Export trained reference models

README.md

@@ -13,7 +13,7 @@ Unconditional reference models trained with gendynamics. The weights correspond
 ## Dataset
 
 Synthetic isotropic alpha-stable samples. Generated by `gendynamics.datasets.fetch_synthetic_data('alpha_stable')`. Sample the configured distribution, then make a seeded random train/validation/test split. 
-The train/validation/test splits contain 10,000/1,000/10,000 samples
+The train/validation/test splits contain 25,000/1,000/10,000 samples
 of shape `[150]`. Normalization: None 
 The exact generator arguments or processed-file identity, split settings, and seed are in `dataset.json`.
 The source data themselves are not redistributed here; source-data terms remain with their provider.
@@ -39,9 +39,9 @@ All independently trained models use this network architecture:
 
 | Directory | Model | Prediction target | Trainable parameters | Sampling steps | Learning rate | Epochs run / best | Val loss | Test loss |
 |---|---|---|---:|---:|---:|---:|---:|---:|
-| ddpm-v | DDPM-V | velocity | 693,894 | 128 | 0.0001 | 322 / 222 | 20.9 | 13.22 |
-| dlpm-eps | DLPM-Eps | noise | 693,894 | 128 | 0.0005 | 640 / 560 | 0.961 | 1.018 |
-| tedm-origin | t-EDM | denoised data | 693,894 | 64 | 0.0005 | 147 / 47 | 1.75 | 4.618 |
+| ddpm-v | DDPM-V | velocity | 693,894 | 128 | 0.0005 | 640 / 588 | 31.33 | 10.01 |
+| dlpm-eps | DLPM-Eps | noise | 693,894 | 128 | 0.0005 | 311 / 211 | 1.009 | 1.12 |
+| tedm-origin | t-EDM | denoised data | 693,894 | 64 | 0.0005 | 132 / 32 | 2.061 | 6.193 |
 
 The complete **selected final** model and training settings are below and in each directory's `config.json`.
 The training settings include the maximum epoch budget; `epochs run / best` reports what actually happened.
@@ -64,7 +64,7 @@ The number of held-out rows used for each test loss is in `metrics.json`. Sample
     "batch_size": 1024,
     "n_epochs": 640,
     "early_stopping_patience": 100,
-    "lr": 0.0001,
+    "lr": 0.0005,
     "num_workers": 0,
     "use_adamw": true,
     "weight_decay": 1e-06,
@@ -86,7 +86,7 @@ The number of held-out rows used for each test loss is in `metrics.json`. Sample
 {
   "model_parameters": {
     "n_steps": 128,
-    "alpha": 1.9,
+    "alpha": 1.8,
     "sampler": "native"
   },
   "training": {
@@ -117,7 +117,7 @@ The number of held-out rows used for each test loss is in `metrics.json`. Sample
 {
   "model_parameters": {
     "n_steps": 64,
-    "nu": 2.1,
+    "nu": 2.2,
     "sigma_min": 0.005,
     "sigma_max": 5.0,
     "sigma_data": 1.0,

ddim-v/config.json

@@ -9,13 +9,13 @@
       "kind": "synthetic",
       "name": "alpha_stable",
       "params": {
-        "n_samples": 21000,
+        "n_samples": 36000,
         "alpha": 1.7,
         "dim": 150
       },
       "split": {
-        "val_size": 0.047619047619047616,
-        "test_size": 0.47619047619047616,
+        "val_size": 0.027777777777777776,
+        "test_size": 0.2777777777777778,
         "random_state": 0,
         "standardize": false
       }
@@ -41,14 +41,14 @@
       "dlpm_eps": {
         "params": {
           "n_steps": 128,
-          "alpha": 1.7,
+          "alpha": 1.8,
           "sampler": "native"
         }
       },
       "tedm_origin": {
         "params": {
           "n_steps": 64,
-          "nu": 2.1,
+          "nu": 2.2,
           "sigma_min": 0.005,
           "sigma_max": 5.0,
           "sigma_data": 1.0,
@@ -82,12 +82,6 @@
           }
         },
         "dlpm_eps": {
-          "model": {
-            "alpha": [
-              1.7,
-              1.9
-            ]
-          },
           "train": {
             "lr": [
               0.0001,
@@ -97,12 +91,6 @@
           }
         },
         "tedm_origin": {
-          "model": {
-            "nu": [
-              2.1,
-              3.0
-            ]
-          },
           "train": {
             "lr": [
               0.0001,
@@ -119,7 +107,7 @@
       "batch_size": 1024,
       "n_epochs": 640,
       "early_stopping_patience": 100,
-      "lr": 0.0001,
+      "lr": 0.0005,
       "num_workers": 0,
       "use_adamw": true,
       "weight_decay": 1e-06,
@@ -154,5 +142,5 @@
   "sample_shape": [
     150
   ],
-  "data_scale": 0.960388184664899
+  "data_scale": 0.9605196847815501
 }

