feat(modeling): simplify abstract_init

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

feat: handle model parallel

fix: style

style(tokenizer): remove unused variables

feat: use fast tokenizer

feat(train): improve pjit speed

fix(train): consider correct batch size

feat(train): distributed_shampoo with pjit

style: unsused import

feat(model): clean way to load on cpu

feat(train): no batch dimension with pjit

feat(train): progress on pjit

feat: use_artifact if run existing

Load from wandb artifact (#121)

Style (isort).

Tokenizer, config, model can be loaded from wandb.

feat(data): support accumulation in non-streaming

feat: custom gradient accumulation

Change import order again.

Fix import order to make isort happy.

Accept changes suggested by linter.

Never consider local dirs as remote wandb references.

Store resolved path after loading model.

Override from_pretrained to support wandb artifacts.

fix(data): no shuffling of validation data

fix(train): handle seed_dataset

feat: support pypi