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-0408 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMOSS-Team/MOSS-VL-Instruct-0408 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("OpenMOSS-Team/MOSS-VL-Instruct-0408", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
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## 📌 Introduction
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MOSS-VL-Instruct-0408 is the instruction-tuned checkpoint of the MOSS-VL series, part of the OpenMOSS ecosystem dedicated to advancing
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Built on top of MOSS-VL-Base-0408 through supervised fine-tuning (SFT), this checkpoint is designed as a high-performance offline multimodal engine. It delivers strong, well-rounded performance across the full spectrum of vision-language tasks — including image understanding, OCR, document parsing, visual reasoning, and instruction following — and is particularly outstanding at video understanding, from long-form comprehension to fine-grained temporal reasoning and action recognition.
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## 📌 Introduction
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MOSS-VL-Instruct-0408 is the instruction-tuned checkpoint of the MOSS-VL series, part of the OpenMOSS ecosystem dedicated to advancing visual understanding.
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Built on top of MOSS-VL-Base-0408 through supervised fine-tuning (SFT), this checkpoint is designed as a high-performance offline multimodal engine. It delivers strong, well-rounded performance across the full spectrum of vision-language tasks — including image understanding, OCR, document parsing, visual reasoning, and instruction following — and is particularly outstanding at video understanding, from long-form comprehension to fine-grained temporal reasoning and action recognition.
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