ddim-v/dataset.json

@@ -2,23 +2,23 @@
   "name": "alpha_stable",
   "kind": "synthetic",
   "params": {
-    "n_samples": 21000,
+    "n_samples": 36000,
     "alpha": 1.7,
     "dim": 150
   },
   "split": {
-    "val_size": 0.047619047619047616,
-    "test_size": 0.47619047619047616,
+    "val_size": 0.027777777777777776,
+    "test_size": 0.2777777777777778,
     "random_state": 0,
     "standardize": false
   },
   "split_mode": "random",
   "counts": {
-    "train": 10000,
+    "train": 25000,
     "validation": 1000,
     "test": 10000
   },
-  "n_samples": 21000,
+  "n_samples": 36000,
   "sample_shape": [
     150
   ],

ddim-v/export_check.json

@@ -10,9 +10,9 @@
   "device": "cpu",
   "model_dtype": "torch.float64",
   "sample_dtype": "torch.float32",
-  "min": -6.404818534851074,
-  "max": 6.515822410583496,
-  "std": 1.9640422383909977,
+  "min": -6.233613967895508,
+  "max": 5.796941757202148,
+  "std": 1.7909797481083234,
   "clamp_min": null,
   "clipped_fraction": 0.0,
   "scope": "Shape and numerical validity only; not distribution quality or tail accuracy.",

ddim-v/metrics.json

@@ -1,6 +1,6 @@
 {
-  "best_epoch": 222,
-  "validation_loss": 20.89935951180678,
+  "best_epoch": 588,
+  "validation_loss": 31.326765199195084,
   "test_loss_samples": 10000,
-  "test_loss": 13.220318937183922
+  "test_loss": 10.014018427903736
 }

ddim-v/model.safetensors

@@ -1,3 +1,3 @@
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 size 5554560

ddim-v/provenance.json

@@ -1,5 +1,5 @@
 {
-  "recorded_at_utc": "2026-10-04T00:15:46.833244+00:00",
+  "recorded_at_utc": "2026-10-04T04:36:27.690018+00:00",
   "python": "3.12.11",
   "platform": "Linux-5.14.0-570.131.1.el9_6.x86_64-x86_64-with-glibc2.34",
   "device": "cuda",

ddpm-v/config.json

@@ -9,13 +9,13 @@
       "kind": "synthetic",
       "name": "alpha_stable",
       "params": {
-        "n_samples": 21000,
+        "n_samples": 36000,
         "alpha": 1.7,
         "dim": 150
       },
       "split": {
-        "val_size": 0.047619047619047616,
-        "test_size": 0.47619047619047616,
+        "val_size": 0.027777777777777776,
+        "test_size": 0.2777777777777778,
         "random_state": 0,
         "standardize": false
       }
@@ -41,14 +41,14 @@
       "dlpm_eps": {
         "params": {
           "n_steps": 128,
-          "alpha": 1.7,
+          "alpha": 1.8,
           "sampler": "native"
         }
       },
       "tedm_origin": {
         "params": {
           "n_steps": 64,
-          "nu": 2.1,
+          "nu": 2.2,
           "sigma_min": 0.005,
           "sigma_max": 5.0,
           "sigma_data": 1.0,
@@ -82,12 +82,6 @@
           }
         },
         "dlpm_eps": {
-          "model": {
-            "alpha": [
-              1.7,
-              1.9
-            ]
-          },
           "train": {
             "lr": [
               0.0001,
@@ -97,12 +91,6 @@
           }
         },
         "tedm_origin": {
-          "model": {
-            "nu": [
-              2.1,
-              3.0
-            ]
-          },
           "train": {
             "lr": [
               0.0001,
@@ -119,7 +107,7 @@
       "batch_size": 1024,
       "n_epochs": 640,
       "early_stopping_patience": 100,
-      "lr": 0.0001,
+      "lr": 0.0005,
       "num_workers": 0,
       "use_adamw": true,
       "weight_decay": 1e-06,
@@ -152,5 +140,5 @@
   "sample_shape": [
     150
   ],
-  "data_scale": 0.960388184664899
+  "data_scale": 0.9605196847815501
 }

