Update FastPLMs runtime and model cards

README.md

@@ -269,7 +269,7 @@ publication requires all artifact, legal, parity, and atomic-publication checks.
 - Redistributable: `true`
 - Complete weight publication required: `false`
 
-## Validation and provenance
+## Validation and sources
 
 FastPLMs pins the checkpoint, upstream source revisions, state transformation,
 and required files in `models.toml`. Built artifacts record exact source

fastplms/models.toml

@@ -392,7 +392,7 @@ checkpoint_license = "MIT"
 hub_license = "mit"
 weights_publication_allowed = true
 state_transform = "identity"
-conversion_provenance = "Input: each pinned Biohub ESMFold2 checkpoint and its separately pinned ESMC checkpoint. Transformation: apply identity to preserve the folding checkpoint exactly, load its parameters in FP32 for CUDA BF16-autocast execution, retain canonical BF16 ESMC weights, and optionally rebuild exactly 80 ESMC attention output projections as transient Transformer Engine linears. Output: the corresponding pinned Synthyra ESMFold2 checkpoint plus its declared ESMC precision policy. Validation: release parity covers exact canonical state, learned projection, prepared features, and seeded BF16 folding; experimental FP8 validation covers strict unavailable-device behavior, all four variants, and three BF16-to-FP8 reload cycles on the standard variant. Limitation: only the four manifest-listed ESMFold2 variants are supported; FP8 is experimental, applies only to inference-time ESMC execution, and requires direct CUDA loading with Transformer Engine availability."
+conversion_provenance = "Input: each pinned Biohub ESMFold2 checkpoint and its separately pinned ESMC checkpoint. Transformation: apply identity to preserve the folding checkpoint exactly, load its parameters in FP32 for CUDA BF16-autocast execution, retain canonical BF16 ESMC weights, and optionally rebuild exactly 80 ESMC attention output projections as transient Transformer Engine linears. Output: the corresponding pinned Synthyra ESMFold2 checkpoint plus its declared ESMC precision policy. Validation: release parity covers exact canonical state, learned projection, prepared features, and seeded BF16 folding; experimental FP8 validation covers strict unavailable-device behavior, the four 6B-backbone variants, and three BF16-to-FP8 reload cycles on the standard variant. Limitation: only the six manifest-listed ESMFold2 variants are supported; FP8 is experimental, applies only to inference-time ESMC execution, and requires direct CUDA loading with Transformer Engine availability."
 representative = "esmfold2"
 documentation = "docs/esmfold2.md"
 test_tiers = ["check", "compliance", "structure", "feature", "artifact", "benchmark"]
@@ -1237,3 +1237,39 @@ official_files = [
   "model.safetensors=sha256:4e903b740ad6ad704ec60881bfd593e0d6c874a630ffa0f0838276e0b665088f",
 ]
 auto_map = { AutoConfig = "fastplms.models.esmfold2.configuration_esmfold2.ESMFold2Config", AutoModel = "fastplms.models.esmfold2.modeling_esmfold2_experimental.ESMFold2ExperimentalModel", AutoModelForSequenceClassification = "fastplms.models.esmfold2.modeling_esmfold2_classification.ESMFold2ExperimentalForSequenceClassification", AutoModelForTokenClassification = "fastplms.models.esmfold2.modeling_esmfold2_classification.ESMFold2ExperimentalForTokenClassification" }
+
+[[models]]
+id = "esmfold2_300"
+family = "esmfold2"
+size_category = "structure"
+generation_contract = "not_applicable"
+msa_conditioning = false
+publication_status = "published"
+fast_repo = "Synthyra/ESMFold2-300"
+fast_revision = "a38a62ae930d157484b331c2bf4241684573adba"
+fast_files = ["config.json=git-sha1:47ec20cf8b234c3b41d6f3ae1bdfe95d4eb4849e", "model.safetensors=sha256:44d6797c5efebf24753d502b40950e0874871c96ceea14f2d7f7e39cebac67fd"]
+official_repo = "biohub/ESMFold2-Experimental-Fast-base300M-step1500k"
+official_revision = "21531e59002c9205284715e28ee802dafb430637"
