README: drop project-specific references

README.md

@@ -16,7 +16,7 @@ size_categories:
 Precomputed [UMT5-XXL](https://huggingface.co/google/umt5-xxl) text embeddings for the
 **1,039,891 unique task prompts** of the RoboTwin 2.0 3D dataset
 ([`flex-pi/robotwin_3d`](https://huggingface.co/datasets/flex-pi/robotwin_3d)), as consumed by
-Wan2.2-TI2V-5B / FastWAM. Precomputing these costs substantial GPU time; this cache skips it.
+Wan2.2-TI2V-5B. Precomputing these costs substantial GPU time; this cache skips it.
 
 ## Cache key
 
@@ -50,13 +50,12 @@ keys in row order):
 | `masks` | `[N, 128]` | `bool` |
 
 `masks` marks valid tokens (mean 40.9 of 128). Values past the mask are raw T5 outputs rather than
-zeros. FastWAM zeroes them on load (`context[~mask] = 0`); do the same if your model does not apply
-the mask.
+zeros. Zero them on load (`context[~mask] = 0`) if your model does not apply the mask.
 
 ## Converting back to the per-file `.pt` layout
 
-FastWAM reads a flat directory of `{key}.t5_len128.wan22ti2v5b.pt` files, each a `{"context", "mask"}`
-dict, rather than shards. This script reconstructs that layout:
+The training pipeline reads a flat directory of `{key}.t5_len128.wan22ti2v5b.pt` files, each a
+`{"context", "mask"}` dict, rather than shards. This script reconstructs that layout:
 
 ```python
 # unpack_to_pt.py -- rebuild the flat .pt cache from the sharded release.