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Welcome to our open source version of DeepMind's Flamingo model! In this repository, we provide a PyTorch implementation for training and evaluating OpenFlamingo models. We also provide an initial OpenFlamingo 9B model trained on a new Multimodal C4 dataset (coming soon). Please refer to our blog post for more details. This repo is still under development, and we hope to release better-performing and larger OpenFlamingo models soon. If you have any questions, please feel free to open an issue. We also welcome contributions! We provide an initial OpenFlamingo 9B model using a CLIP ViT-Large vision encoder and a LLaMA-7B language model. In general, we support any CLIP vision encoder. For the language model, we support LLaMA, OPT, GPT-Neo, GPT-J, and Pythia models. OpenFlamingo is a multimodal language model that can be used for a variety of tasks. It is trained on a large multimodal dataset.
Features
- OpenFlamingo seeks to fuse a pretrained vision encoder and a language model using cross attention layers
- OpenFlamingo is a multimodal language model that can be used for a variety of tasks
- Trained on a large multimodal dataset (e.g. Multimodal C4) and can be used to generate text conditioned on interleaved images/text
- OpenFlamingo can be used to generate a caption for an image
- Generate a question given an image and a text passage
- We provide an initial OpenFlamingo 9B model using a CLIP ViT-Large vision encoder and a LLaMA-7B language model
Project Samples
