A tiny open vision-language model for learning how image-to-text works
SupraLabs released SupraVL-Nano-900k, its first . It has about 900,000 and was trained from scratch on the Flickr8k dataset. It is not meant to be a production model.
It is meant to be a clear, readable blueprint for understanding how image-to-text models work inside. The whole fits in one . The image side uses a CNN visual encoder, and the text side uses a GPT-2-style decoder.
Its tokenizer was trained on the Flickr8k captions and uses 2,048 BPE tokens. The model combines 16 s and 48 text tokens, for 64 total positions.
Key points
- SupraVL-Nano-900k is a small with about 900,000 .
- It was trained from scratch on Flickr8k and fits in one .
- The image part uses a CNN visual encoder, while the text part uses a GPT-2-style decoder.
- It uses 16 s and 48 text tokens, for 64 total positions.
- The release is mainly for learning, not for production use.