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dalle-mini

Commit History

feat: load from bucket
1c4e839

boris commited on

feat(train): save to bucket
50498e6

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feat: reduce artifact space + offset step
34cf91c

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feat(demo): update reference
e558000

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feat: restore weights on CPU
5f954fc

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feat(train): simplify tokenizer loading
4cb21dd

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doc: update README
db5a22a

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feat: cleanup notebook
5a390e8

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feat: wandb required for checkpoints
38c2c4e

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feat(demo): uncomment pip install
094e178

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feat: improve inference demo
35fe578

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fix: position embedding for generate method
ebac379

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feat(train): use compilation cache
da9367c

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fix: typo
68cc185

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fix: load from checkpoint
44b7c3e

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fix: style
d483294

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Merge branch 'main' of https://github.com/borisdayma/dalle-mini into main
0a691de

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feat: log num_parameters early
7cfe576

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fix: distributed shampoo class
696422e

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feat: update distributed_shampoo
5996680

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feat(modeling): simplify abstract_init
fa72aa7

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feat(train) - handle multiple nodes (#130)
0952927
unverified

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feat: handle model parallel
1bb3269

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feat(train): more custom x-axis
5f28cd2

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feat(train): split artifact into model/state (#128)
7c4c287
unverified

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fix: style
386f839

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fix(train): opt_state_shape for distributed_shampoo
225b6ff

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feat(train): split artifact into model/state
fa5b058

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style(tokenizer): remove unused variables
605df32

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feat: use fast tokenizer
767d78a

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feat(train): another 25% faster
14abe8c

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Merge pull request #127 from borisdayma/pjit-t5x
e4401dd
unverified

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feat(train): overhead from 70% to 1% 🥳
2b7f5f1

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feat(pjit): follow t5x style
7b5868f

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fix(train): grads spec
00710bc

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feat(train): improve pjit speed
f254058

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fix(train): consider correct batch size
b7c7458

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feat(train): custom start_preconditioning_step
8149924

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feat(train): handle distributed_shampoo in pjit
032f623

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feat: update distributed_shampoo + fix None spec
8a9e367

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feat(train): distributed_shampoo with pjit
cc34d07

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feat(train): use pjit (#125)
f5239e1
unverified

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style: unsused import
7a176b9

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fix style
f044cb8

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feat(train): restore opt_state efficiently
1bfc1b5

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feat(model): clean way to load on cpu
12f323d

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feat(train): load model on CPU
3d43591

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feat(train): different rng per node
2d212d8

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feat(train): no batch dimension with pjit
df1fe19

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feat(train): progress on pjit
49597a2

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