docs(streaming): correct load_hot_layer's "page cache" backing claim - #118
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load_hot_layer()'s docstring described the hot-expert backing store as "page cache" that "survives across requests, counts ZERO toward MLX active" -- true of the zero-MLX-cost and survives-across-requests parts, but the backing is built with np.concatenate(rows), which copies. That makes it resident anonymous process memory, not mmap'd/file-backed page cache: it is not reclaimable by the OS the way page cache is, and does not shrink under memory pressure the way the docstring's "page cache" framing implies. Fixes the misleading description and the matching inline comment further down the same method. No behavior change. Addresses point 2 of Edge0-AI#106 (the README wording in points 1 and 3 was already addressed in the issue thread by the maintainer). Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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Addresses point 2 of #106.
load_hot_layer()keeps copied NumPy buffers, not file-backed page cache. Clarify that these buffers survive across requests and are outside MLX active memory. They can be paged out by the OS, but cannot be discarded and reloaded from the checkpoint like clean mapped file pages.Documentation only: the docstring and inline comment change; execution is unchanged. Rebased onto the current
python/layout.Validation: default local suite, 79 passed, 1 skipped, 4 slow tests deselected (MLX 0.30.4, mlx-lm 0.31.0). No model-checkpoint benchmark was run.