Upload NexaMass V3 safetensors weights and release metadata
Browse files- README.md +7 -4
- config.json +63 -0
- configs/foundation_config.json +98 -0
- configs/runtime_requirements.json +21 -0
- configs/structure_alignment_config.json +162 -0
- metadata/Final_V3_manifest.json +10 -0
- metadata/archive/20260413T201817Z/latest_checkpoint.txt +1 -0
- metadata/archive/20260413T202042Z/latest_checkpoint.txt +1 -0
- metadata/archive/20260413T213902Z/day1_validation_report.json +2475 -0
- metadata/archive/20260413T213902Z/latest_checkpoint.txt +1 -0
- metadata/archive/20260413T213902Z/latest_metrics.json +169 -0
- metadata/archive/20260413T213902Z/perf_summary.json +16 -0
- metadata/archive/20260413T213902Z/results.md +171 -0
- metadata/checksums.sha256 +29 -0
- metadata/closeout_receipt.json +9 -0
- metadata/day1_validation_report.json +2475 -0
- metadata/eval_coverage.json +46 -0
- metadata/fast_eval_surface.json +14 -0
- metadata/latest_checkpoint.txt +1 -0
- metadata/latest_metrics.json +169 -0
- metadata/perf_summary.json +16 -0
- metadata/results.md +171 -0
- metadata/training_surface.json +68 -0
- model_index.json +48 -0
- runtime/README.md +14 -0
- runtime/nexamass_encoder.py +139 -0
- weights/Final_V3-model_state.pt +3 -0
- weights/Final_V3-model_state.safetensors +3 -0
- weights/NexaMass-V3-Struct-model_state.pt +3 -0
- weights/NexaMass-V3-Struct-model_state.safetensors +3 -0
README.md
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@@ -30,7 +30,7 @@ The model was developed as part of the Nexa MS/MS pipeline. The foundation check
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## Model Summary
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`NexaMass-V3-Struct` is an encoder-only transformer with
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The model contains a spectral transformer backbone, an SSL projection head, a Morgan fingerprint structure head, a spectrum-side retrieval query projection, a fingerprint-side target projection, and experimental retrieval/reranking heads. The strongest current inference surface is the Morgan fingerprint prediction and candidate-bank decode path. The trained retrieval projection is included for research, but it is not promoted as a reliable final decision layer.
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Do not use this model as an unrestricted de novo structure generator. Do not treat the top-ranked candidate as a production-grade molecular identification without external validation. Do not treat the raw confidence or score gap as calibrated probability. Candidate outputs should be validated with chemistry-aware checks such as RDKit sanitization, formula and exact-mass agreement, precursor/adduct compatibility, fingerprint similarity, and spectrum-to-candidate consistency.
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## Inference Contract
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The recommended inference path is spectrum-to-embedding plus candidate-bank decoding. A spectrum is converted into a normalized embedding and a `2048`-dimensional Morgan fingerprint probability vector. That vector is compared against candidate molecular fingerprints from a known bank. The output should be a ranked shortlist with validation metadata, not a single unqualified structure claim.
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## Evaluation
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The structure-aligned checkpoint reached validation structure BCE `0.0653`, validation fingerprint cosine `0.4255`, and embedding standard deviation `0.0722` in the V26 alignment run. A candidate-bank structure gallery over `20` displayed validation examples
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On a MassSpecGym retrieval adapter using the frozen V3 projected-dot scorer, the model reached validation Hit@1 `0.1162`, Hit@5 `0.1915`, and Hit@20 `0.3328`. On the MassSpecGym test dataloader under Hit@k-only evaluation, it reached Hit@1 `0.0627`, Hit@5 `0.1753`, and Hit@20 `0.3505`. This places the model above lower MassSpecGym retrieval baselines such as Random, DeepSets, Fingerprint FFN, and DeepSets with Fourier features, but below specialized retrieval systems such as MIST. The result should be interpreted as evidence of useful top-k narrowing, not leaderboard-grade retrieval.
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Internal larger-bank pressure tests also support a bounded interpretation. On a roughly `29k` unique-identity candidate bank, the frozen projected-dot scorer beat random and compressed candidate rank substantially, but exact local discrimination remained weak. The best current read is that the model contains real structure and retrieval signal, while top-1 ranking and calibrated confidence require additional downstream machinery.
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## Citation
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If you use this model, cite the NexaMass project release and the accompanying technical report
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## Recommended Name
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## Model Summary
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`NexaMass-V3-Struct` is an encoder-only transformer with `14,106,690` trainable parameters. The full training checkpoint is approximately `153 MB` in object storage, while the public model-state weights are approximately `54 MB` per checkpoint and are provided in both PyTorch and Safetensors formats, making the released model substantially smaller than many public MS/MS representation or chemistry foundation checkpoints. The model uses `256` peak tokens, an input feature width of `8`, hidden dimension `384`, `6` transformer layers, `8` attention heads, feed-forward dimension `1536`, dropout `0.1`, SSL projection dimension `192`, RDKit Morgan fingerprint dimension `2048`, target projection dimension `256`, and retrieval MLP hidden dimension `512`.
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The model contains a spectral transformer backbone, an SSL projection head, a Morgan fingerprint structure head, a spectrum-side retrieval query projection, a fingerprint-side target projection, and experimental retrieval/reranking heads. The strongest current inference surface is the Morgan fingerprint prediction and candidate-bank decode path. The trained retrieval projection is included for research, but it is not promoted as a reliable final decision layer.
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Do not use this model as an unrestricted de novo structure generator. Do not treat the top-ranked candidate as a production-grade molecular identification without external validation. Do not treat the raw confidence or score gap as calibrated probability. Candidate outputs should be validated with chemistry-aware checks such as RDKit sanitization, formula and exact-mass agreement, precursor/adduct compatibility, fingerprint similarity, and spectrum-to-candidate consistency.
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The preferred public inference files are `weights/NexaMass-V3-Struct-model_state.safetensors` and `weights/Final_V3-model_state.safetensors`. PyTorch model-state `.pt` fallbacks are included for compatibility. Optimizer-bearing full training checkpoints are retained in Wasabi/object storage and are not part of the public Hugging Face payload.
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## Inference Contract
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The recommended inference path is spectrum-to-embedding plus candidate-bank decoding. A spectrum is converted into a normalized embedding and a `2048`-dimensional Morgan fingerprint probability vector. That vector is compared against candidate molecular fingerprints from a known bank. The output should be a ranked shortlist with validation metadata, not a single unqualified structure claim.
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## Evaluation
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The structure-aligned checkpoint reached validation structure BCE `0.0653`, validation fingerprint cosine `0.4255`, and embedding standard deviation `0.0722` in the V26 alignment run. A candidate-bank structure gallery over `20` displayed validation examples matched the ground-truth candidate identity in `11` cases through the fingerprint decode path. The same gallery matched `0` exact identities through the trained retrieval projection, which is why the card treats ranking as an open decision-layer problem rather than a solved property.
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On a MassSpecGym retrieval adapter using the frozen V3 projected-dot scorer, the model reached validation Hit@1 `0.1162`, Hit@5 `0.1915`, and Hit@20 `0.3328`. On the MassSpecGym test dataloader under Hit@k-only evaluation, it reached Hit@1 `0.0627`, Hit@5 `0.1753`, and Hit@20 `0.3505`. This Hit@20-only adapter comparison places the model above lower MassSpecGym retrieval baselines such as Random, DeepSets, Fingerprint FFN, and DeepSets with Fourier features, but below specialized retrieval systems such as MIST. The result should be interpreted as evidence of useful top-k narrowing, not leaderboard-grade retrieval.
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Internal larger-bank pressure tests also support a bounded interpretation. On a roughly `29k` unique-identity candidate bank, the frozen projected-dot scorer beat random and compressed candidate rank substantially, but exact local discrimination remained weak. The best current read is that the model contains real structure and retrieval signal, while top-1 ranking and calibrated confidence require additional downstream machinery.
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## Citation
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If you use this model, cite the NexaMass project release and the accompanying technical report when available. Relevant background work includes DreaMS for self-supervised MS/MS representation learning, MassSpecGym for benchmark framing, CSI:FingerID for fingerprint-mediated candidate search, and related spectra-structure retrieval and de novo generation systems such as MIST, MSNovelist, CMSSP, CSU-MS2, MSBERT, Spec2Mol, and MS2Mol.
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## Recommended Name
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config.json
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{
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"architecture_config": {
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"collision_max": 200.0,
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"dropout": 0.1,
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"fingerprint_dim": 2048,
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"heads": 8,
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"layers": 6,
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"max_peaks": 256,
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"metadata_scale": 0.02,
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"model_dim": 384,
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"mz_max": 2000.0,
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"projection_dim": 192,
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"retrieval_mlp_hidden_dim": 512,
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"target_projection_dim": 256
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},
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"architectures": [
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"NexaMassSpectralEncoder"
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],
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"foundation_checkpoint": "weights/Final_V3-model_state.safetensors",
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"foundation_checkpoint_format": "safetensors",
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"foundation_pytorch_model_state_checkpoint": "weights/Final_V3-model_state.pt",
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"full_training_checkpoints": {
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"location": "Wasabi object storage",
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"note": "Optimizer-bearing full training checkpoints are intentionally not part of the public Hugging Face payload; the public release contains model-state weights only."
