Video-Text-to-Text
Transformers
Safetensors
English
Chinese
moss_vl
feature-extraction
Base
Video-Understanding
Image-Understanding
MOSS-VL
OpenMOSS
multimodal
video
vision-language
custom_code
Instructions to use OpenMOSS-Team/MOSS-VL-Base-0708 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMOSS-Team/MOSS-VL-Base-0708 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("OpenMOSS-Team/MOSS-VL-Base-0708", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
30 days ago
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per-sample media budgets in processor; per-sample MRoPE position ids for batched vision inputs
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per-sample media budgets in processor; per-sample MRoPE position ids for batched vision inputs
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sync offline system prompts with training data: no_thinking -> no system message, deep_thinking -> <think>/<answer> tags
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fix: apply query RoPE in cross-attention when reusing cached vision KV during decode
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Unify MOSS-VL processor and inference preprocessing defaults
Verified signatureUse the shared processor and tokenizer, set image/video pixel budgets to 201326592, and disable vision tail padding with vision_seq_pad_multiple=1. Preserve model implementation and weights.ba1bee7
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