fix: release accelerator model refs during cleanup

[xk-huang]

Summary

• Release Accelerate-held model references during lmms.clean() by calling Accelerator.free_memory() when an accelerator is attached.
• Fixes a post-inference GPU memory retention path where prepared models remain referenced by Accelerate even after direct nn.Module attributes are deleted.
• Add regression coverage for accelerator-retained model references.

In scope

• Update lmms_eval/api/model.py so base model cleanup clears Accelerate internal model references before existing module deletion, garbage collection, and CUDA cache clearing.
• Add test/models/test_model_cleanup.py to verify cleanup calls free_memory() and removes direct model attributes.

Out of scope

• Does not change model generation, postprocessing, distributed gather behavior, sample logging, or task metrics.
• Does not add model-specific cleanup logic for llava_onevision1_5; the fix applies through the shared base cleanup path.

Validation

• python -m compileall -q lmms_eval/api/model.py test/models/test_model_cleanup.py | sample size: N=2 files | key metrics: syntax/import bytecode compilation | result: pass
• python -m pytest -q test/models/test_model_cleanup.py | sample size: N=1 test | key metrics: accelerator model references released during clean() | result: pass (1 passed in 3.47s)

Risk / Compatibility

• Low risk: this only runs during explicit model cleanup after inference; normal model execution is unchanged.
• For Accelerate-backed models, free_memory() clears Accelerate internal references before the existing direct module cleanup and CUDA cache clearing, reducing rank memory pressure before postprocessing/gathering.

Type of Change

• Bug fix (non-breaking change)
• New feature
• New benchmark/task
• New model integration
• Breaking change
• Documentation update
• Refactoring (no functional changes)