feat(serverless): support forking checkpoints from a different W&B entity

[AnnaSuSu]

What

ServerlessBackend._experimental_fork_checkpoint builds the source W&B artifact path from the destination model's entity:

from_entity = model.entity or api.default_entity
collection_path = f"{from_entity}/{from_project}/{from_model}"

So a checkpoint can only be forked within the same entity — forking e.g. willow-voice/willow_normal/kl-000-1 into wb-training/willow_normal/my-new-run fails because it looks for the artifact under wb-training. The only workaround is to download from the source entity and re-upload to the destination, doubling storage.

Closes #649.

Fix

Add an optional from_entity parameter to _experimental_fork_checkpoint and resolve the source entity as from_entity → model.entity → api.default_entity. The resolution + path building move into a small pure helper, _wandb_checkpoint_collection_path, which also raises a clear ValueError when no entity can be determined (previously this produced a "None/…" path).

await backend._experimental_fork_checkpoint(
    model,                       # destination, e.g. wb-training/...
    from_model="kl-000-1",
    from_project="willow_normal",
    from_entity="willow-voice",  # NEW: source entity
)

This re-implements the approach from #676 (validated there but voluntarily closed by its author); credit to @poofeth for the original.

Tests

New tests/unit/test_serverless_fork_checkpoint.py — no GPU/backend deps, runs on the base install:

• _wandb_checkpoint_collection_path resolution: explicit from_entity wins → falls back to model entity → falls back to default entity → raises when none.
• An async test drives _experimental_fork_checkpoint through the W&B branch with a fake wandb.Api and asserts the artifact query uses the explicit source entity (("lora", "src-entity/src-project/src-model")).

tests/unit/test_serverless_fork_checkpoint.py ..... [5 passed]
existing serverless unit tests: 12 passed (no regressions)