ddpm-v/dataset.json

@@ -2,23 +2,23 @@
   "name": "alpha_stable",
   "kind": "synthetic",
   "params": {
-    "n_samples": 21000,
+    "n_samples": 36000,
     "alpha": 1.7,
     "dim": 150
   },
   "split": {
-    "val_size": 0.047619047619047616,
-    "test_size": 0.47619047619047616,
+    "val_size": 0.027777777777777776,
+    "test_size": 0.2777777777777778,
     "random_state": 0,
     "standardize": false
   },
   "split_mode": "random",
   "counts": {
-    "train": 10000,
+    "train": 25000,
     "validation": 1000,
     "test": 10000
   },
-  "n_samples": 21000,
+  "n_samples": 36000,
   "sample_shape": [
     150
   ],

ddpm-v/export_check.json

@@ -10,9 +10,9 @@
   "device": "cpu",
   "model_dtype": "torch.float64",
   "sample_dtype": "torch.float32",
-  "min": -5.344755172729492,
-  "max": 5.205076694488525,
-  "std": 1.8227817269334758,
+  "min": -4.0817179679870605,
+  "max": 4.098446846008301,
+  "std": 1.4471645552985881,
   "clamp_min": null,
   "clipped_fraction": 0.0,
   "scope": "Shape and numerical validity only; not distribution quality or tail accuracy.",

ddpm-v/metrics.json

@@ -1,6 +1,6 @@
 {
-  "best_epoch": 222,
-  "validation_loss": 20.89935951180678,
+  "best_epoch": 588,
+  "validation_loss": 31.326765199195084,
   "test_loss_samples": 10000,
-  "test_loss": 13.220318937183922
+  "test_loss": 10.014018427903736
 }

ddpm-v/model.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:a85e88c859866ad34cab14d34e9f991b2145930de8d986f5b2a5e38c41112b60
 size 5554560

ddpm-v/provenance.json

@@ -1,5 +1,5 @@
 {
-  "recorded_at_utc": "2026-10-04T00:15:46.833244+00:00",
+  "recorded_at_utc": "2026-10-04T04:36:27.690018+00:00",
   "python": "3.12.11",
   "platform": "Linux-5.14.0-570.131.1.el9_6.x86_64-x86_64-with-glibc2.34",
   "device": "cuda",

dlpm-eps/config.json

@@ -9,13 +9,13 @@
       "kind": "synthetic",
       "name": "alpha_stable",
       "params": {
-        "n_samples": 21000,
+        "n_samples": 36000,
         "alpha": 1.7,
         "dim": 150
       },
       "split": {
-        "val_size": 0.047619047619047616,
-        "test_size": 0.47619047619047616,
+        "val_size": 0.027777777777777776,
+        "test_size": 0.2777777777777778,
         "random_state": 0,
         "standardize": false
       }
@@ -41,14 +41,14 @@
       "dlpm_eps": {
         "params": {
           "n_steps": 128,
-          "alpha": 1.9,
+          "alpha": 1.8,
           "sampler": "native"
         }
       },
       "tedm_origin": {
         "params": {
           "n_steps": 64,
-          "nu": 2.1,
+          "nu": 2.2,
           "sigma_min": 0.005,
           "sigma_max": 5.0,
           "sigma_data": 1.0,
@@ -82,12 +82,6 @@
           }
         },
         "dlpm_eps": {
-          "model": {
-            "alpha": [
-              1.7,
-              1.9
-            ]
-          },
           "train": {
             "lr": [
               0.0001,
@@ -97,12 +91,6 @@
           }
         },
         "tedm_origin": {
-          "model": {
-            "nu": [
-              2.1,
-              3.0
-            ]
-          },
           "train": {
             "lr": [
               0.0001,
@@ -146,11 +134,11 @@
   "model_name": "dlpm_eps",
   "model_params": {
     "n_steps": 128,
-    "alpha": 1.9,
+    "alpha": 1.8,
     "sampler": "native"
   },
   "sample_shape": [
     150
   ],
-  "data_scale": 0.960388184664899
+  "data_scale": 0.9605196847815501
 }

dlpm-eps/dataset.json

@@ -2,23 +2,23 @@
   "name": "alpha_stable",
   "kind": "synthetic",
   "params": {
-    "n_samples": 21000,
+    "n_samples": 36000,
     "alpha": 1.7,
     "dim": 150
   },
   "split": {
-    "val_size": 0.047619047619047616,
-    "test_size": 0.47619047619047616,
+    "val_size": 0.027777777777777776,
+    "test_size": 0.2777777777777778,
     "random_state": 0,
     "standardize": false
   },
   "split_mode": "random",
   "counts": {
-    "train": 10000,
+    "train": 25000,
     "validation": 1000,
     "test": 10000
   },
-  "n_samples": 21000,
+  "n_samples": 36000,
   "sample_shape": [
     150
   ],

