Instructions to use hfl/vle-large-for-vqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hfl/vle-large-for-vqa with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hfl/vle-large-for-vqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from hfl/vle-large-for-vqa: direct link, hf CLI and curl.
- Browser
- Download file 700 Bytes
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https://huggingface.co/hfl/vle-large-for-vqa/resolve/main/README.md
- Command line
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hf download hf://hfl/vle-large-for-vqa/README.md
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curl -L -o README.md https://huggingface.co/hfl/vle-large-for-vqa/resolve/main/README.md
700 Bytes
metadata
license: apache-2.0
language:
- en
VLE (Visual-Language Encoder) is an image-text multimodal understanding model built on the pre-trained text and image encoders. It can be used for multimodal discriminative tasks such as visual question answering and image-text retrieval. Especially on the visual commonsense reasoning (VCR) task, which requires high-level language understanding and reasoning skills, VLE achieves significant improvements.
For more details see https://github.com/iflytek/VLE.
Online VLE demo on Visual Question Answering: https://huggingface.co/spaces/hfl/VQA_VLE_LLM