Sync current FastPLMs source, model cards and dependencies

fastplms/models.toml

@@ -1254,36 +1254,38 @@ auto_map = { AutoConfig = "fastplms.models.esmfold2.configuration_esmfold2.ESMFo
 
 [[models]]
 id = "esmfold2_300"
+confidence_adaptation = { release = "v1", head_sha256 = "40fd7f3d82fcefe8ad20ab2b32a37a68a84b54a527a4bce6eb9437bad2b77e31", base_weight_sha256 = "44d6797c5efebf24753d502b40950e0874871c96ceea14f2d7f7e39cebac67fd", donor_repo = "biohub/ESMFold2-Experimental-Fast-Cutoff2025", donor_revision = "74b88548bf19688b8727432db0d698cb2e1d8783", donor_weight_sha256 = "4e903b740ad6ad704ec60881bfd593e0d6c874a630ffa0f0838276e0b665088f", training_url = "https://wandb.ai/lhallee/fastplms-confidence/runs/9558b6d23daf", evaluation_url = "https://huggingface.co/datasets/Synthyra/FastPLMs-artifacts/tree/309f353b07e0e46de4d77a5266d4eddd695538e3/confidence-v2/v2-reproduction-20260922/public/evaluation/esmfold2_300", evidence_path = "docs/evidence/confidence/esmfold2_300-v1.json", frozen_base = { repo = "Synthyra/ESMFold2-300", revision = "a38a62ae930d157484b331c2bf4241684573adba", files = ["config.json=git-sha1:47ec20cf8b234c3b41d6f3ae1bdfe95d4eb4849e", "model.safetensors=sha256:44d6797c5efebf24753d502b40950e0874871c96ceea14f2d7f7e39cebac67fd"] } }
 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"]
+fast_revision = "960fd2538ef8a5690473cc6ee44bbd74cce78d91"
+fast_files = ["config.json=git-sha1:e1e346a9dd66e9658e4f13cb815777d13eb944cc", "model.safetensors=sha256:bb2eaf6389e1d58f5117ca3173a5ee73d73548cda5c89f95f85c4a2280ad0e7e"]
 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."
+notes = "Experimental Fast model with a frozen 300M ESM++ backbone, 24 folding blocks, no MSA conditioning, and a Synthyra-trained confidence head enabled by default. BF16 execution uses FP32 folding parameters with CUDA autocast; FP8 is unsupported. Confidence evaluation does not establish full structure-model equivalence to production ESMFold2."
 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"
+confidence_adaptation = { release = "v1", head_sha256 = "e84726a050722e1b722712c87d17a5388bd3699e2520e4a59abb1d828dfb8de7", base_weight_sha256 = "11a53c1b4700b6c62a5a464fc3ec7076160c19e8584157f88e13275116cfd602", donor_repo = "biohub/ESMFold2-Experimental-Fast-Cutoff2025", donor_revision = "74b88548bf19688b8727432db0d698cb2e1d8783", donor_weight_sha256 = "4e903b740ad6ad704ec60881bfd593e0d6c874a630ffa0f0838276e0b665088f", training_url = "https://wandb.ai/lhallee/fastplms-confidence/runs/820d2cfa56c0", evaluation_url = "https://huggingface.co/datasets/Synthyra/FastPLMs-artifacts/tree/6e62186cd36b9047cc4691980076be9f76482192/confidence-v2/v2-reproduction-20260922/public/evaluation/esmfold2_600", evidence_path = "docs/evidence/confidence/esmfold2_600-v1.json", frozen_base = { repo = "Synthyra/ESMFold2-600", revision = "71c67d0b2b73dc245ea7c3cc0d0476439a882d08", files = ["config.json=git-sha1:8e271837cbdada96c4974c8e543f84065e0f06f1", "model.safetensors=sha256:11a53c1b4700b6c62a5a464fc3ec7076160c19e8584157f88e13275116cfd602"] } }
 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"]
+fast_revision = "639d1ed9673a7a139a58c2813a22fc2ff8b61a9b"
+fast_files = ["config.json=git-sha1:8f2dbe39b6aeddb39dc17e78cc6ee6588960757a", "model.safetensors=sha256:4e3baff4574120dfa58a25f34c910a05943e343ce63ad268775d2ce33b3b8c64"]
 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."
+notes = "Experimental Fast model with a frozen 600M ESM++ backbone, 24 folding blocks, no MSA conditioning, and a Synthyra-trained confidence head enabled by default. BF16 execution uses FP32 folding parameters with CUDA autocast; FP8 is unsupported. Confidence evaluation does not establish full structure-model equivalence to production ESMFold2."
 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"] }

fastplms/registry.py

@@ -1344,6 +1344,9 @@ def _parse_confidence_adaptation(
             or weight.digest != digests["base_weight_sha256"]
         ):
             raise RegistryError(f"{base_context} model.safetensors must match base_weight_sha256.")
+        config = frozen_base.file_map.get("config.json")
+        if config is None or config.algorithm != "git-sha1":
+            raise RegistryError(f"{base_context} must pin config.json with git-sha1.")
     if release == "v1" and frozen_base is None:
         raise RegistryError(f"{adaptation_context}.frozen_base is required for v1.")
     donor_repo = _require_str(raw, "donor_repo", adaptation_context)

modeling_fastplms.py

@@ -13,7 +13,7 @@ from zipfile import ZIP_DEFLATED, ZipFile
 
 from .fastplms_bundle import RUNTIME_DATA, RUNTIME_HASH
 
-if RUNTIME_HASH != "392fc34c758ca8a8c39f1eaa4f7ecbbaacc498758b96a4b7a2341bf8d5082cb6":
+if RUNTIME_HASH != "095a1c0d07c0815531c1129036e277944e4f7485c85892af0083cf3d57b79894":
     raise RuntimeError("FastPLMs runtime identity differs from the bridge.")
 
 _RUNTIME_TEMPORARIES = []