Practical overview
When it fits
Use Vilio when you need repository-based model code and notebook examples for vision-language research, especially hateful-meme experiments or meme inference.
Who it is for
- Researchers reproducing or adapting vision-language model experiments.
- Developers building notebook-based inference demonstrations for memes.
- Practitioners comparing model architectures for image-and-text classification.
Documented uses
- Reproduce results for the Hateful Memes Task using the repository's documented score-reproduction path.
- Run inference on memes using the provided example notebooks and adapt the input dataset or pretrained model.
- Explore or adapt implementations of ERNIE-ViL, DeVLBERT, OSCAR, UNITER, VisualBERT and LXMERT.
- Use the framework for another task by following its GETTING_STARTED.md guide.
Requirements & limits
The repository is public and identifies an MIT license.
- The repository combines PyTorch implementations with a PaddlePaddle ERNIE-ViL sub-repository.
- The README provides separate GPU and CPU inference notebook examples.
- The repository notes that import statements currently work only when run from the main folder and lists easier import-based model loading as a to-do.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Reproduce the Hateful Memes Task results
Use the repository’s stated score-reproduction path for the Hateful Memes Task.
Before you start
- Vilio repository
- Follow the repository's SCORE_REPRO.md instructions
Steps
Consult SCORE_REPRO.md for the procedure to reproduce performance on the Hateful Memes Task.
Use the repository's shell files for reproducing hateful-meme results, as directed by those instructions.
Expected outcome
A workflow for reproducing the repository's Hateful Memes Task performance.
Adapt a notebook for meme inference
Use a Vilio example notebook as a starting point for inference on a meme.
Before you start
- A Vilio inference notebook, with GPU and CPU examples listed by the README
- An example input dataset and pretrained model to adapt
Steps
Open the GPU or CPU inference notebook listed in the repository README.
Adapt the example input dataset or input model for the meme and pretrained model you want to use.
Expected outcome
An adapted notebook workflow for performing inference on a meme.
Guide limitations
- The README does not specify detailed environment setup or model-specific adaptation steps.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 17 October 2026
- Original website title
- Muennighoff/vilio · Official homepage title · retained in its source language