Video-Text-to-Text
Transformers
Safetensors
English
Chinese
moss_vl
feature-extraction
SFT
Video-Understanding
Image-Understanding
MOSS-VL
OpenMOSS
multimodal
video
vision-language
custom_code
Instructions to use OpenMOSS-Team/MOSS-VL-Instruct-0708 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMOSS-Team/MOSS-VL-Instruct-0708 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("OpenMOSS-Team/MOSS-VL-Instruct-0708", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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feat(model): add boundary-aware FA3 cross-attention
Verified signatureThis adds an opt-in path for the specialized MOSS-VL FA3 cross-attention kernel. With `cross_attention_implementation="flash_attention_3"`, the model converts each frame-level mask row to an int32 visible-KV boundary and passes it to FA3. This path no longer materializes a dense text-by-vision mask. Existing eager, SDPA, and FA2 behavior is unchanged. Setting global `attn_implementation="flash_attention_3"` alone also keeps the previous cross-attention dispatch. The separate override lets Ampere runs keep FA2 or SDPA for self-attention while using the MOSS FA3 kernel for cross-attention. Tested: - 11 CPU unit tests covering mask parity, invalid masks, config serialization, dispatch, and default compatibility - A800 BF16 forward/backward parity at the model head shape: 32 query heads, 8 KV heads, head dimension 128 - MOSS-VL 11B two-image prefill and four cached decode steps: 60 FA3 calls, no dense mask expansion, stable 48-token vision KV cache, and matching greedy top-1 against SDP…0dbae6f
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