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},
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"input_contract": {
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"feature_width": 8,
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"mask": "boolean valid-peak mask, shape [batch, max_peaks]",
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"max_peaks": 256,
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"metadata_embeddings": [
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"adduct_id",
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"instrument_id"
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],
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"per_peak_features": [
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"mz",
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"intensity",
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"mz_to_precursor",
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"peak_rank",
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"precursor_mz",
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"charge",
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"collision_energy",
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"peak_count"
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]
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},
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"library_name": "pytorch",
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"model_type": "nexamass",
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"not_supported": [
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"unrestricted de novo molecule generation",
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"calibrated top-1 molecular identification without external validation"
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],
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"outputs": {
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"morgan_fingerprint_logits": 2048,
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"retrieval_target_dim": 256,
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"ssl_projection_dim": 192,
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"structure_query_dim": 256
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},
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"parameter_count": 14106690,
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"primary_checkpoint": "weights/NexaMass-V3-Struct-model_state.safetensors",
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"primary_checkpoint_format": "safetensors",
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"pytorch_model_state_checkpoint": "weights/NexaMass-V3-Struct-model_state.pt",
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"recommended_inference": "Use the structure-aligned model-state checkpoint for spectrum embeddings and Morgan fingerprint probability vectors, then compare against a candidate molecular fingerprint bank.",
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"task": "MS/MS spectral representation learning and RDKit Morgan fingerprint alignment"
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}
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configs/foundation_config.json
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{
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"checkpoint": {
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"bytes": 151419519,
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"config": {
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"collision_max": 200.0,
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"dropout": 0.1,
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"fingerprint_dim": 2048,
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"heads": 8,
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"layers": 6,
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"max_peaks": 256,
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"metadata_scale": 0.02,
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"model_dim": 384,
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"mz_max": 2000.0,
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"projection_dim": 192,
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"retrieval_mlp_hidden_dim": 512,
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"target_projection_dim": 256
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},
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"created_at": "2026-04-13T19:01:18Z",
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"file": "weights/Final_V3.pt",
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"metrics_keys": [
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"backbone_lr",
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"contrastive_loss",
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"covariance_loss",
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"covariance_to_contrastive_ratio",
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"covariance_weight",
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"dataloader_time_sec",
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"dataloader_wait_pct_300s",
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"dataloader_wait_pct_60s",
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"dataloader_wait_pct_900s",
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"dataloader_wait_pct_run",
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"distributed_rank",
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"distributed_world_size",
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"effective_batch_size",
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"effective_hard_negative_scale",
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"effective_retrieval_weight",
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"effective_structure_bce_weight",
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"embedding_std",
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"global_batch_size",
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| 39 |
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"gpu_active_fraction",
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"gpu_count_observed",
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"gpu_max_memory_gb",
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"gpu_mem_used_mean_gb",
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"gpu_mem_util_mean_pct",
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"gpu_memory_utilization",
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"gpu_snapshot_available",
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| 46 |
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"gpu_util_max_pct",
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| 47 |
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"gpu_util_mean_pct",
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"gpu_util_min_pct",
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| 49 |
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"grad_norm",
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"local_batch_size",
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"lr",
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"mfu_estimate",
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| 53 |
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"msms_objective",
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| 54 |
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"optimizer_step",
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| 55 |
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"regularizer_ramp",
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| 56 |
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"resumed_from_checkpoint",
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| 57 |
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"retrieval_head_lr",
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| 58 |
+
"samples_per_sec",
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| 59 |
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"samples_per_sec_300s",
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| 60 |
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"samples_per_sec_60s",
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| 61 |
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"samples_per_sec_900s",
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| 62 |
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"samples_per_sec_run_mean",
|
| 63 |
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"step_time_cv_300s",
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| 64 |
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"step_time_cv_60s",
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| 65 |
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"step_time_cv_900s",
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| 66 |
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"step_time_sec",
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| 67 |
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"structure_bce_locked",
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| 68 |
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"structure_bce_loss",
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| 69 |
+
"structure_fingerprint_cosine",
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| 70 |
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"structure_head_lr",
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| 71 |
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"structure_retrieval_loss",
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| 72 |
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"structure_retrieval_skipped",
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| 73 |
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"structure_target_backend",
|
| 74 |
+
"structure_to_ssl_ratio",
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| 75 |
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"train_loss",
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| 76 |
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"train_loss_ema",
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| 77 |
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"train_retrieval_bank_size",
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| 78 |
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"variance_loss",
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| 79 |
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"variance_weight",
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| 80 |
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"weighted_covariance_loss",
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| 81 |
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"weighted_regularizer_to_contrastive_ratio",
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| 82 |
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"weighted_ssl_loss",
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| 83 |
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"weighted_structure_bce_loss",
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| 84 |
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"weighted_structure_retrieval_loss",
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| 85 |
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"weighted_variance_loss"
|
| 86 |
+
],
|
| 87 |
+
"model_state_bytes": 56473018,
|
| 88 |
+
"model_state_file": "weights/Final_V3-model_state.pt",
|
| 89 |
+
"param_count": 14106690,
|
| 90 |
+
"state_tensors": 103,
|
| 91 |
+
"step": 81001
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| 92 |
+
},
|
| 93 |
+
"dataset": "GeMS-derived deterministic Parquet shard campaign",
|
| 94 |
+
"name": "NexaMass-V3 foundation checkpoint",
|
| 95 |
+
"objective_summary": "self-supervised spectral contrastive representation learning with structure-facing foundation instrumentation",
|
| 96 |
+
"phase": "self_supervised_ssl_structure_foundation",
|
| 97 |
+
"processed_samples_approx": 201000000
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}
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configs/runtime_requirements.json
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|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"core_packages": [
|
| 3 |
+
"torch",
|
| 4 |
+
"numpy",
|
| 5 |
+
"pyarrow",
|
| 6 |
+
"rdkit"
|
| 7 |
+
],
|
| 8 |
+
"framework": "PyTorch",
|
| 9 |
+
"load_example": [
|
| 10 |
+
"payload = torch.load(\"weights/NexaMass-V3-Struct-model_state.pt\", map_location=\"cpu\")",
|
| 11 |
+
"cfg = ModelConfig(**payload[\"args-derived config or configs/config.json\"])",
|
| 12 |
+
"model = SpectralEncoder(cfg)",
|
| 13 |
+
"model.load_state_dict(payload[\"model_state\"], strict=True)"
|
| 14 |
+
],
|
| 15 |
+
"optional_packages": [
|
| 16 |
+
"qdrant-client",
|
| 17 |
+
"huggingface-hub"
|
| 18 |
+
],
|
| 19 |
+
"python": ">=3.11",
|
| 20 |
+
"reference_runtime": "Nexa_compute scripts.msms.inspect_msms_checkpoint_structure.SpectralEncoder"
|
| 21 |
+
}
|
configs/structure_alignment_config.json
ADDED
|
@@ -0,0 +1,162 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
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|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"checkpoint": {
|
| 3 |
+
"bytes": 160482975,
|
| 4 |
+
"config": {
|
| 5 |
+
"collision_max": 200.0,
|
| 6 |
+
"dropout": 0.1,
|
| 7 |
+
"fingerprint_dim": 2048,
|
| 8 |
+
"heads": 8,
|
| 9 |
+
"layers": 6,
|
| 10 |
+
"max_peaks": 256,
|
| 11 |
+
"metadata_scale": 0.02,
|
| 12 |
+
"model_dim": 384,
|
| 13 |
+
"mz_max": 2000.0,
|
| 14 |
+
"projection_dim": 192,
|
| 15 |
+
"retrieval_mlp_hidden_dim": 512,
|
| 16 |
+
"target_projection_dim": 256
|
| 17 |
+
},
|
| 18 |
+
"created_at": "2026-04-13T21:34:38Z",
|
| 19 |
+
"file": "weights/NexaMass-V3-Struct.pt",
|
| 20 |
+
"metrics_keys": [
|
| 21 |
+
"backbone_lr",
|
| 22 |
+
"contrastive_loss",
|
| 23 |
+
"covariance_loss",
|
| 24 |
+
"covariance_to_contrastive_ratio",
|
| 25 |
+
"covariance_weight",
|
| 26 |
+
"dataloader_time_sec",
|
| 27 |
+
"dataloader_wait_pct_300s",
|
| 28 |
+
"dataloader_wait_pct_60s",
|
| 29 |
+
"dataloader_wait_pct_900s",
|
| 30 |
+
"dataloader_wait_pct_run",
|
| 31 |
+
"distributed_rank",
|
| 32 |
+
"distributed_world_size",
|
| 33 |
+
"effective_batch_size",
|
| 34 |
+
"effective_hard_negative_scale",
|
| 35 |
+
"effective_retrieval_weight",
|
| 36 |
+
"effective_structure_bce_weight",
|
| 37 |
+
"embedding_std",
|
| 38 |
+
"global_batch_size",
|
| 39 |
+
"gpu_active_fraction",
|
| 40 |
+
"gpu_count_observed",
|
| 41 |
+
"gpu_max_memory_gb",
|
| 42 |
+
"gpu_mem_used_mean_gb",
|
| 43 |
+
"gpu_mem_util_mean_pct",
|
| 44 |
+
"gpu_memory_utilization",
|
| 45 |
+
"gpu_snapshot_available",
|
| 46 |
+
"gpu_util_max_pct",
|
| 47 |
+
"gpu_util_mean_pct",
|
| 48 |
+
"gpu_util_min_pct",
|
| 49 |
+
"grad_norm",
|
| 50 |
+
"local_batch_size",
|
| 51 |
+
"lr",
|
| 52 |
+
"mfu_estimate",
|
| 53 |
+
"msms_objective",
|
| 54 |
+
"optimizer_step",
|
| 55 |
+
"regularizer_ramp",
|
| 56 |
+
"resumed_from_checkpoint",
|
| 57 |
+
"retrieval_head_lr",
|
| 58 |
+
"samples_per_sec",
|
| 59 |
+
"samples_per_sec_300s",
|
| 60 |
+
"samples_per_sec_60s",
|
| 61 |
+
"samples_per_sec_900s",
|
| 62 |
+
"samples_per_sec_run_mean",
|
| 63 |
+
"step_time_cv_300s",
|
| 64 |
+
"step_time_cv_60s",
|
| 65 |
+
"step_time_cv_900s",
|
| 66 |
+
"step_time_sec",
|
| 67 |
+
"structure_bce_locked",
|
| 68 |
+
"structure_bce_loss",
|
| 69 |
+
"structure_candidate_count",
|
| 70 |
+
"structure_clear_fraction",
|
| 71 |
+
"structure_clear_hit_at_1",
|
| 72 |
+
"structure_close_fraction",
|
| 73 |
+
"structure_close_hit_at_1",
|
| 74 |
+
"structure_confidence_bucket_score_mean",
|
| 75 |
+
"structure_confidence_bucket_score_mode",
|
| 76 |
+
"structure_confidence_candidate_count",
|
| 77 |
+
"structure_confidence_clear_threshold",
|
| 78 |
+
"structure_confidence_close_threshold",
|
| 79 |
+
"structure_confidence_eval_key_mode",
|
| 80 |
+
"structure_confidence_separation",
|
| 81 |
+
"structure_correct_logit_mean",
|
| 82 |
+
"structure_fingerprint_cosine",
|
| 83 |
+
"structure_hard_negative_bonus_applied",
|
| 84 |
+
"structure_hard_negative_bonus_mean",
|
| 85 |
+
"structure_hard_negative_scale",
