Update FastPLMs runtime files

Add-only FastPLMs files-only publication. Checkpoint weights and complete-artifact attestations are unchanged.

README.md

@@ -18,11 +18,11 @@ Supported Transformers entry points are `AutoConfig`, `AutoModel`.
 
 ## Install and platform requirements
 
-Install FastPLMs from the exact source revision paired with this model card:
+Install the current FastPLMs package:
 
 ```bash
 python -m pip install \
-  "fastplms[structure] @ git+https://github.com/Synthyra/FastPLMs.git@ed38b898187ba16b64d801874decdddfe2938a58"
+  "fastplms[structure] @ git+https://github.com/Synthyra/FastPLMs.git"
 ```
 
 Python 3.11-3.14, PyTorch 2.13, and Transformers 5.13 are required. Structure inference requires the `structure` extra and a CUDA device for the published execution contract. The current validated release target is the exact NVIDIA GH200 on Linux aarch64; Linux x86-64, CPU-only, Windows, and macOS structure runs are not current release evidence. The Hub quick start below requires network
@@ -238,9 +238,8 @@ signals, not experimental evidence of affinity or specificity. See the
 ## Release record
 
 - FastPLMs weights: `Synthyra/ESMFold2-Experimental-Fast-Cutoff2025`
-- Runtime revision: `ed38b898187ba16b64d801874decdddfe2938a58`
-- Runtime source-tree SHA-256: `a004b0823a77112077314f3df75fbbf734aaf4e16563511facd7f74bc613f045`
-- Runtime bundle SHA-256: `f0f08be30a8100f0a817d561efb367d45e9360b9a6cc5894d0f65b029dc7e4a6`
+- Runtime revision: recorded separately in the built artifact and published commit
+- Source-tree and runtime-bundle SHA-256: recorded in `provenance.json`
 - Generator/schema version and complete/runtime-only attestations: recorded in `provenance.json`
 - Official checkpoint: `biohub/ESMFold2-Experimental-Fast-Cutoff2025`
 - Artifact source: `fast`

config.json

@@ -29,10 +29,10 @@
   "fastplms_checkpoint_repo_id": "Synthyra/ESMFold2-Experimental-Fast-Cutoff2025",
   "fastplms_checkpoint_revision": "8f022c2514a6c32692aaca078a8391d6bc6c4bac",
   "fastplms_model_id": "esmfold2_experimental_fast_cutoff2025",
-  "fastplms_release_tool_revision": "ed38b898187ba16b64d801874decdddfe2938a58",
-  "fastplms_release_tool_sha256": "6b15c1ec4a44faba83aa128452b06c617de4c4f580ca1cc91f9fce9747ca784e",
+  "fastplms_release_tool_revision": "95691a87c781f052f65b6ab3ca99dfce4ebb59c7",
+  "fastplms_release_tool_sha256": "19b4247aba04a74de5069397518374ab2c3b9e0ce18ba9f36de34f0f4c5d1ceb",
   "fastplms_runtime_bundle_sha256": "f0f08be30a8100f0a817d561efb367d45e9360b9a6cc5894d0f65b029dc7e4a6",
-  "fastplms_runtime_revision": "ed38b898187ba16b64d801874decdddfe2938a58",
+  "fastplms_runtime_revision": "95691a87c781f052f65b6ab3ca99dfce4ebb59c7",
   "fastplms_source_tree_sha256": "a004b0823a77112077314f3df75fbbf734aaf4e16563511facd7f74bc613f045",
   "fastplms_weights_revision": "8f022c2514a6c32692aaca078a8391d6bc6c4bac",
   "folding_trunk": {

runtime-attestation.json

@@ -6,9 +6,9 @@
     "LICENSES/biohub-transformers/LICENSE": "sha256:77fd4710def9ec3c0f6225800e0235f15a425abd4a8b03559127fcd782612049",
     "LICENSES/protein-ttt/LICENSE": "sha256:bb01e7d5554f9e2e117172e56551452f68a7818df7bc8e71cd7a776a1d4ba3df",
     "LICENSES/protein-ttt/PROVENANCE.md": "sha256:dc641c37353c2efd50ccbdb316ca4aae495ec02c1563e0e15bac92f75fc482e5",
-    "README.md": "sha256:1fa7ab2b77f7e975c9bac5ce53dab9bbe1d1f710dbb5d051f46799b2ffee0882",
+    "README.md": "sha256:06ffee169617fd2b0eaa4fc62452c2a33fdbc01698586fd4dcaf27a7a7b44c85",
     "THIRD_PARTY_NOTICES.md": "sha256:25704b3c76404696cae52e7fca13088d329f70f412687340351259e86cd62baa",
-    "config.json": "sha256:932d280e0a255bd0df1799f7aba70b0da15d62f64cd54e33fff097f6486b8885",
+    "config.json": "sha256:3dce1d62df3f01457f5482b1e86ff030aca8e79b29fb0b614ecf8d0e5f82e016",
     "fastplms/__init__.py": "sha256:4fb3196022ca8ec699d59d09bdbc5f0184195552b773698ab9b061fe3cd7df12",
     "fastplms/attention/__init__.py": "sha256:f60b9fecfb4bcb37a4e7c26dc2f752b9035f9cbad627b4a84213f3a92ec88f7d",
     "fastplms/attention/_core.py": "sha256:8f7ec5b65bd8b6c6fa4951d50d1c0e499abf03ae00914794b51fc410201e3e33",
@@ -69,10 +69,10 @@
   },
   "model_id": "esmfold2_experimental_fast_cutoff2025",
   "redistributable": true,
-  "release_tool_revision": "ed38b898187ba16b64d801874decdddfe2938a58",
-  "release_tool_sha256": "6b15c1ec4a44faba83aa128452b06c617de4c4f580ca1cc91f9fce9747ca784e",
+  "release_tool_revision": "95691a87c781f052f65b6ab3ca99dfce4ebb59c7",
+  "release_tool_sha256": "19b4247aba04a74de5069397518374ab2c3b9e0ce18ba9f36de34f0f4c5d1ceb",
   "runtime_bundle_sha256": "f0f08be30a8100f0a817d561efb367d45e9360b9a6cc5894d0f65b029dc7e4a6",
-  "runtime_revision": "ed38b898187ba16b64d801874decdddfe2938a58",
+  "runtime_revision": "95691a87c781f052f65b6ab3ca99dfce4ebb59c7",
   "schema_version": 2,
   "scope": "runtime-only",
   "source_tree_sha256": "a004b0823a77112077314f3df75fbbf734aaf4e16563511facd7f74bc613f045",