() -> None:
text = Path(
"data_pipeline/temporal_scope/build_source_aligned_sft_release.py"
).read_text()
assert "--retain-semantic-exposures" in text
assert "ordered_curriculum or retain_semantic_exposures" in text
assert '"retain_semantic_exposures": retain_semantic_exposures' in text
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