|
| 86 |
+
"structure_head_lr",
|
| 87 |
+
"structure_hit_at_1",
|
| 88 |
+
"structure_hit_at_20",
|
| 89 |
+
"structure_hit_at_5",
|
| 90 |
+
"structure_local_ce_loss",
|
| 91 |
+
"structure_local_ce_loss_weight",
|
| 92 |
+
"structure_local_rerank_loss",
|
| 93 |
+
"structure_local_rerank_loss_weight",
|
| 94 |
+
"structure_local_rerank_score_scale",
|
| 95 |
+
"structure_logit_margin_mean",
|
| 96 |
+
"structure_margin_loss",
|
| 97 |
+
"structure_margin_loss_weight",
|
| 98 |
+
"structure_missing_formula_fraction",
|
| 99 |
+
"structure_missing_inchikey_fraction",
|
| 100 |
+
"structure_pairwise_logistic_loss",
|
| 101 |
+
"structure_pairwise_logistic_loss_weight",
|
| 102 |
+
"structure_positive_count_mean",
|
| 103 |
+
"structure_post_label_candidate_count",
|
| 104 |
+
"structure_post_precursor_candidate_count",
|
| 105 |
+
"structure_prefilter_candidate_count",
|
| 106 |
+
"structure_retrieval_logit_std",
|
| 107 |
+
"structure_retrieval_loss",
|
| 108 |
+
"structure_retrieval_scorer",
|
| 109 |
+
"structure_retrieval_target_mode",
|
| 110 |
+
"structure_skipped_query_fraction",
|
| 111 |
+
"structure_soft_target_exact_mix",
|
| 112 |
+
"structure_soft_target_loss",
|
| 113 |
+
"structure_soft_target_loss_weight",
|
| 114 |
+
"structure_target_backend",
|
| 115 |
+
"structure_target_equivalent_count_mean",
|
| 116 |
+
"structure_target_identity_collision_count",
|
| 117 |
+
"structure_target_rank_mean",
|
| 118 |
+
"structure_to_ssl_ratio",
|
| 119 |
+
"structure_top1_top2_gap_mean",
|
| 120 |
+
"structure_top1_top2_gap_norm_mean",
|
| 121 |
+
"structure_top_negative_logit_mean",
|
| 122 |
+
"structure_uncertain_fraction",
|
| 123 |
+
"structure_uncertain_hit_at_1",
|
| 124 |
+
"structure_valid_query_fraction",
|
| 125 |
+
"train_loss",
|
| 126 |
+
"train_loss_ema",
|
| 127 |
+
"train_retrieval_bank_size",
|
| 128 |
+
"variance_loss",
|
| 129 |
+
"variance_weight",
|
| 130 |
+
"weighted_covariance_loss",
|
| 131 |
+
"weighted_regularizer_to_contrastive_ratio",
|
| 132 |
+
"weighted_ssl_loss",
|
| 133 |
+
"weighted_structure_bce_loss",
|
| 134 |
+
"weighted_structure_retrieval_loss",
|
| 135 |
+
"weighted_variance_loss"
|
| 136 |
+
],
|
| 137 |
+
"model_state_bytes": 56474172,
|
| 138 |
+
"model_state_file": "weights/NexaMass-V3-Struct-model_state.pt",
|
| 139 |
+
"param_count": 14106690,
|
| 140 |
+
"state_tensors": 103,
|
| 141 |
+
"step": 91001
|
| 142 |
+
},
|
| 143 |
+
"dataset": "corrected labeled GeMS handoff surface with enforced molecular labels",
|
| 144 |
+
"evaluation_summary": {
|
| 145 |
+
"embedding_std": 0.0722,
|
| 146 |
+
"gallery_exact_fingerprint_decodes": 11,
|
| 147 |
+
"gallery_size": 20,
|
| 148 |
+
"massspecgym_test_hit20_adapter": 0.3505,
|
| 149 |
+
"validation_fingerprint_cosine": 0.4255,
|
| 150 |
+
"validation_structure_bce": 0.0653
|
| 151 |
+
},
|
| 152 |
+
"name": "NexaMass-V3-Struct alignment checkpoint",
|
| 153 |
+
"objective_weights": {
|
| 154 |
+
"retrieval_local_ce_loss_weight": 0.4,
|
| 155 |
+
"retrieval_soft_target_loss_weight": 0.3,
|
| 156 |
+
"retrieval_weight": 0.6,
|
| 157 |
+
"ssl_preservation_weight": 0.05,
|
| 158 |
+
"structure_bce_weight": 1.0
|
| 159 |
+
},
|
| 160 |
+
"parent_checkpoint": "weights/Final_V3-model_state.pt",
|
| 161 |
+
"phase": "RDKit Morgan fingerprint structure alignment"
|
| 162 |
+
}
|
metadata/Final_V3_manifest.json
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"model_name": "Final_V3",
|
| 3 |
+
"created_at_utc": "2026-04-13T19:53:04Z",
|
| 4 |
+
"source_checkpoint": "/home/ubuntu/artifacts/nexa_mol/v1_full_pretrain/phase1_blackwell_v3/current_checkpoint.pt",
|
| 5 |
+
"checkpoint_alias": "/home/ubuntu/artifacts/nexa_mol/v1_full_pretrain/phase1_blackwell_v3/Final_V3.pt",
|
| 6 |
+
"bytes": 151419519,
|
| 7 |
+
"run_name": "gems_v1_full_pretrain_blackwell_phase1_v3",
|
| 8 |
+
"dataset_root": "/home/ubuntu/data/gems_v1_v1_full_pretrain_phase1_v3",
|
| 9 |
+
"phase": "ssl_structure_foundation_final_v3"
|
| 10 |
+
}
|
metadata/archive/20260413T201817Z/latest_checkpoint.txt
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
/home/ubuntu/artifacts/nexa_mol/final_v3_structure_alignment/v26_r1/current_checkpoint.pt
|
metadata/archive/20260413T202042Z/latest_checkpoint.txt
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
/home/ubuntu/artifacts/nexa_mol/final_v3_structure_alignment/v26_r1/current_checkpoint.pt
|
metadata/archive/20260413T213902Z/day1_validation_report.json
ADDED
|
@@ -0,0 +1,2475 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
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|
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|
|
|
|
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|
|
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|
| 1 |
+
{
|
| 2 |
+
"analysis_basis": [
|
| 3 |
+
"docs/projects/msms_pipeline/Technical_Plan.md",
|
| 4 |
+
"docs/projects/msms_pipeline/Engineering.md"
|
| 5 |
+
],
|
| 6 |
+
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|
| 7 |
+
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|
| 8 |
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|
| 9 |
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| 10 |
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|
| 11 |
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|
| 12 |
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| 13 |
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| 14 |
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| 15 |
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| 18 |
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|
| 19 |
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|
| 20 |
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|
| 21 |
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| 22 |
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|
| 23 |
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|
| 24 |
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| 25 |
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| 26 |
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| 27 |
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| 28 |
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| 30 |
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| 31 |
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| 32 |
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| 33 |
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| 34 |
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| 35 |
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| 36 |
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| 37 |
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| 40 |
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| 42 |
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|
| 43 |
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|
| 44 |
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|
| 45 |
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|
| 46 |
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| 47 |
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|
| 55 |
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|
| 56 |
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|
| 57 |
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| 58 |
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| 59 |
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| 68 |
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| 71 |
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| 80 |
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| 92 |
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| 103 |
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| 104 |
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| 106 |
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|
| 116 |
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|
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| 128 |
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| 140 |
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| 152 |
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|
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|
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|
| 2475 |
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|
metadata/archive/20260413T213902Z/latest_checkpoint.txt
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
/home/ubuntu/artifacts/nexa_mol/final_v3_structure_alignment/v26_r1/current_checkpoint.pt
|
metadata/archive/20260413T213902Z/latest_metrics.json
ADDED
|
@@ -0,0 +1,169 @@
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|
| 1 |
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{
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| 2 |
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|
| 3 |
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|
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| 114 |
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"structure_top_negative_logit_mean": 10.1875,
|
| 115 |
+
"structure_uncertain_fraction": 0.970703125,
|
| 116 |
+
"structure_uncertain_hit_at_1": 0.0040241447277367115,
|
| 117 |
+
"structure_valid_query_fraction": 1.0,
|
| 118 |
+
"test_expected_shard_count": 2.0,
|
| 119 |
+
"test_full_coverage": 0.0,
|
| 120 |
+
"test_observed_shard_count": 0.0,
|
| 121 |
+
"train_loss": 6.491608619689941,
|
| 122 |
+
"train_loss_ema": 6.519052214060582,
|
| 123 |
+
"train_retrieval_bank_size": 4096.0,
|
| 124 |
+
"val_deep_eval_single_view": 0.0,
|
| 125 |
+
"val_deep_eval_structure_only": 0.0,
|
| 126 |
+
"val_effective_structure_bce_weight": 1.0,
|
| 127 |
+
"val_eval_loader_mode": "stream",
|
| 128 |
+
"val_eval_mode": "deep",
|
| 129 |
+
"val_expected_shard_count": 2.0,
|
| 130 |
+
"val_full_coverage": 1.0,
|
| 131 |
+
"val_hit_at_1": 0.0282135009765625,
|
| 132 |
+
"val_hit_at_20": 0.15270233154296875,
|
| 133 |
+
"val_hit_at_5": 0.0726776123046875,
|
| 134 |
+
"val_loss": 6.646750174145564,
|
| 135 |
+
"val_observed_eval_batches": 256.0,
|
| 136 |
+
"val_observed_shard_count": 2.0,
|
| 137 |
+
"val_ssl_loss": 1.0397093277424574,
|
| 138 |
+
"val_structure_bce_loss": 0.06532714269997086,
|
| 139 |
+
"val_structure_candidate_count": 4119.69140625,
|
| 140 |
+
"val_structure_clear_fraction": 0.0,
|
| 141 |
+
"val_structure_clear_hit_at_1": 0.0,
|
| 142 |
+
"val_structure_close_fraction": 0.0077972412109375,
|
| 143 |
+
"val_structure_close_hit_at_1": 0.014835291571216658,
|
| 144 |
+
"val_structure_confidence_candidate_count": 4096.0,
|
| 145 |
+
"val_structure_confidence_separation": -0.027957430157584895,
|
| 146 |
+
"val_structure_correct_logit_mean": 9.49462890625,
|
| 147 |
+
"val_structure_fingerprint_cosine": 0.4255251376889646,
|
| 148 |
+
"val_structure_logit_margin_mean": -1.221923828125,
|
| 149 |
+
"val_structure_missing_formula_fraction": 0.0,
|
| 150 |
+
"val_structure_missing_inchikey_fraction": 0.0,
|
| 151 |
+
"val_structure_retrieval_logit_std": 2.6622845763340592,
|
| 152 |
+
"val_structure_retrieval_loss": 10.882395941764116,
|
| 153 |
+
"val_structure_target_equivalent_count_mean": 1.0,
|
| 154 |
+
"val_structure_target_identity_collision_count": 23.69140625,
|
| 155 |
+
"val_structure_target_rank_mean": 379.8781280517578,
|
| 156 |
+
"val_structure_top1_top2_gap_mean": 0.11855697631835938,
|
| 157 |
+
"val_structure_top1_top2_gap_norm_mean": 0.05411380449368153,
|
| 158 |
+
"val_structure_top_negative_logit_mean": 10.714599609375,
|
| 159 |
+
"val_structure_uncertain_fraction": 0.9922027587890625,
|
| 160 |
+
"val_structure_uncertain_hit_at_1": 0.027957430157584895,
|
| 161 |
+
"variance_loss": 0.00213623046875,
|
| 162 |
+
"variance_weight": 0.8,
|
| 163 |
+
"weighted_covariance_loss": 0.015018927097320557,
|
| 164 |
+
"weighted_regularizer_to_contrastive_ratio": 0.028996589606316913,
|
| 165 |
+
"weighted_ssl_loss": 0.02968101501464844,
|
| 166 |
+
"weighted_structure_bce_loss": 0.08383000642061234,
|
| 167 |
+
"weighted_structure_retrieval_loss": 6.378097343444824,
|
| 168 |
+
"weighted_variance_loss": 0.001708984375
|
| 169 |
+
}
|
metadata/archive/20260413T213902Z/perf_summary.json
ADDED
|
@@ -0,0 +1,16 @@
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+
{
|
| 2 |
+
"logged_points": 521,
|
| 3 |
+
"train_loss_min": 3.2278988361358643,
|
| 4 |
+
"train_loss_max": 7.677141189575195,
|
| 5 |
+
"train_loss_last": 6.491608619689941,
|
| 6 |
+
"train_loss_ema_last": 6.519052214060582,
|
| 7 |
+
"weighted_regularizer_ratio_median": 0.02686493425176378,
|
| 8 |
+
"weighted_regularizer_ratio_p90": 0.09558363904110317,
|
| 9 |
+
"samples_per_sec_mean": 1545.3851888056406,
|
| 10 |
+
"samples_per_sec_min": 930.5498563655322,
|
| 11 |
+
"samples_per_sec_max": 2247.460787425693,
|
| 12 |
+
"step_time_mean": 0.33468720350667036,
|
| 13 |
+
"mfu_mean": 0.06923433462788683,
|
| 14 |
+
"mfu_max": 0.10068781126337953,
|
| 15 |
+
"gpu_mem_max_gb": 13.279157161712646
|
| 16 |
+
}
|
metadata/archive/20260413T213902Z/results.md
ADDED
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@@ -0,0 +1,171 @@
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|
| 1 |
+
# MS/MS Training Run Result
|
| 2 |
+
|
| 3 |
+
- Run name: `final_v3_structure_alignment_v26_r1`
|
| 4 |
+
- Created at: `2026-04-13T21:36:19Z`
|
| 5 |
+
- Dataset root: `/home/ubuntu/data/gems_v1_handoff_labeled_v26`
|
| 6 |
+
- Shard counts: `{'train': 4, 'val': 2, 'test': 2}`
|
| 7 |
+
|
| 8 |
+
## Primary Metrics
|
| 9 |
+
|
| 10 |
+
- `train_loss`: `6.491608619689941`
|
| 11 |
+
- `val_loss`: `6.646750174145564`
|
| 12 |
+
- `grad_norm`: `1.7659728527069092`
|
| 13 |
+
- `contrastive_loss`: `0.5768923759460449`
|
| 14 |
+
- `structure_bce_loss`: `0.08383000642061234`
|
| 15 |
+
- `val_structure_bce_loss`: `0.06532714269997086`
|
| 16 |
+
- `val_structure_fingerprint_cosine`: `0.4255251376889646`
|
| 17 |
+
- `different_inchikey_mean_cosine`: `0.004679911238535274`
|
| 18 |
+
- `embedding_std`: `0.07216806709766388`
|
| 19 |
+
- `val_hit_at_20`: `0.15270233154296875`
|
| 20 |
+
- `val_structure_candidate_count`: `4119.69140625`
|
| 21 |
+
- `val_structure_top1_top2_gap_mean`: `0.11855697631835938`
|
| 22 |
+
- `val_structure_uncertain_fraction`: `0.9922027587890625`
|
| 23 |
+
- `val_structure_clear_hit_at_1`: `0.0`
|
| 24 |
+
- `val_structure_confidence_separation`: `-0.027957430157584895`
|
| 25 |
+
- `samples_per_sec`: `1399.0203442084614`
|
| 26 |
+
|
| 27 |
+
## Failure Mode Assessment
|
| 28 |
+
|
| 29 |
+
- `dataset_shards_accessible`: pass. Observed shard counts {'train': 4, 'val': 2, 'test': 2}; the canonical Day 1 loader contract requires all splits to be readable.
|
| 30 |
+
- `dataloader_streaming_stable`: pass. Training batches continued to arrive with positive throughput and no iterator stall.
|
| 31 |
+
- `non_finite_loss_or_tensor_failure`: pass. Technical_Plan requires zero NaN or Inf tensors and finite train/val loss.
|
| 32 |
+
- `validation_observability_gap`: pass. Engineering.md expects slope-aware monitoring; missing validation or retrieval metrics make trend judgment weaker.
|
| 33 |
+
- `gpu_memory_instability`: pass. Technical_Plan Day 1 success requires stable GPU memory through the bounded run.
|
| 34 |
+
- `checkpoint_save_reload_failure`: pass. Checkpoint save and reload is an explicit Day 1 success criterion.
|
| 35 |
+
- `embedding_collapse`: pass. Engineering.md expects embedding variance monitoring and non-collapsed representations.
|
| 36 |
+
- `regularizer_dominance`: pass. The current instability mode is regularization overpowering the SSL objective and producing jagged total loss.
|
| 37 |
+
- `structure_supervision_gap`: pass. Once the bridge phase begins, Technical_Plan requires retrieval-oriented validation rather than only augmented contrastive loss.
|
| 38 |
+
- `confidence_surface_gap`: pass. Structure-first qualification now requires explicit confidence diagnostics such as top-1/top-2 separation and uncertainty buckets, not just broader retrieval metrics.
|
| 39 |
+
- `chemistry_target_backend_gap`: pass. The bridge phase should prefer chemistry-grounded fingerprint targets; hashed fallback targets weaken the retrieval signal.
|
| 40 |
+
- `bridge_fit_without_ranking`: pass. Bridge runs must improve retrieval after fingerprint fit saturates; fitting BCE while val Hit@20 stays at candidate-bank baseline means ranking is not generalizing.
|
| 41 |
+
- `metadata_shortcutting`: watch. Engineering.md flags acquisition-artefact shortcut risk; adduct and instrument embeddings remain deliberately down-scaled.
