feat: harden node placement and partial model loading

docs/issues/44-tracker-shard-source-partial-download.md

@@ -86,10 +86,10 @@ What exists already (build on it, don't duplicate):
 - [ ] Two-machine test: machine A (tracker + node, holds full snapshot) serves
   layers 0–k; machine B joins with no model and receives **only** the files
   for its assigned range from A — nothing fetched from HF
-- [ ] Machine B's resident memory scales with its shard size, not model size
-- [ ] Checksums verified end-to-end; corrupted transfer falls back cleanly
+- [x] Machine B's resident memory scales with its shard size, not model size
+- [x] Checksums verified end-to-end; corrupted transfer falls back cleanly
 - [x] Single-node/full-model flows unchanged
-- [ ] `python -m pytest` passes from repo root
+- [x] `python -m pytest` passes from repo root
 
 ## Implementation notes
 
@@ -98,6 +98,13 @@ What exists already (build on it, don't duplicate):
   `full_url`; HuggingFace remains fallback-only, and when it is used the node
   computes `allow_patterns` from the repo's remote SafeTensors index so it
   stays layer-filtered even without tracker-cached files. Remaining hard half
-  is true partial model materialization: the backend can prefer a downloaded
+  is partial model materialization: the backend can prefer a downloaded
   local model directory, but Transformers still needs a `meta`-device load
   path that materializes only assigned layers.
+- 2026-07-13: Partial LOAD implemented. `_load_partial_model_from_snapshot` builds
+  on `meta` via `init_empty_weights`, materializes only layer-scoped checkpoint
+  tensors, and finalizes device placement without copying unmaterialized meta
+  weights (`_finalize_active_shard_modules_on_device`). Tests cover memory
+  scaling (`test_partial_snapshot_resident_weight_numel_scales_with_shard`)
+  and real-torch meta-vs-materialized counts. Remaining: live two-machine LAN
+  verification.