This repository is a compact, custom PyTorch implementation of Blip for Generation. The nano configuration is intended for code review, smoke tests, and small controlled experiments rather than as a production-ready pretrained release. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses adamw with a exponential schedule. These are…
Independent publisher
Zachary Gomez
zacharygl
Interested in machine learning, NLP and open source AI.
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Datasets in Library0
Models on Hugging Face2
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