|
| 42 |
+
|
| 43 |
+
## Pressure Summary
|
| 44 |
+
|
| 45 |
+
- all metrics overall: `↗ improving (+21.89% weighted)`
|
| 46 |
+
- all metrics weighted improvement: `+21.89%`
|
| 47 |
+
- all metrics average improvement: `+34.52%`
|
| 48 |
+
- all metrics weighted pressure score: `0.284`
|
| 49 |
+
- all metrics counts: `up=21` `none=7` `down=11` `up_fraction=+53.85%`
|
| 50 |
+
- primary metrics overall: `↗ improving (-4.45% weighted)`
|
| 51 |
+
- primary metrics weighted improvement: `-4.45%`
|
| 52 |
+
|
| 53 |
+
## Metric Pressure
|
| 54 |
+
|
| 55 |
+
- `train_loss`: `goal ↓ | ↗ +10.57% improving` (early `6.340343519442343`, mid `5.795929590401622`, late `5.670367905463295`, last `6.491608619689941`)
|
| 56 |
+
- `train_loss_ema`: `goal ↓ | ↗ +10.46% improving` (early `6.265581717362126`, mid `5.8371378655061745`, late `5.610470838300897`, last `6.519052214060582`)
|
| 57 |
+
- `contrastive_loss`: `goal ↓ | → -1.64% flat` (early `1.409792113166324`, mid `1.3598130204084982`, late `1.43291388983014`, last `0.5768923759460449`)
|
| 58 |
+
- `val_loss`: `goal ↓ | ↘ -55.82% worsening` (early `1.2876316868363775`, mid `2.20697861268016`, late `2.006446816829876`, last `6.646750174145564`)
|
| 59 |
+
- `val_ssl_loss`: `goal ↓ | → +2.32% flat` (early `1.0849772497183745`, mid `1.0747967428483425`, late `1.0598136518327963`, last `1.0397093277424574`)
|
| 60 |
+
- `structure_bce_loss`: `goal ↓ | ↗ +39.75% improving` (early `0.12293951650489272`, mid `0.07652380578004556`, late `0.07407209518012302`, last `0.08383000642061234`)
|
| 61 |
+
- `val_structure_bce_loss`: `goal ↓ | ↗ +12.20% improving` (early `0.0771060927848642`, mid `0.07029345531468391`, late `0.06769936999660872`, last `0.06532714269997086`)
|
| 62 |
+
- `structure_retrieval_loss`: `goal ↓ | ↗ +10.09% improving` (early `10.237479978903181`, mid `9.41523380775672`, late `9.204101929719421`, last `10.630162239074707`)
|
| 63 |
+
- `val_structure_retrieval_loss`: `goal ↓ | → +4.86% flat` (early `11.562767315655947`, mid `12.150514367967844`, late `11.000247791409492`, last `10.882395941764116`)
|
| 64 |
+
- `structure_fingerprint_cosine`: `goal ↑ | ↗ +17.59% improving` (early `0.3965519580968543`, mid `0.4464927057310336`, late `0.46629545006943846`, last `0.46021854877471924`)
|
| 65 |
+
- `val_structure_fingerprint_cosine`: `goal ↑ | → +0.17% flat` (early `0.4147872725734487`, mid `0.4204998221830465`, late `0.4154996814322658`, last `0.4255251376889646`)
|
| 66 |
+
- `structure_target_rank_mean`: `goal ↓ | ↗ +58.80% improving` (early `351.84337743858384`, mid `176.16389315390174`, late `144.9534280711207`, last `314.703125`)
|
| 67 |
+
- `val_structure_target_rank_mean`: `goal ↓ | ↗ +45.45% improving` (early `760.7540435791016`, mid `683.3385162353516`, late `415.0084457397461`, last `379.8781280517578`)
|
| 68 |
+
- `structure_correct_logit_mean`: `goal ↑ | ↗ +47.31% improving` (early `6.134031791907514`, mid `9.474891618497109`, late `9.035739942528735`, last `9.1875`)
|
| 69 |
+
- `val_structure_correct_logit_mean`: `goal ↑ | ↗ +16.44% improving` (early `8.0311279296875`, mid `8.441162109375`, late `9.3515625`, last `9.49462890625`)
|
| 70 |
+
- `structure_top_negative_logit_mean`: `goal ↓ | ↘ -39.72% worsening` (early `7.262102601156069`, mid `10.490606936416185`, late `10.14691091954023`, last `10.1875`)
|
| 71 |
+
- `val_structure_top_negative_logit_mean`: `goal ↓ | ↘ -16.08% worsening` (early `9.2255859375`, mid `10.51171875`, late `10.708740234375`, last `10.714599609375`)
|
| 72 |
+
- `structure_logit_margin_mean`: `goal ↑ | → +1.23% flat` (early `-1.1263999277456647`, mid `-1.0154725885115607`, late `-1.1125740840517242`, last `-1.0078125`)
|
| 73 |
+
- `val_structure_logit_margin_mean`: `goal ↑ | ↘ -13.67% worsening` (early `-1.194091796875`, mid `-2.0707931518554688`, late `-1.3573150634765625`, last `-1.221923828125`)
|
| 74 |
+
- `structure_retrieval_logit_std`: `goal ↑ | ↗ +112.06% improving` (early `1.0455416592559375`, mid `2.0036371215919537`, late `2.2171744848119803`, last `0.9839304089546204`)
|
| 75 |
+
- `val_structure_retrieval_logit_std`: `goal ↑ | ↗ +102.33% improving` (early `1.2326001720502973`, mid `2.022283688886091`, late `2.49387572822161`, last `2.6622845763340592`)
|
| 76 |
+
- `structure_top1_top2_gap_mean`: `goal ↑ | ↗ +59.22% improving` (early `0.09793567106213873`, mid `0.12284365968208093`, late `0.1559343009159483`, last `0.0986328125`)
|
| 77 |
+
- `val_structure_top1_top2_gap_mean`: `goal ↑ | ↗ +54.91% improving` (early `0.07839679718017578`, mid `0.1310901641845703`, late `0.12144279479980469`, last `0.11855697631835938`)
|
| 78 |
+
- `structure_top1_top2_gap_norm_mean`: `goal ↑ | ↘ -35.65% worsening` (early `0.12215662799301864`, mid `0.07032911504236604`, late `0.07860379065547524`, last `0.10155132412910461`)
|
| 79 |
+
- `val_structure_top1_top2_gap_norm_mean`: `goal ↑ | ↘ -16.83% worsening` (early `0.06950851557485294`, mid `0.07577372609375743`, late `0.0578127475164365`, last `0.05411380449368153`)
|
| 80 |
+
- `structure_uncertain_fraction`: `goal ↓ | ↘ -6.11% worsening` (early `0.924663565751445`, mid `0.984747561416185`, late `0.9811422413793104`, last `0.970703125`)
|
| 81 |
+
- `val_structure_uncertain_fraction`: `goal ↓ | → -0.03% flat` (early `0.9941177368164062`, mid `0.9762382507324219`, late `0.9944419860839844`, last `0.9922027587890625`)
|
| 82 |
+
- `structure_confidence_separation`: `goal ↑ | ↘ -531.56% worsening` (early `-0.007918493679557749`, mid `-0.032136323077151974`, late `-0.05001017991881603`, last `-0.0040241447277367115`)
|
| 83 |
+
- `val_structure_confidence_separation`: `goal ↑ | ↘ -204.85% worsening` (early `-0.00795668972114072`, mid `-0.01309866098972634`, late `-0.024256365849851136`, last `-0.027957430157584895`)
|
| 84 |
+
- `structure_hit_at_1`: `goal ↑ | ↗ +549.68% improving` (early `0.007880238439306358`, mid `0.032706376445086706`, late `0.051196569683908046`, last `0.00390625`)
|
| 85 |
+
- `structure_hit_at_5`: `goal ↑ | ↗ +402.47% improving` (early `0.03360955563583815`, mid `0.11673591040462428`, late `0.16887796336206898`, last `0.029296875`)
|
| 86 |
+
- `structure_hit_at_20`: `goal ↑ | ↗ +239.46% improving` (early `0.10721865968208093`, mid `0.28105807442196534`, late `0.3639659662356322`, last `0.1171875`)
|
| 87 |
+
- `val_hit_at_1`: `goal ↑ | → +207.79% flat` (early `0.0079345703125`, mid `0.01372528076171875`, late `0.02442169189453125`, last `0.0282135009765625`)
|
| 88 |
+
- `val_hit_at_5`: `goal ↑ | ↗ +134.85% improving` (early `0.0288543701171875`, mid `0.0425567626953125`, late `0.0677642822265625`, last `0.0726776123046875`)
|
| 89 |
+
- `val_hit_at_20`: `goal ↑ | ↗ +76.67% improving` (early `0.08158111572265625`, mid `0.094146728515625`, late `0.14413070678710938`, last `0.15270233154296875`)
|
| 90 |
+
- `weighted_regularizer_to_contrastive_ratio`: `goal ↓ | ↗ +54.61% improving` (early `0.0667222202939108`, mid `0.0329437448262181`, late `0.03028750823471251`, last `0.028996589606316913`)
|
| 91 |
+
- `samples_per_sec`: `goal ↑ | ↘ -2.88% worsening` (early `1575.5664566260843`, mid `1531.3735887168505`, late `1530.1496274666802`, last `1399.0203442084614`)
|
| 92 |
+
- `gpu_max_memory_gb`: `goal ≈ | ↗ +100.00% improving` (early `13.252925169950275`, mid `13.279059082097401`, late `13.279157161712646`, last `13.279157161712646`)
|
| 93 |
+
- `grad_norm`: `goal ≈ | ↘ -100.00% worsening` (early `4.638080189338309`, mid `4.979553528603791`, late `6.072961742165445`, last `1.7659728527069092`)
|
| 94 |
+
|
| 95 |
+
## Primary Metric Pressure
|
| 96 |
+
|
| 97 |
+
- `train_loss`: `goal ↓ | ↗ +10.57% improving` (early `6.340343519442343`, mid `5.795929590401622`, late `5.670367905463295`, last `6.491608619689941`)
|
| 98 |
+
- `val_loss`: `goal ↓ | ↘ -55.82% worsening` (early `1.2876316868363775`, mid `2.20697861268016`, late `2.006446816829876`, last `6.646750174145564`)
|
| 99 |
+
- `grad_norm`: `goal ≈ | ↘ -100.00% worsening` (early `4.638080189338309`, mid `4.979553528603791`, late `6.072961742165445`, last `1.7659728527069092`)
|
| 100 |
+
- `contrastive_loss`: `goal ↓ | → -1.64% flat` (early `1.409792113166324`, mid `1.3598130204084982`, late `1.43291388983014`, last `0.5768923759460449`)
|
| 101 |
+
- `structure_bce_loss`: `goal ↓ | ↗ +39.75% improving` (early `0.12293951650489272`, mid `0.07652380578004556`, late `0.07407209518012302`, last `0.08383000642061234`)
|
| 102 |
+
- `val_structure_bce_loss`: `goal ↓ | ↗ +12.20% improving` (early `0.0771060927848642`, mid `0.07029345531468391`, late `0.06769936999660872`, last `0.06532714269997086`)
|
| 103 |
+
- `val_structure_fingerprint_cosine`: `goal ↑ | → +0.17% flat` (early `0.4147872725734487`, mid `0.4204998221830465`, late `0.4154996814322658`, last `0.4255251376889646`)
|
| 104 |
+
- `val_hit_at_20`: `goal ↑ | ↗ +76.67% improving` (early `0.08158111572265625`, mid `0.094146728515625`, late `0.14413070678710938`, last `0.15270233154296875`)
|
| 105 |
+
- `val_structure_top1_top2_gap_mean`: `goal ↑ | ↗ +54.91% improving` (early `0.07839679718017578`, mid `0.1310901641845703`, late `0.12144279479980469`, last `0.11855697631835938`)
|
| 106 |
+
- `val_structure_uncertain_fraction`: `goal ↓ | → -0.03% flat` (early `0.9941177368164062`, mid `0.9762382507324219`, late `0.9944419860839844`, last `0.9922027587890625`)
|
| 107 |
+
- `val_structure_confidence_separation`: `goal ↑ | ↘ -204.85% worsening` (early `-0.00795668972114072`, mid `-0.01309866098972634`, late `-0.024256365849851136`, last `-0.027957430157584895`)
|
| 108 |
+
- `samples_per_sec`: `goal ↑ | ↘ -2.88% worsening` (early `1575.5664566260843`, mid `1531.3735887168505`, late `1530.1496274666802`, last `1399.0203442084614`)
|
| 109 |
+
|
| 110 |
+
## Debug Metrics
|
| 111 |
+
|
| 112 |
+
- `train_loss`: `6.491608619689941`
|
| 113 |
+
- `train_loss_ema`: `6.519052214060582`
|
| 114 |
+
- `val_loss`: `6.646750174145564`
|
| 115 |
+
- `contrastive_loss`: `0.5768923759460449`
|
| 116 |
+
- `val_ssl_loss`: `1.0397093277424574`
|
| 117 |
+
- `variance_loss`: `0.00213623046875`
|
| 118 |
+
- `covariance_loss`: `10.012618064880371`
|
| 119 |
+
- `structure_bce_loss`: `0.08383000642061234`
|
| 120 |
+
- `structure_retrieval_loss`: `10.630162239074707`
|
| 121 |
+
- `val_structure_retrieval_loss`: `10.882395941764116`
|
| 122 |
+
- `structure_fingerprint_cosine`: `0.46021854877471924`
|
| 123 |
+
- `structure_hit_at_1`: `0.00390625`
|
| 124 |
+
- `structure_hit_at_5`: `0.029296875`
|
| 125 |
+
- `structure_hit_at_20`: `0.1171875`
|
| 126 |
+
- `structure_target_rank_mean`: `314.703125`
|
| 127 |
+
- `structure_logit_margin_mean`: `-1.0078125`
|
| 128 |
+
- `structure_retrieval_logit_std`: `0.9839304089546204`
|
| 129 |
+
- `val_structure_retrieval_logit_std`: `2.6622845763340592`
|
| 130 |
+
- `structure_correct_logit_mean`: `9.1875`
|
| 131 |
+
- `structure_top_negative_logit_mean`: `10.1875`
|
| 132 |
+
- `val_structure_correct_logit_mean`: `9.49462890625`
|
| 133 |
+
- `val_structure_top_negative_logit_mean`: `10.714599609375`
|
| 134 |
+
- `structure_top1_top2_gap_mean`: `0.0986328125`
|
| 135 |
+
- `structure_top1_top2_gap_norm_mean`: `0.10155132412910461`
|
| 136 |
+
- `structure_confidence_candidate_count`: `2126.0`
|
| 137 |
+
- `structure_close_fraction`: `0.029296875`
|
| 138 |
+
- `structure_uncertain_fraction`: `0.970703125`
|
| 139 |
+
- `structure_uncertain_hit_at_1`: `0.0040241447277367115`
|
| 140 |
+
- `structure_confidence_separation`: `-0.0040241447277367115`
|
| 141 |
+
- `val_structure_top1_top2_gap_mean`: `0.11855697631835938`
|
| 142 |
+
- `val_structure_top1_top2_gap_norm_mean`: `0.05411380449368153`
|
| 143 |
+
- `val_structure_confidence_candidate_count`: `4096.0`
|
| 144 |
+
- `val_structure_close_fraction`: `0.0077972412109375`
|
| 145 |
+
- `val_structure_uncertain_fraction`: `0.9922027587890625`
|
| 146 |
+
- `val_structure_close_hit_at_1`: `0.014835291571216658`
|
| 147 |
+
- `val_structure_uncertain_hit_at_1`: `0.027957430157584895`
|
| 148 |
+
- `val_structure_confidence_separation`: `-0.027957430157584895`
|
| 149 |
+
- `grad_norm`: `1.7659728527069092`
|
| 150 |
+
- `weighted_regularizer_to_contrastive_ratio`: `0.028996589606316913`
|
| 151 |
+
- `samples_per_sec`: `1399.0203442084614`
|
| 152 |
+
- `samples_per_sec_60s`: `1399.0203442084614`
|
| 153 |
+
- `samples_per_sec_300s`: `1547.033197547462`
|
| 154 |
+
- `samples_per_sec_900s`: `1512.7885250414204`
|
| 155 |
+
- `samples_per_sec_run_mean`: `1533.0231795943853`
|
| 156 |
+
- `gpu_max_memory_gb`: `13.279157161712646`
|
| 157 |
+
- `gpu_util_mean_pct`: `36.0`
|
| 158 |
+
- `gpu_util_min_pct`: `18.0`
|
| 159 |
+
- `gpu_util_max_pct`: `54.0`
|
| 160 |
+
- `gpu_active_fraction`: `1.0`
|
| 161 |
+
- `gpu_mem_used_mean_gb`: `14.1943359375`
|
| 162 |
+
- `gpu_mem_util_mean_pct`: `14.848754175733243`
|
| 163 |
+
- `gpu_snapshot_available`: `1.0`
|
| 164 |
+
- `step_time_sec`: `0.3659703749981418`
|
| 165 |
+
- `step_time_cv_300s`: `0.06743440395418321`
|
| 166 |
+
- `step_time_cv_900s`: `0.09218780075615579`
|
| 167 |
+
- `dataloader_wait_pct_60s`: `0.0961140629935623`
|
| 168 |
+
- `dataloader_wait_pct_300s`: `0.0382592069912683`
|
| 169 |
+
- `dataloader_wait_pct_900s`: `0.066640439237091`
|
| 170 |
+
- `dataloader_wait_pct_run`: `0.06717802801040498`
|
| 171 |
+
- `mfu_estimate`: `0.06267708747552383`
|
metadata/checksums.sha256
ADDED
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cef7a30afbc0f4c107dc791039b42edf61bff2337023f9d80b6ff5a2a94bbf29 README.md
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|
metadata/closeout_receipt.json
ADDED
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{
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"created_at": "2026-04-13T21:47:04Z",
|
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"dataset_receipt_path": "/home/ubuntu/artifacts/nexa_mol/final_v3_structure_alignment/v26_r1/dataset_receipt.json",
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"remote_checkpoint_uri": "wasabi:nexa-ms/models/dreams_pretrain_v1/checkpoints",
|
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"remote_dataset_uri": "wasabi:nexa-ms/processed/gems_v1/receipts",
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"remote_log_uri": "wasabi:nexa-ms/models/dreams_pretrain_v1/logs",
|
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+
"run_name": "final_v3_structure_alignment_v26_r1"
|
| 9 |
+
}
|
metadata/day1_validation_report.json
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|
| 1 |
+
{
|
| 2 |
+
"analysis_basis": [
|
| 3 |
+
"docs/projects/msms_pipeline/Technical_Plan.md",
|
| 4 |
+
"docs/projects/msms_pipeline/Engineering.md"
|
| 5 |
+
],
|
| 6 |
+
"created_at": "2026-04-13T21:36:19Z",
|
| 7 |
+
"dataset_root": "/home/ubuntu/data/gems_v1_handoff_labeled_v26",
|
| 8 |
+
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|
| 9 |
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| 10 |
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|
| 11 |
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|
| 12 |
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|
| 13 |
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| 14 |
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| 15 |
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| 16 |
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| 18 |
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|
| 19 |
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|
