transformers
91fb62d0 - Speedup training by using numpy instead of jnp for batch shuffling (#15963)

Commit

3 years ago

Speedup training by using numpy instead of jnp for batch shuffling (#15963) Speedup training by using numpy instead of jnp for batch shuffling Co-authored-by: Yeb Havinga <y.t.havinga@mgrid.net>

References

#15963 - Speedup T5 Flax training by using Numpy instead of JAX for batch shuffling

#19449 - [WIP] Fix weights initialization of several vision models

#27720 - Add common processor tests

#29969 - [SigLIP] Add fast tokenizer

#32831 - [Docs] Update resources

#33111 - [Backbone] Remove out_features everywhere

#33174 - [Zero-shot image classification pipeline] Remove tokenizer_kwargs

#39821 - Support MetaCLIP 2

#58 - Add EoMT DINOv3 model

#59 - Fix attention mask handling in EoMT-DINOv3 converter

#41212 - Add EoMT with DINOv3 backbone

#62 - Add initial DEIMv2 model implementation

Author

yhavinga

Parents

ea07064a

transformers 91fb62d0 - Speedup training by using numpy instead of jnp for batch shuffling (#15963)

transformers
91fb62d0 - Speedup training by using numpy instead of jnp for batch shuffling (#15963)