| { |
| "schema_version": 1, |
| "model_name": "PoreFM-RNA prototype", |
| "model_version": "0.1.0", |
| "embedding_dim": 512, |
| "chemistry": "rna004", |
| "molecule_type": "rna", |
| "class_labels": [ |
| "A", |
| "C", |
| "G", |
| "U", |
| "m6A", |
| "Inosine", |
| "2OmeA", |
| "m5C", |
| "2OmeC", |
| "2OmeG", |
| "pseU", |
| "2OmeU" |
| ], |
| "scorer": "residual-effects", |
| "artifacts": [ |
| { |
| "file": "RNA004_prototype.pt", |
| "size_bytes": 684590831, |
| "file_sha256": "79bec4aa0d52bcadf433f97ece376936a70f0cde08420b522addfd698aca2043", |
| "role": "prototype", |
| "format": "porefm_inference_bundle", |
| "bundle_format_version": 1, |
| "components": { |
| "model_path": { |
| "role": "backbone", |
| "member": "artifacts/model_path.pt", |
| "file_sha256": "8718a68ae3aad7bfbb0b27b42971a293a5f61d19c2853370d65aa146c04209ec", |
| "tensor_fingerprint": { |
| "scheme": "porefm-tensor-content-v1", |
| "sha256": "4fd8da31505651e199ed2bdf66e0a3835867812f2bd1ae2e097b3c1a0b7ef689" |
| } |
| }, |
| "effect_model": { |
| "role": "fitted_effects", |
| "member": "artifacts/effect_model.pt", |
| "file_sha256": "07827f8b0043f6547fd76b82f8f1c6068fbc90c1ad5712d9bab8dc1c7d9c0cca", |
| "tensor_fingerprint": { |
| "scheme": "porefm-tensor-content-v1", |
| "sha256": "10b966f4a6f63f589a0fe06fcb7feb71d6fc52c70c742f3b62753052e1c8d6a2" |
| } |
| } |
| } |
| } |
| ], |
| "tensor_fingerprint_scheme": { |
| "name": "porefm-tensor-content-v1", |
| "algorithm": "sha256", |
| "scope": "Stored tensors and numeric arrays, including their names, shapes and dtypes; scalar configuration and other metadata are excluded.", |
| "encoding": [ |
| "Initialize SHA-256 with ASCII porefm-tensor-content-v1 followed by one zero byte.", |
| "Recursively traverse dictionaries sorted by (type(key).__name__, str(key)), and lists/tuples in index order. Skip leaves other than torch.Tensor and numpy.ndarray.", |
| "For each leaf, compact ASCII-escaped UTF-8 JSON encodes [key_path, dtype, shape, nbytes]. key_path is the list of dictionary keys and sequence indices. dtype is torch: plus str(tensor.dtype), or numpy: plus array.dtype.str.", |
| "Append the JSON byte length as an unsigned 8-byte big-endian integer, the JSON bytes, then C-contiguous numerical bytes in little-endian representation. Tensor/array storage padding is excluded." |
| ] |
| } |
| } |
|
|