| 20 |
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|
| 21 |
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| 22 |
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|
| 23 |
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|
| 24 |
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| 25 |
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| 26 |
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| 27 |
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| 28 |
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| 30 |
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| 31 |
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| 32 |
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| 33 |
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| 34 |
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| 35 |
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| 36 |
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| 37 |
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| 40 |
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|
| 41 |
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| 42 |
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|
| 43 |
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|
| 44 |
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|
| 45 |
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|
| 46 |
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| 47 |
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| 48 |
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|
| 55 |
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|
| 56 |
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|
| 57 |
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|
| 58 |
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|
| 59 |
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| 68 |
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| 71 |
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| 80 |
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| 92 |
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| 103 |
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|
| 104 |
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| 106 |
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|
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|
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|
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|
| 116 |
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|
| 117 |
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|
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| 128 |
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| 140 |
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| 142 |
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|
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metadata/eval_coverage.json
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metadata/fast_eval_surface.json
ADDED
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@@ -0,0 +1,14 @@
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{
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metadata/latest_checkpoint.txt
ADDED
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@@ -0,0 +1 @@
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/home/ubuntu/artifacts/nexa_mol/final_v3_structure_alignment/v26_r1/current_checkpoint.pt
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metadata/latest_metrics.json
ADDED
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@@ -0,0 +1,169 @@
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| 31 |
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|
| 32 |
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| 33 |
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| 34 |
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| 35 |
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| 36 |
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| 37 |
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| 39 |
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|
| 40 |
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"lr": 3.886130904636371e-06,
|
| 41 |
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| 42 |
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| 43 |
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| 44 |
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|
| 45 |
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|
| 46 |
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|
| 47 |
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| 70 |
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"structure_confidence_clear_threshold": 1.0,
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| 71 |
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"structure_confidence_close_threshold": 0.35,
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| 72 |
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"structure_post_label_candidate_count": 2126.0,
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"structure_post_precursor_candidate_count": 2128.0,
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"structure_prefilter_candidate_count": 2128.0,
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"structure_retrieval_logit_std": 0.9839304089546204,
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| 100 |
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"structure_retrieval_loss": 10.630162239074707,
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"structure_retrieval_scorer": "projected_dot",
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"structure_retrieval_target_mode": "sample",
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| 103 |
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"structure_skipped_query_fraction": 0.0,
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| 104 |
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"structure_soft_target_exact_mix": 0.4,
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| 105 |
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"structure_soft_target_loss": 7.523618221282959,
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"structure_soft_target_loss_weight": 0.3,
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"structure_target_backend": "rdkit_morgan",
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"structure_target_equivalent_count_mean": 1.0,
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| 109 |
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"structure_target_identity_collision_count": 0.0,
|
| 110 |
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"structure_target_rank_mean": 314.703125,
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| 111 |
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"structure_to_ssl_ratio": 217.7124787233957,
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| 112 |
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"structure_top1_top2_gap_mean": 0.0986328125,
|
| 113 |
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"structure_top1_top2_gap_norm_mean": 0.10155132412910461,
|
| 114 |
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"structure_top_negative_logit_mean": 10.1875,
|
| 115 |
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"structure_uncertain_fraction": 0.970703125,
|
| 116 |
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"structure_uncertain_hit_at_1": 0.0040241447277367115,
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| 117 |
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"structure_valid_query_fraction": 1.0,
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| 118 |
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"test_expected_shard_count": 2.0,
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| 119 |
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"test_full_coverage": 0.0,
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| 120 |
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"test_observed_shard_count": 0.0,
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| 121 |
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"train_loss": 6.491608619689941,
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"train_loss_ema": 6.519052214060582,
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"train_retrieval_bank_size": 4096.0,
|
| 124 |
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"val_deep_eval_single_view": 0.0,
|
| 125 |
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"val_deep_eval_structure_only": 0.0,
|
| 126 |
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"val_effective_structure_bce_weight": 1.0,
|
| 127 |
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"val_eval_loader_mode": "stream",
|
| 128 |
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"val_eval_mode": "deep",
|
| 129 |
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"val_expected_shard_count": 2.0,
|
| 130 |
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"val_full_coverage": 1.0,
|
| 131 |
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"val_hit_at_1": 0.0282135009765625,
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"val_hit_at_20": 0.15270233154296875,
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| 133 |
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"val_hit_at_5": 0.0726776123046875,
|
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"val_loss": 6.646750174145564,
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"val_observed_eval_batches": 256.0,
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| 136 |
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"val_observed_shard_count": 2.0,
|
| 137 |
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"val_ssl_loss": 1.0397093277424574,
|
| 138 |
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"val_structure_bce_loss": 0.06532714269997086,
|
| 139 |
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"val_structure_candidate_count": 4119.69140625,
|
| 140 |
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"val_structure_clear_fraction": 0.0,
|
| 141 |
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"val_structure_clear_hit_at_1": 0.0,
|
| 142 |
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"val_structure_close_fraction": 0.0077972412109375,
|
| 143 |
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"val_structure_close_hit_at_1": 0.014835291571216658,
|
| 144 |
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"val_structure_confidence_candidate_count": 4096.0,
|
| 145 |
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"val_structure_confidence_separation": -0.027957430157584895,
|
| 146 |
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"val_structure_correct_logit_mean": 9.49462890625,
|
| 147 |
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"val_structure_fingerprint_cosine": 0.4255251376889646,
|
| 148 |
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"val_structure_logit_margin_mean": -1.221923828125,
|
| 149 |
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"val_structure_missing_formula_fraction": 0.0,
|
| 150 |
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"val_structure_missing_inchikey_fraction": 0.0,
|
| 151 |
+
"val_structure_retrieval_logit_std": 2.6622845763340592,
|
| 152 |
+
"val_structure_retrieval_loss": 10.882395941764116,
|
| 153 |
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"val_structure_target_equivalent_count_mean": 1.0,
|
| 154 |
+
"val_structure_target_identity_collision_count": 23.69140625,
|
| 155 |
+
"val_structure_target_rank_mean": 379.8781280517578,
|
| 156 |
+
"val_structure_top1_top2_gap_mean": 0.11855697631835938,
|
| 157 |
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"val_structure_top1_top2_gap_norm_mean": 0.05411380449368153,
|
| 158 |
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"val_structure_top_negative_logit_mean": 10.714599609375,
|
| 159 |
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"val_structure_uncertain_fraction": 0.9922027587890625,
|
| 160 |
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"val_structure_uncertain_hit_at_1": 0.027957430157584895,
|
| 161 |
+
"variance_loss": 0.00213623046875,
|
| 162 |
+
"variance_weight": 0.8,
|
| 163 |
+
"weighted_covariance_loss": 0.015018927097320557,
|
| 164 |
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"weighted_regularizer_to_contrastive_ratio": 0.028996589606316913,
|
| 165 |
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"weighted_ssl_loss": 0.02968101501464844,
|
| 166 |
+
"weighted_structure_bce_loss": 0.08383000642061234,
|
| 167 |
+
"weighted_structure_retrieval_loss": 6.378097343444824,
|
| 168 |
+
"weighted_variance_loss": 0.001708984375
|
| 169 |
+
}
|
metadata/perf_summary.json
ADDED
|
@@ -0,0 +1,16 @@
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+
{
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| 2 |
+
"logged_points": 521,
|
| 3 |
+
"train_loss_min": 3.2278988361358643,
|
| 4 |
+
"train_loss_max": 7.677141189575195,
|
| 5 |
+
"train_loss_last": 6.491608619689941,
|
| 6 |
+
"train_loss_ema_last": 6.519052214060582,
|
| 7 |
+
"weighted_regularizer_ratio_median": 0.02686493425176378,
|
| 8 |
+
"weighted_regularizer_ratio_p90": 0.09558363904110317,
|
| 9 |
+
"samples_per_sec_mean": 1545.3851888056406,
|
| 10 |
+
"samples_per_sec_min": 930.5498563655322,
|
| 11 |
+
"samples_per_sec_max": 2247.460787425693,
|
| 12 |
+
"step_time_mean": 0.33468720350667036,
|
| 13 |
+
"mfu_mean": 0.06923433462788683,
|
| 14 |
+
"mfu_max": 0.10068781126337953,
|
| 15 |
+
"gpu_mem_max_gb": 13.279157161712646
|
| 16 |
+
}
|
metadata/results.md
ADDED
|
@@ -0,0 +1,171 @@
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|
| 1 |
+
# MS/MS Training Run Result
|
| 2 |
+
|
| 3 |
+
- Run name: `final_v3_structure_alignment_v26_r1`
|
| 4 |
+
- Created at: `2026-04-13T21:36:19Z`
|
| 5 |
+
- Dataset root: `/home/ubuntu/data/gems_v1_handoff_labeled_v26`
|
| 6 |
+
- Shard counts: `{'train': 4, 'val': 2, 'test': 2}`
|
| 7 |
+
|
| 8 |
+
## Primary Metrics
|
| 9 |
+
|
| 10 |
+
- `train_loss`: `6.491608619689941`
|
| 11 |
+
- `val_loss`: `6.646750174145564`
|
| 12 |
+
- `grad_norm`: `1.7659728527069092`
|
| 13 |
+
- `contrastive_loss`: `0.5768923759460449`
|
| 14 |
+
- `structure_bce_loss`: `0.08383000642061234`
|
| 15 |
+
- `val_structure_bce_loss`: `0.06532714269997086`
|
| 16 |
+
- `val_structure_fingerprint_cosine`: `0.4255251376889646`
|
| 17 |
+
- `different_inchikey_mean_cosine`: `0.004679911238535274`
|
| 18 |
+
- `embedding_std`: `0.07216806709766388`
|
| 19 |
+
- `val_hit_at_20`: `0.15270233154296875`
|
| 20 |
+
- `val_structure_candidate_count`: `4119.69140625`
|
| 21 |
+
- `val_structure_top1_top2_gap_mean`: `0.11855697631835938`
|
| 22 |
+
- `val_structure_uncertain_fraction`: `0.9922027587890625`
|
| 23 |
+
- `val_structure_clear_hit_at_1`: `0.0`
|
| 24 |
+
- `val_structure_confidence_separation`: `-0.027957430157584895`
|
| 25 |
+
- `samples_per_sec`: `1399.0203442084614`
|
| 26 |
+
|
| 27 |
+
## Failure Mode Assessment
|
| 28 |
+
|
| 29 |
+
- `dataset_shards_accessible`: pass. Observed shard counts {'train': 4, 'val': 2, 'test': 2}; the canonical Day 1 loader contract requires all splits to be readable.