+official_files = ["config.json=git-sha1:8c9a04fe22b0e5fca77bc4e2861a12c9494ef4d4", "model.safetensors=sha256:44d6797c5efebf24753d502b40950e0874871c96ceea14f2d7f7e39cebac67fd"]
+notes = "Experimental Fast checkpoint with a frozen 300M ESM++ backbone, tensor-exact in BF16 with the pinned step-1500000 source, 24 folding blocks, no MSA conditioning, and no confidence head. BF16 execution uses FP32 folding parameters with CUDA autocast. FP8 is unsupported. Docker BF16 inference validation passed on the compact Protein G case."
+auto_map = { AutoConfig = "fastplms.models.esmfold2.configuration_esmfold2.ESMFold2Config", AutoModel = "fastplms.models.esmfold2.modeling_esmfold2_experimental.ESMFold2ExperimentalModel", AutoModelForSequenceClassification = "fastplms.models.esmfold2.modeling_esmfold2_classification.ESMFold2ExperimentalForSequenceClassification", AutoModelForTokenClassification = "fastplms.models.esmfold2.modeling_esmfold2_classification.ESMFold2ExperimentalForTokenClassification" }
+backbone_model = "esmc_small"
+backbone = { repo = "biohub/ESMC-300M-1500000", revision = "56803b6378b82e16c3b24aac49d1fce4445540b7", files = ["config.json=git-sha1:7fe728a0eb3fb81b24491d6cc1de816bf7797c27", "model.safetensors=sha256:8bd6cacf9b5a92d51954b64b20407f1f9f564a7e4849b8663470784d2a8b7ed2", "tokenizer.json=git-sha1:81c797f56768b22dec0301fa771f018b7e43e98c", "tokenizer_config.json=git-sha1:f49f57b24a1c93bd544974811e8ecbd61b7fae89", "special_tokens_map.json=git-sha1:c907ee1dc19b24241749b32d665c291c7e6e8e4b"] }
+
+[[models]]
+id = "esmfold2_600"
+family = "esmfold2"
+size_category = "structure"
+generation_contract = "not_applicable"
+msa_conditioning = false
+publication_status = "published"
+fast_repo = "Synthyra/ESMFold2-600"
+fast_revision = "71c67d0b2b73dc245ea7c3cc0d0476439a882d08"
+fast_files = ["config.json=git-sha1:8e271837cbdada96c4974c8e543f84065e0f06f1", "model.safetensors=sha256:11a53c1b4700b6c62a5a464fc3ec7076160c19e8584157f88e13275116cfd602"]
+official_repo = "biohub/ESMFold2-Experimental-Fast-base600M-step1500k"
+official_revision = "15cf2d6648692f6c17cee1297d8a285476fffa9b"
+official_files = ["config.json=git-sha1:95517e555f17a1eca4b68866c89033a1f6916a5d", "model.safetensors=sha256:11a53c1b4700b6c62a5a464fc3ec7076160c19e8584157f88e13275116cfd602"]
+notes = "Experimental Fast checkpoint with a frozen 600M ESM++ backbone, tensor-exact in BF16 with the pinned step-1500000 source, 24 folding blocks, no MSA conditioning, and no confidence head. BF16 execution uses FP32 folding parameters with CUDA autocast. FP8 is unsupported. Configuration, weight identities, and artifact reload are verified; this model is not inference-validated."
+auto_map = { AutoConfig = "fastplms.models.esmfold2.configuration_esmfold2.ESMFold2Config", AutoModel = "fastplms.models.esmfold2.modeling_esmfold2_experimental.ESMFold2ExperimentalModel", AutoModelForSequenceClassification = "fastplms.models.esmfold2.modeling_esmfold2_classification.ESMFold2ExperimentalForSequenceClassification", AutoModelForTokenClassification = "fastplms.models.esmfold2.modeling_esmfold2_classification.ESMFold2ExperimentalForTokenClassification" }
+backbone_model = "esmc_large"
+backbone = { repo = "biohub/ESMC-600M-1500000", revision = "21af9cc429af76ebda6c48074fb624db4735aaaf", files = ["config.json=git-sha1:ec29f6009b21d710f64bf1c058f3a9710833d692", "model.safetensors=sha256:d6869f5ae0f11e5dc829b195e062e87cfcc2f851a08a5edbaf5d1083ae7f76cc", "tokenizer.json=git-sha1:81c797f56768b22dec0301fa771f018b7e43e98c", "tokenizer_config.json=git-sha1:f49f57b24a1c93bd544974811e8ecbd61b7fae89", "special_tokens_map.json=git-sha1:c907ee1dc19b24241749b32d665c291c7e6e8e4b"] }

modeling_fastplms.py

@@ -13,7 +13,7 @@ from zipfile import ZIP_DEFLATED, ZipFile
 
 from .fastplms_bundle import RUNTIME_DATA, RUNTIME_HASH
 
-if RUNTIME_HASH != "1d1e93d324d699f48e94cf425f436d33b1b5e76ce96735a80a0dbb9727d5a26f":
+if RUNTIME_HASH != "9f3d9a8dfcef0d7bd216a50f5b224acb7b7f38c0c3c82ff238d8f1a11c0fec43":
     raise RuntimeError("FastPLMs runtime identity differs from the bridge.")
 
 _RUNTIME_TEMPORARIES = []