Add files using upload-large-folder tool

README.md

@@ -0,0 +1,34 @@
+---
+license: apache-2.0
+tags:
+- polca
+- loss-landscape
+- curvature
+- beetle
+---
+
+# POLCA curvature artifacts — `beetle-bilingual-l2-50-sequential-33-67-b3-fineweb-100m-nld-eng`
+
+Per-checkpoint loss-landscape data extracted during training by
+`beetlelm/src/training/polca_extractor.py`. **These are not model weights** — the trained
+checkpoints for this run are on the model repo `Beetle-FineWeb-100M/beetle-bilingual-l2-50-sequential-33-67-b3-fineweb-100m-nld-eng` as `step-<N>` branches.
+
+One file per checkpoint, `step_<N>.pt` (torch, ~28 GB each):
+
+| key | shape | meaning |
+|---|---|---|
+| `grad_basis` | `[8, n_params]` f32 | gradient-subspace basis |
+| `grad_singvals` | `[8]` f32 | corresponding singular values |
+| `hess_eigvecs` | `[32, n_params]` f32 | top Hessian eigenvectors |
+| `hess_eigvals` | `[32]` f32 | corresponding eigenvalues |
+| `param_names`, `param_shapes`, `param_count` | | flat-vector layout for the rows above |
+| `spec`, `schema_version` | | extractor configuration |
+
+Steps present: 0, 1
+
+```python
+import torch
+d = torch.load("step_0.pt", map_location="cpu", weights_only=False, mmap=True)
+d["hess_eigvals"]          # [32]
+d["hess_eigvecs"][0]       # top eigenvector, flat over param_names/param_shapes
+```

step_0.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ebb755fccdf6a07531d0542599787866c997b7130da0179a81e75d55952ea565
+size 31008654075

step_1.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fc76177dee2ef3dbabd98ad3380ee9fd52c7409577c3db5a600f2e4f6b95c375
+size 31008654075