|
| 30 |
+
- `dataloader_streaming_stable`: pass. Training batches continued to arrive with positive throughput and no iterator stall.
|
| 31 |
+
- `non_finite_loss_or_tensor_failure`: pass. Technical_Plan requires zero NaN or Inf tensors and finite train/val loss.
|
| 32 |
+
- `validation_observability_gap`: pass. Engineering.md expects slope-aware monitoring; missing validation or retrieval metrics make trend judgment weaker.
|
| 33 |
+
- `gpu_memory_instability`: pass. Technical_Plan Day 1 success requires stable GPU memory through the bounded run.
|
| 34 |
+
- `checkpoint_save_reload_failure`: pass. Checkpoint save and reload is an explicit Day 1 success criterion.
|
| 35 |
+
- `embedding_collapse`: pass. Engineering.md expects embedding variance monitoring and non-collapsed representations.
|
| 36 |
+
- `regularizer_dominance`: pass. The current instability mode is regularization overpowering the SSL objective and producing jagged total loss.
|
| 37 |
+
- `structure_supervision_gap`: pass. Once the bridge phase begins, Technical_Plan requires retrieval-oriented validation rather than only augmented contrastive loss.
|
| 38 |
+
- `confidence_surface_gap`: pass. Structure-first qualification now requires explicit confidence diagnostics such as top-1/top-2 separation and uncertainty buckets, not just broader retrieval metrics.
|
| 39 |
+
- `chemistry_target_backend_gap`: pass. The bridge phase should prefer chemistry-grounded fingerprint targets; hashed fallback targets weaken the retrieval signal.
|
| 40 |
+
- `bridge_fit_without_ranking`: pass. Bridge runs must improve retrieval after fingerprint fit saturates; fitting BCE while val Hit@20 stays at candidate-bank baseline means ranking is not generalizing.
|
| 41 |
+
- `metadata_shortcutting`: watch. Engineering.md flags acquisition-artefact shortcut risk; adduct and instrument embeddings remain deliberately down-scaled.
|
| 42 |
+
|
| 43 |
+
## Pressure Summary
|
| 44 |
+
|
| 45 |
+
- all metrics overall: `↗ improving (+21.89% weighted)`
|
| 46 |
+
- all metrics weighted improvement: `+21.89%`
|
| 47 |
+
- all metrics average improvement: `+34.52%`
|
| 48 |
+
- all metrics weighted pressure score: `0.284`
|
| 49 |
+
- all metrics counts: `up=21` `none=7` `down=11` `up_fraction=+53.85%`
|
| 50 |
+
- primary metrics overall: `↗ improving (-4.45% weighted)`
|
| 51 |
+
- primary metrics weighted improvement: `-4.45%`
|
| 52 |
+
|
| 53 |
+
## Metric Pressure
|
| 54 |
+
|
| 55 |
+
- `train_loss`: `goal ↓ | ↗ +10.57% improving` (early `6.340343519442343`, mid `5.795929590401622`, late `5.670367905463295`, last `6.491608619689941`)
|
| 56 |
+
- `train_loss_ema`: `goal ↓ | ↗ +10.46% improving` (early `6.265581717362126`, mid `5.8371378655061745`, late `5.610470838300897`, last `6.519052214060582`)
|
| 57 |
+
- `contrastive_loss`: `goal ↓ | → -1.64% flat` (early `1.409792113166324`, mid `1.3598130204084982`, late `1.43291388983014`, last `0.5768923759460449`)
|
| 58 |
+
- `val_loss`: `goal ↓ | ↘ -55.82% worsening` (early `1.2876316868363775`, mid `2.20697861268016`, late `2.006446816829876`, last `6.646750174145564`)
|
| 59 |
+
- `val_ssl_loss`: `goal ↓ | → +2.32% flat` (early `1.0849772497183745`, mid `1.0747967428483425`, late `1.0598136518327963`, last `1.0397093277424574`)
|
| 60 |
+
- `structure_bce_loss`: `goal ↓ | ↗ +39.75% improving` (early `0.12293951650489272`, mid `0.07652380578004556`, late `0.07407209518012302`, last `0.08383000642061234`)
|
| 61 |
+
- `val_structure_bce_loss`: `goal ↓ | ↗ +12.20% improving` (early `0.0771060927848642`, mid `0.07029345531468391`, late `0.06769936999660872`, last `0.06532714269997086`)
|
| 62 |
+
- `structure_retrieval_loss`: `goal ↓ | ↗ +10.09% improving` (early `10.237479978903181`, mid `9.41523380775672`, late `9.204101929719421`, last `10.630162239074707`)
|
| 63 |
+
- `val_structure_retrieval_loss`: `goal ↓ | → +4.86% flat` (early `11.562767315655947`, mid `12.150514367967844`, late `11.000247791409492`, last `10.882395941764116`)
|
| 64 |
+
- `structure_fingerprint_cosine`: `goal ↑ | ↗ +17.59% improving` (early `0.3965519580968543`, mid `0.4464927057310336`, late `0.46629545006943846`, last `0.46021854877471924`)
|
| 65 |
+
- `val_structure_fingerprint_cosine`: `goal ↑ | → +0.17% flat` (early `0.4147872725734487`, mid `0.4204998221830465`, late `0.4154996814322658`, last `0.4255251376889646`)
|
| 66 |
+
- `structure_target_rank_mean`: `goal ↓ | ↗ +58.80% improving` (early `351.84337743858384`, mid `176.16389315390174`, late `144.9534280711207`, last `314.703125`)
|
| 67 |
+
- `val_structure_target_rank_mean`: `goal ↓ | ↗ +45.45% improving` (early `760.7540435791016`, mid `683.3385162353516`, late `415.0084457397461`, last `379.8781280517578`)
|
| 68 |
+
- `structure_correct_logit_mean`: `goal ↑ | ↗ +47.31% improving` (early `6.134031791907514`, mid `9.474891618497109`, late `9.035739942528735`, last `9.1875`)
|
| 69 |
+
- `val_structure_correct_logit_mean`: `goal ↑ | ↗ +16.44% improving` (early `8.0311279296875`, mid `8.441162109375`, late `9.3515625`, last `9.49462890625`)
|
| 70 |
+
- `structure_top_negative_logit_mean`: `goal ↓ | ↘ -39.72% worsening` (early `7.262102601156069`, mid `10.490606936416185`, late `10.14691091954023`, last `10.1875`)
|
| 71 |
+
- `val_structure_top_negative_logit_mean`: `goal ↓ | ↘ -16.08% worsening` (early `9.2255859375`, mid `10.51171875`, late `10.708740234375`, last `10.714599609375`)
|
| 72 |
+
- `structure_logit_margin_mean`: `goal ↑ | → +1.23% flat` (early `-1.1263999277456647`, mid `-1.0154725885115607`, late `-1.1125740840517242`, last `-1.0078125`)
|
| 73 |
+
- `val_structure_logit_margin_mean`: `goal ↑ | ↘ -13.67% worsening` (early `-1.194091796875`, mid `-2.0707931518554688`, late `-1.3573150634765625`, last `-1.221923828125`)
|
| 74 |
+
- `structure_retrieval_logit_std`: `goal ↑ | ↗ +112.06% improving` (early `1.0455416592559375`, mid `2.0036371215919537`, late `2.2171744848119803`, last `0.9839304089546204`)
|
| 75 |
+
- `val_structure_retrieval_logit_std`: `goal ↑ | ↗ +102.33% improving` (early `1.2326001720502973`, mid `2.022283688886091`, late `2.49387572822161`, last `2.6622845763340592`)
|
| 76 |
+
- `structure_top1_top2_gap_mean`: `goal ↑ | ↗ +59.22% improving` (early `0.09793567106213873`, mid `0.12284365968208093`, late `0.1559343009159483`, last `0.0986328125`)
|
| 77 |
+
- `val_structure_top1_top2_gap_mean`: `goal ↑ | ↗ +54.91% improving` (early `0.07839679718017578`, mid `0.1310901641845703`, late `0.12144279479980469`, last `0.11855697631835938`)
|
| 78 |
+
- `structure_top1_top2_gap_norm_mean`: `goal ↑ | ↘ -35.65% worsening` (early `0.12215662799301864`, mid `0.07032911504236604`, late `0.07860379065547524`, last `0.10155132412910461`)
|
| 79 |
+
- `val_structure_top1_top2_gap_norm_mean`: `goal ↑ | ↘ -16.83% worsening` (early `0.06950851557485294`, mid `0.07577372609375743`, late `0.0578127475164365`, last `0.05411380449368153`)
|
| 80 |
+
- `structure_uncertain_fraction`: `goal ↓ | ↘ -6.11% worsening` (early `0.924663565751445`, mid `0.984747561416185`, late `0.9811422413793104`, last `0.970703125`)
|
| 81 |
+
- `val_structure_uncertain_fraction`: `goal ↓ | → -0.03% flat` (early `0.9941177368164062`, mid `0.9762382507324219`, late `0.9944419860839844`, last `0.9922027587890625`)
|
| 82 |
+
- `structure_confidence_separation`: `goal ↑ | ↘ -531.56% worsening` (early `-0.007918493679557749`, mid `-0.032136323077151974`, late `-0.05001017991881603`, last `-0.0040241447277367115`)
|
| 83 |
+
- `val_structure_confidence_separation`: `goal ↑ | ↘ -204.85% worsening` (early `-0.00795668972114072`, mid `-0.01309866098972634`, late `-0.024256365849851136`, last `-0.027957430157584895`)
|
| 84 |
+
- `structure_hit_at_1`: `goal ↑ | ↗ +549.68% improving` (early `0.007880238439306358`, mid `0.032706376445086706`, late `0.051196569683908046`, last `0.00390625`)
|
| 85 |
+
- `structure_hit_at_5`: `goal ↑ | ↗ +402.47% improving` (early `0.03360955563583815`, mid `0.11673591040462428`, late `0.16887796336206898`, last `0.029296875`)
|
| 86 |
+
- `structure_hit_at_20`: `goal ↑ | ↗ +239.46% improving` (early `0.10721865968208093`, mid `0.28105807442196534`, late `0.3639659662356322`, last `0.1171875`)
|
| 87 |
+
- `val_hit_at_1`: `goal ↑ | → +207.79% flat` (early `0.0079345703125`, mid `0.01372528076171875`, late `0.02442169189453125`, last `0.0282135009765625`)
|
| 88 |
+
- `val_hit_at_5`: `goal ↑ | ↗ +134.85% improving` (early `0.0288543701171875`, mid `0.0425567626953125`, late `0.0677642822265625`, last `0.0726776123046875`)
|
| 89 |
+
- `val_hit_at_20`: `goal ↑ | ↗ +76.67% improving` (early `0.08158111572265625`, mid `0.094146728515625`, late `0.14413070678710938`, last `0.15270233154296875`)
|
| 90 |
+
- `weighted_regularizer_to_contrastive_ratio`: `goal ↓ | ↗ +54.61% improving` (early `0.0667222202939108`, mid `0.0329437448262181`, late `0.03028750823471251`, last `0.028996589606316913`)
|
| 91 |
+
- `samples_per_sec`: `goal ↑ | ↘ -2.88% worsening` (early `1575.5664566260843`, mid `1531.3735887168505`, late `1530.1496274666802`, last `1399.0203442084614`)
|
| 92 |
+
- `gpu_max_memory_gb`: `goal ≈ | ↗ +100.00% improving` (early `13.252925169950275`, mid `13.279059082097401`, late `13.279157161712646`, last `13.279157161712646`)
|
| 93 |
+
- `grad_norm`: `goal ≈ | ↘ -100.00% worsening` (early `4.638080189338309`, mid `4.979553528603791`, late `6.072961742165445`, last `1.7659728527069092`)
|
| 94 |
+
|
| 95 |
+
## Primary Metric Pressure
|
| 96 |
+
|
| 97 |
+
- `train_loss`: `goal ↓ | ↗ +10.57% improving` (early `6.340343519442343`, mid `5.795929590401622`, late `5.670367905463295`, last `6.491608619689941`)
|
| 98 |
+
- `val_loss`: `goal ↓ | ↘ -55.82% worsening` (early `1.2876316868363775`, mid `2.20697861268016`, late `2.006446816829876`, last `6.646750174145564`)
|
| 99 |
+
- `grad_norm`: `goal ≈ | ↘ -100.00% worsening` (early `4.638080189338309`, mid `4.979553528603791`, late `6.072961742165445`, last `1.7659728527069092`)
|
| 100 |
+
- `contrastive_loss`: `goal ↓ | → -1.64% flat` (early `1.409792113166324`, mid `1.3598130204084982`, late `1.43291388983014`, last `0.5768923759460449`)
|
| 101 |
+
- `structure_bce_loss`: `goal ↓ | ↗ +39.75% improving` (early `0.12293951650489272`, mid `0.07652380578004556`, late `0.07407209518012302`, last `0.08383000642061234`)
|
| 102 |
+
- `val_structure_bce_loss`: `goal ↓ | ↗ +12.20% improving` (early `0.0771060927848642`, mid `0.07029345531468391`, late `0.06769936999660872`, last `0.06532714269997086`)
|
| 103 |
+
- `val_structure_fingerprint_cosine`: `goal ↑ | → +0.17% flat` (early `0.4147872725734487`, mid `0.4204998221830465`, late `0.4154996814322658`, last `0.4255251376889646`)
|
| 104 |
+
- `val_hit_at_20`: `goal ↑ | ↗ +76.67% improving` (early `0.08158111572265625`, mid `0.094146728515625`, late `0.14413070678710938`, last `0.15270233154296875`)
|
| 105 |
+
- `val_structure_top1_top2_gap_mean`: `goal ↑ | ↗ +54.91% improving` (early `0.07839679718017578`, mid `0.1310901641845703`, late `0.12144279479980469`, last `0.11855697631835938`)
|
| 106 |
+
- `val_structure_uncertain_fraction`: `goal ↓ | → -0.03% flat` (early `0.9941177368164062`, mid `0.9762382507324219`, late `0.9944419860839844`, last `0.9922027587890625`)
|
| 107 |
+
- `val_structure_confidence_separation`: `goal ↑ | ↘ -204.85% worsening` (early `-0.00795668972114072`, mid `-0.01309866098972634`, late `-0.024256365849851136`, last `-0.027957430157584895`)
|
| 108 |
+
- `samples_per_sec`: `goal ↑ | ↘ -2.88% worsening` (early `1575.5664566260843`, mid `1531.3735887168505`, late `1530.1496274666802`, last `1399.0203442084614`)
|
| 109 |
+
|
| 110 |
+
## Debug Metrics
|
| 111 |
+
|
| 112 |
+
- `train_loss`: `6.491608619689941`
|
| 113 |
+
- `train_loss_ema`: `6.519052214060582`
|
| 114 |
+
- `val_loss`: `6.646750174145564`
|
| 115 |
+
- `contrastive_loss`: `0.5768923759460449`
|
| 116 |
+
- `val_ssl_loss`: `1.0397093277424574`
|
| 117 |
+
- `variance_loss`: `0.00213623046875`
|
| 118 |
+
- `covariance_loss`: `10.012618064880371`
|
| 119 |
+
- `structure_bce_loss`: `0.08383000642061234`
|
| 120 |
+
- `structure_retrieval_loss`: `10.630162239074707`
|
| 121 |
+
- `val_structure_retrieval_loss`: `10.882395941764116`
|
| 122 |
+
- `structure_fingerprint_cosine`: `0.46021854877471924`
|
| 123 |
+
- `structure_hit_at_1`: `0.00390625`
|
| 124 |
+
- `structure_hit_at_5`: `0.029296875`
|
| 125 |
+
- `structure_hit_at_20`: `0.1171875`
|
| 126 |
+
- `structure_target_rank_mean`: `314.703125`
|
| 127 |
+
- `structure_logit_margin_mean`: `-1.0078125`
|
| 128 |
+
- `structure_retrieval_logit_std`: `0.9839304089546204`
|
| 129 |
+
- `val_structure_retrieval_logit_std`: `2.6622845763340592`
|
| 130 |
+
- `structure_correct_logit_mean`: `9.1875`
|
| 131 |
+
- `structure_top_negative_logit_mean`: `10.1875`
|
| 132 |
+
- `val_structure_correct_logit_mean`: `9.49462890625`
|
| 133 |
+
- `val_structure_top_negative_logit_mean`: `10.714599609375`
|
| 134 |
+
- `structure_top1_top2_gap_mean`: `0.0986328125`
|
| 135 |
+
- `structure_top1_top2_gap_norm_mean`: `0.10155132412910461`
|
| 136 |
+
- `structure_confidence_candidate_count`: `2126.0`
|
| 137 |
+
- `structure_close_fraction`: `0.029296875`
|
| 138 |
+
- `structure_uncertain_fraction`: `0.970703125`
|
| 139 |
+
- `structure_uncertain_hit_at_1`: `0.0040241447277367115`
|
| 140 |
+
- `structure_confidence_separation`: `-0.0040241447277367115`
|
| 141 |
+
- `val_structure_top1_top2_gap_mean`: `0.11855697631835938`
|
| 142 |
+
- `val_structure_top1_top2_gap_norm_mean`: `0.05411380449368153`
|
| 143 |
+
- `val_structure_confidence_candidate_count`: `4096.0`
|
| 144 |
+
- `val_structure_close_fraction`: `0.0077972412109375`
|
| 145 |
+
- `val_structure_uncertain_fraction`: `0.9922027587890625`
|
| 146 |
+
- `val_structure_close_hit_at_1`: `0.014835291571216658`
|
| 147 |
+
- `val_structure_uncertain_hit_at_1`: `0.027957430157584895`
|
| 148 |
+
- `val_structure_confidence_separation`: `-0.027957430157584895`
|
| 149 |
+
- `grad_norm`: `1.7659728527069092`
|
| 150 |
+
- `weighted_regularizer_to_contrastive_ratio`: `0.028996589606316913`
|
| 151 |
+
- `samples_per_sec`: `1399.0203442084614`
|
| 152 |
+
- `samples_per_sec_60s`: `1399.0203442084614`
|
| 153 |
+
- `samples_per_sec_300s`: `1547.033197547462`
|
| 154 |
+
- `samples_per_sec_900s`: `1512.7885250414204`
|
| 155 |
+
- `samples_per_sec_run_mean`: `1533.0231795943853`
|
| 156 |
+
- `gpu_max_memory_gb`: `13.279157161712646`
|
| 157 |
+
- `gpu_util_mean_pct`: `36.0`
|
| 158 |
+
- `gpu_util_min_pct`: `18.0`
|
| 159 |
+
- `gpu_util_max_pct`: `54.0`
|
| 160 |
+
- `gpu_active_fraction`: `1.0`
|
| 161 |
+
- `gpu_mem_used_mean_gb`: `14.1943359375`
|
| 162 |
+
- `gpu_mem_util_mean_pct`: `14.848754175733243`
|
| 163 |
+
- `gpu_snapshot_available`: `1.0`
|
| 164 |
+
- `step_time_sec`: `0.3659703749981418`
|
| 165 |
+
- `step_time_cv_300s`: `0.06743440395418321`
|
| 166 |
+
- `step_time_cv_900s`: `0.09218780075615579`
|
| 167 |
+
- `dataloader_wait_pct_60s`: `0.0961140629935623`
|
| 168 |
+
- `dataloader_wait_pct_300s`: `0.0382592069912683`
|
| 169 |
+
- `dataloader_wait_pct_900s`: `0.066640439237091`
|
| 170 |
+
- `dataloader_wait_pct_run`: `0.06717802801040498`
|
| 171 |
+
- `mfu_estimate`: `0.06267708747552383`
|
metadata/training_surface.json
ADDED
|
@@ -0,0 +1,68 @@
|
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|
| 1 |
+
{
|
| 2 |
+
"created_at": "2026-04-13T20:20:55Z",
|
| 3 |
+
"run_name": "final_v3_structure_alignment_v26_r1",
|
| 4 |
+
"wandb_project": "Nexa_Mol",
|
| 5 |
+
"wandb_run_name": "final_v3_structure_alignment_v26_r1",
|
| 6 |
+
"wandb_group": "Structure-Alignment",
|
| 7 |
+
"dataset_root": "/home/ubuntu/data/gems_v1_handoff_labeled_v26",
|
| 8 |
+
"selected_shards_json": "/home/ubuntu/data/gems_v1_handoff_labeled_v26/selected_shards.json",
|
| 9 |
+
"selected_shards_exists": true,
|
| 10 |
+
"phase_ready_marker": "",
|
| 11 |
+
"phase_ready_exists": false,
|
| 12 |
+
"phase_receipt_path": "",
|
| 13 |
+
"phase_receipt_exists": false,
|
| 14 |
+
"train_shard_surface_mode": "train_only",
|
| 15 |
+
"refresh_selected_shards_on_iterator_reset": false,
|
| 16 |
+
"parent_policy": "local_only",
|
| 17 |
+
"labeled_eval_root": "",
|
| 18 |
+
"query_dataset_root": "",
|
| 19 |
+
"bank_dataset_root": "",
|
| 20 |
+
"primary_checkpoint_run_name": "",
|
| 21 |
+
"fallback_checkpoint_run_name": "",
|
| 22 |
+
"local_parent_checkpoint": "/home/ubuntu/artifacts/nexa_mol/v1_full_pretrain/phase1_blackwell_v3/Final_V3.pt",
|
| 23 |
+
"current_checkpoint_path": "",
|
| 24 |
+
"current_checkpoint_exists": true,
|
| 25 |
+
"disable_auto_resume": true,
|
| 26 |
+
"resume_model_only": true,
|
| 27 |
+
"objective": "hybrid",
|
| 28 |
+
"ssl_weight": 0.05,
|
| 29 |
+
"structure_weight": 1.0,
|
| 30 |
+
"structure_post_saturation_weight": 0.0,
|
| 31 |
+
"structure_threshold": 0.0,
|
| 32 |
+
"structure_lock_patience": 0,
|
| 33 |
+
"train_retrieval_watch_only": false,
|
| 34 |
+
"freeze_retrieval_head": false,
|
| 35 |
+
"retrieval_weight": 0.6,
|
| 36 |
+
"batch_size": 512,
|
| 37 |
+
"eval_batch_size": 512,
|
| 38 |
+
"num_workers": 12,
|
| 39 |
+
"grad_accum_steps": 1,
|
| 40 |
+
"loader_prefetch_factor": 4,
|
| 41 |
+
"log_every": 20,
|
| 42 |
+
"sync_every": 1000,
|
| 43 |
+
"amp_mode": "",
|
| 44 |
+
"additional_steps": 10000,
|
| 45 |
+
"max_steps": 91001,
|
| 46 |
+
"eval_batches": 24,
|
| 47 |
+
"eval_batches_per_shard": 0,
|
| 48 |
+
"eval_start_after_steps": 0,
|
| 49 |
+
"deep_eval_every": 2000,
|
| 50 |
+
"deep_eval_start_after_steps": 2000,
|
| 51 |
+
"deep_eval_batches": 256,
|
| 52 |
+
"deep_eval_single_view": false,
|
| 53 |
+
"deep_eval_structure_only": false,
|
| 54 |
+
"active_train_shards": 0,
|
| 55 |
+
"prefetch_train_shards": 0,
|
| 56 |
+
"val_shards": 0,
|
| 57 |
+
"test_shards": 0,
|
| 58 |
+
"train_offset": 0,
|
| 59 |
+
"val_offset": 0,
|
| 60 |
+
"test_offset": 0,
|
| 61 |
+
"selected_shards_summary": {
|
| 62 |
+
"train_count": 4,
|
| 63 |
+
"val_count": 2,
|
| 64 |
+
"test_count": 2,
|
| 65 |
+
"prefetch_count": 0,
|
| 66 |
+
"subset_strategy": "v26_native_confidence_policy_subset"
|
| 67 |
+
}
|
| 68 |
+
}
|
model_index.json
ADDED
|
@@ -0,0 +1,48 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"model-index": [
|
| 3 |
+
{
|
| 4 |
+
"name": "NexaMass-V3-Struct",
|
| 5 |
+
"results": [
|
| 6 |
+
{
|
| 7 |
+
"dataset": {
|
| 8 |
+
"name": "GeMS-derived labeled validation surface",
|
| 9 |
+
"type": "roman-bushuiev/GeMS"
|
| 10 |
+
},
|
| 11 |
+
"metrics": [
|
| 12 |
+
{
|
| 13 |
+
"name": "Validation structure BCE",
|
| 14 |
+
"type": "binary_cross_entropy",
|
| 15 |
+
"value": 0.0653
|
| 16 |
+
},
|
| 17 |
+
{
|
| 18 |
+
"name": "Validation fingerprint cosine",
|
| 19 |
+
"type": "cosine_similarity",
|
| 20 |
+
"value": 0.4255
|
| 21 |
+
}
|
| 22 |
+
],
|
| 23 |
+
"task": {
|
| 24 |
+
"name": "MS/MS structure-aware representation learning",
|
| 25 |
+
"type": "feature-extraction"
|
| 26 |
+
}
|
| 27 |
+
},
|
| 28 |
+
{
|
| 29 |
+
"dataset": {
|
| 30 |
+
"name": "MassSpecGym test dataloader",
|
| 31 |
+
"type": "massspecgym"
|
| 32 |
+
},
|
| 33 |
+
"metrics": [
|
| 34 |
+
{
|
| 35 |
+
"name": "Hit@20",
|
| 36 |
+
"type": "hit@20",
|
| 37 |
+
"value": 0.3505
|
| 38 |
+
}
|
| 39 |
+
],
|
| 40 |
+
"task": {
|
| 41 |
+
"name": "MassSpecGym adapter sanity check",
|
| 42 |
+
"type": "retrieval"
|
| 43 |
+
}
|
| 44 |
+
}
|
| 45 |
+
]
|
| 46 |
+
}
|
| 47 |
+
]
|
| 48 |
+
}
|
runtime/README.md
ADDED
|
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# NexaMass Runtime
|
| 2 |
+
|
| 3 |
+
`nexamass_encoder.py` is a minimal PyTorch loader for the model-state files in this repository.
|
| 4 |
+
|
| 5 |
+
Recommended checkpoint for inference:
|
| 6 |
+
|
| 7 |
+
```python
|
| 8 |
+
from runtime.nexamass_encoder import ModelConfig, load_nexamass_model_state
|
| 9 |
+
|
| 10 |
+
model = load_nexamass_model_state("weights/NexaMass-V3-Struct-model_state.pt", ModelConfig()) # PyTorch fallback
|
| 11 |
+
# Safetensors weights are also provided for safer tensor-only loading: weights/NexaMass-V3-Struct-model_state.safetensors
|
| 12 |
+
```
|
| 13 |
+
|
| 14 |
+
Use `weights/NexaMass-V3-Struct-model_state.safetensors` for the RDKit/Morgan-aligned model and `weights/Final_V3-model_state.safetensors` for the self-supervised foundation parent. PyTorch model-state `.pt` fallbacks are also included. Optimizer-bearing full training checkpoints remain in object storage and are intentionally excluded from this public HF payload.
|
runtime/nexamass_encoder.py
ADDED
|
@@ -0,0 +1,139 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
from __future__ import annotations
|
| 2 |
+
|
| 3 |
+
from dataclasses import dataclass
|
| 4 |
+
|
| 5 |
+
import torch
|
| 6 |
+
import torch.nn as nn
|
| 7 |
+
import torch.nn.functional as F
|
| 8 |
+
|
| 9 |
+
|
| 10 |
+
@dataclass(frozen=True)
|
| 11 |
+
class ModelConfig:
|
| 12 |
+
max_peaks: int = 256
|
| 13 |
+
mz_max: float = 2000.0
|
| 14 |
+
collision_max: float = 200.0
|
| 15 |
+
model_dim: int = 384
|
| 16 |
+
layers: int = 6
|
| 17 |
+
heads: int = 8
|
| 18 |
+
dropout: float = 0.1
|
| 19 |
+
projection_dim: int = 192
|
| 20 |
+
fingerprint_dim: int = 2048
|
| 21 |
+
target_projection_dim: int = 256
|
| 22 |
+
retrieval_mlp_hidden_dim: int = 512
|
| 23 |
+
metadata_scale: float = 0.02
|
| 24 |
+
|
| 25 |
+
|
| 26 |
+
class NexaMassSpectralEncoder(nn.Module):
|
| 27 |
+
"""Encoder-only MS/MS transformer used by NexaMass-V3-Struct.
|
| 28 |
+
|
| 29 |
+
Expected batch keys:
|
| 30 |
+
- mzs, ints, mz_to_precursor, peak_rank: float tensors [batch, max_peaks]
|
| 31 |
+
- precursor_mz, charge, collision_energy, peak_count: float tensors [batch]
|
| 32 |
+
- adduct_id, instrument_id: long tensors [batch]
|
| 33 |
+
- mask: bool tensor [batch, max_peaks], True for valid peaks
|
| 34 |
+
"""
|
| 35 |
+
|
| 36 |
+
def __init__(self, cfg: ModelConfig) -> None:
|
| 37 |
+
super().__init__()
|
| 38 |
+
self.cfg = cfg
|
| 39 |
+
self.adduct_embedding = nn.Embedding(64, cfg.model_dim)
|
| 40 |
+
self.instrument_embedding = nn.Embedding(64, cfg.model_dim)
|
| 41 |
+
self.input_projection = nn.Linear(8, cfg.model_dim)
|
| 42 |
+
encoder_layer = nn.TransformerEncoderLayer(
|
| 43 |
+
d_model=cfg.model_dim,
|
| 44 |
+
nhead=cfg.heads,
|
| 45 |
+
dim_feedforward=cfg.model_dim * 4,
|
| 46 |
+
dropout=cfg.dropout,
|
| 47 |
+
activation="gelu",
|
| 48 |
+
batch_first=True,
|
| 49 |
+
norm_first=True,
|
| 50 |
+
)
|
| 51 |
+
try:
|
| 52 |
+
self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=cfg.layers, enable_nested_tensor=False)
|
| 53 |
+
except TypeError:
|
| 54 |
+
self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=cfg.layers)
|
| 55 |
+
self.final_norm = nn.LayerNorm(cfg.model_dim)
|
| 56 |
+
self.projection = nn.Sequential(
|
| 57 |
+
nn.Linear(cfg.model_dim, cfg.model_dim),
|
| 58 |
+
nn.GELU(),
|
| 59 |
+
nn.Dropout(cfg.dropout),
|
| 60 |
+
nn.Linear(cfg.model_dim, cfg.projection_dim),
|
| 61 |
+
)
|
| 62 |
+
self.structure_head = nn.Sequential(
|
| 63 |
+
nn.Linear(cfg.model_dim, cfg.model_dim),
|
| 64 |
+
nn.GELU(),
|
| 65 |
+
nn.Dropout(cfg.dropout),
|
| 66 |
+
nn.Linear(cfg.model_dim, cfg.fingerprint_dim),
|
| 67 |
+
)
|
| 68 |
+
self.structure_query = nn.Sequential(
|
| 69 |
+
nn.Linear(cfg.model_dim, cfg.model_dim),
|
| 70 |
+
nn.GELU(),
|
| 71 |
+
nn.Dropout(cfg.dropout),
|
| 72 |
+
nn.Linear(cfg.model_dim, cfg.target_projection_dim),
|
| 73 |
+
)
|
| 74 |
+
self.target_projection = nn.Sequential(
|
| 75 |
+
nn.Linear(cfg.fingerprint_dim, cfg.model_dim),
|
| 76 |
+
nn.GELU(),
|
| 77 |
+
nn.Dropout(cfg.dropout),
|
| 78 |
+
nn.Linear(cfg.model_dim, cfg.target_projection_dim),
|
| 79 |
+
)
|
| 80 |
+
self.retrieval_bilinear = nn.Linear(cfg.target_projection_dim, cfg.target_projection_dim, bias=False)
|
| 81 |
+
self.retrieval_pair_mlp = nn.Sequential(
|
| 82 |
+
nn.Linear(cfg.target_projection_dim * 4, cfg.retrieval_mlp_hidden_dim),
|
| 83 |
+
nn.GELU(),
|
| 84 |
+
nn.Dropout(cfg.dropout),
|
| 85 |
+
nn.Linear(cfg.retrieval_mlp_hidden_dim, 1),
|
| 86 |
+
)
|
| 87 |
+
self.local_rerank_mlp = nn.Sequential(
|
| 88 |
+
nn.Linear(cfg.target_projection_dim * 4 + 1, cfg.retrieval_mlp_hidden_dim),
|
| 89 |
+
nn.GELU(),
|
| 90 |
+
nn.Dropout(cfg.dropout),
|
| 91 |
+
nn.Linear(cfg.retrieval_mlp_hidden_dim, 1),
|
| 92 |
+
)
|
| 93 |
+
|
| 94 |
+
def encode(self, batch: dict[str, torch.Tensor]) -> torch.Tensor:
|
| 95 |
+
features = torch.stack(
|
| 96 |
+
[
|
| 97 |
+
batch["mzs"],
|
| 98 |
+
batch["ints"],
|
| 99 |
+
batch["mz_to_precursor"],
|
| 100 |
+
batch["peak_rank"],
|
| 101 |
+
batch["precursor_mz"].unsqueeze(-1).expand_as(batch["mzs"]),
|
| 102 |
+
batch["charge"].unsqueeze(-1).expand_as(batch["mzs"]),
|
| 103 |
+
batch["collision_energy"].unsqueeze(-1).expand_as(batch["mzs"]),
|
| 104 |
+
batch["peak_count"].unsqueeze(-1).expand_as(batch["mzs"]),
|
| 105 |
+
],
|
| 106 |
+
dim=-1,
|
| 107 |
+
)
|
| 108 |
+
hidden = self.input_projection(features)
|
| 109 |
+
hidden = hidden + self.adduct_embedding(batch["adduct_id"])[:, None, :] * self.cfg.metadata_scale
|
| 110 |
+
hidden = hidden + self.instrument_embedding(batch["instrument_id"])[:, None, :] * self.cfg.metadata_scale
|
| 111 |
+
encoded = self.encoder(hidden, src_key_padding_mask=~batch["mask"])
|
| 112 |
+
encoded = self.final_norm(encoded)
|
| 113 |
+
mask = batch["mask"].unsqueeze(-1)
|
| 114 |
+
return (encoded * mask).sum(dim=1) / mask.sum(dim=1).clamp(min=1)
|
| 115 |
+
|
| 116 |
+
def forward_with_heads(
|
| 117 |
+
self, batch: dict[str, torch.Tensor]
|
| 118 |
+
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
|
| 119 |
+
pooled = self.encode(batch)
|
| 120 |
+
raw_projected = self.projection(pooled)
|
| 121 |
+
structure_logits = self.structure_head(pooled)
|
| 122 |
+
structure_query_raw = self.structure_query(pooled)
|
| 123 |
+
return F.normalize(raw_projected, dim=-1), raw_projected, structure_logits, structure_query_raw
|
| 124 |
+
|
| 125 |
+
def project_structure_targets(self, targets: torch.Tensor) -> torch.Tensor:
|
| 126 |
+
return F.normalize(self.target_projection(targets), dim=-1)
|
| 127 |
+
|
| 128 |
+
|
| 129 |
+
def load_nexamass_model_state(
|
| 130 |
+
checkpoint_path: str,
|
| 131 |
+
cfg: ModelConfig | None = None,
|
| 132 |
+
map_location: str | torch.device = "cpu",
|
| 133 |
+
) -> NexaMassSpectralEncoder:
|
| 134 |
+
payload = torch.load(checkpoint_path, map_location=map_location)
|
| 135 |
+
cfg = cfg or ModelConfig()
|
| 136 |
+
model = NexaMassSpectralEncoder(cfg)
|
| 137 |
+
model.load_state_dict(payload["model_state"], strict=True)
|
| 138 |
+
model.eval()
|
| 139 |
+
return model
|
weights/Final_V3-model_state.pt
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:c16f3bb2ab7c2612b6b84a3359abeaf0a1cb1c055399beac21b39521424328b5
|
| 3 |
+
size 56473018
|
weights/Final_V3-model_state.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:870b30c7bb6a340a460306882fe631d76282f233baf3920ca3a45c363eaf0ee6
|
| 3 |
+
size 56437128
|
weights/NexaMass-V3-Struct-model_state.pt
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:bde396a7448b6972bcb784a465812b4d5b819f8d0c76cf2fba7066db7be9da1f
|
| 3 |
+
size 56474172
|
weights/NexaMass-V3-Struct-model_state.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:6e006a6fa4ba6b8f127c7eabc9eaf6b7b64675e0136dee3ca1655bb3a4a3eadd
|
| 3 |
+
size 56437136
|