dlpm-eps/export_check.json

@@ -10,9 +10,9 @@
   "device": "cpu",
   "model_dtype": "torch.float64",
   "sample_dtype": "torch.float32",
-  "min": -3.3542916774749756,
-  "max": 3.3618104457855225,
-  "std": 1.2538432200388956,
+  "min": -4.972592830657959,
+  "max": 5.022611141204834,
+  "std": 1.6282988876023636,
   "clamp_min": null,
   "clipped_fraction": 0.0,
   "scope": "Shape and numerical validity only; not distribution quality or tail accuracy.",

dlpm-eps/metrics.json

@@ -1,6 +1,6 @@
 {
-  "best_epoch": 560,
-  "validation_loss": 0.9610193009149608,
+  "best_epoch": 211,
+  "validation_loss": 1.0092464005503803,
   "test_loss_samples": 10000,
-  "test_loss": 1.0183712744487767
+  "test_loss": 1.120285918624625
 }

dlpm-eps/model.safetensors

@@ -1,3 +1,3 @@
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 size 5554560

dlpm-eps/provenance.json

@@ -1,5 +1,5 @@
 {
-  "recorded_at_utc": "2026-10-04T00:15:12.196121+00:00",
+  "recorded_at_utc": "2026-10-04T04:32:32.762598+00:00",
   "python": "3.12.11",
   "platform": "Linux-5.14.0-570.131.1.el9_6.x86_64-x86_64-with-glibc2.34",
   "device": "cuda",

tedm-origin/config.json

@@ -9,13 +9,13 @@
       "kind": "synthetic",
       "name": "alpha_stable",
       "params": {
-        "n_samples": 21000,
+        "n_samples": 36000,
         "alpha": 1.7,
         "dim": 150
       },
       "split": {
-        "val_size": 0.047619047619047616,
-        "test_size": 0.47619047619047616,
+        "val_size": 0.027777777777777776,
+        "test_size": 0.2777777777777778,
         "random_state": 0,
         "standardize": false
       }
@@ -41,14 +41,14 @@
       "dlpm_eps": {
         "params": {
           "n_steps": 128,
-          "alpha": 1.7,
+          "alpha": 1.8,
           "sampler": "native"
         }
       },
       "tedm_origin": {
         "params": {
           "n_steps": 64,
-          "nu": 2.1,
+          "nu": 2.2,
           "sigma_min": 0.005,
           "sigma_max": 5.0,
           "sigma_data": 1.0,
@@ -82,12 +82,6 @@
           }
         },
         "dlpm_eps": {
-          "model": {
-            "alpha": [
-              1.7,
-              1.9
-            ]
-          },
           "train": {
             "lr": [
               0.0001,
@@ -97,12 +91,6 @@
           }
         },
         "tedm_origin": {
-          "model": {
-            "nu": [
-              2.1,
-              3.0
-            ]
-          },
           "train": {
             "lr": [
               0.0001,
@@ -146,7 +134,7 @@
   "model_name": "tedm_origin",
   "model_params": {
     "n_steps": 64,
-    "nu": 2.1,
+    "nu": 2.2,
     "sigma_min": 0.005,
     "sigma_max": 5.0,
     "sigma_data": 1.0,
@@ -155,5 +143,5 @@
   "sample_shape": [
     150
   ],
-  "data_scale": 0.960388184664899
+  "data_scale": 0.9605196847815501
 }

tedm-origin/dataset.json

@@ -2,23 +2,23 @@
   "name": "alpha_stable",
   "kind": "synthetic",
   "params": {
-    "n_samples": 21000,
+    "n_samples": 36000,
     "alpha": 1.7,
     "dim": 150
   },
   "split": {
-    "val_size": 0.047619047619047616,
-    "test_size": 0.47619047619047616,
+    "val_size": 0.027777777777777776,
+    "test_size": 0.2777777777777778,
     "random_state": 0,
     "standardize": false
   },
   "split_mode": "random",
   "counts": {
-    "train": 10000,
+    "train": 25000,
     "validation": 1000,
     "test": 10000
   },
-  "n_samples": 21000,
+  "n_samples": 36000,
   "sample_shape": [
     150
   ],

tedm-origin/export_check.json

@@ -10,9 +10,9 @@
   "device": "cpu",
   "model_dtype": "torch.float64",
   "sample_dtype": "torch.float32",
-  "min": -7.146265506744385,
-  "max": 7.430809020996094,
-  "std": 1.7117973315796688,
+  "min": -6.468786239624023,
+  "max": 6.966502666473389,
+  "std": 1.6643134203224617,
   "clamp_min": null,
   "clipped_fraction": 0.0,
   "scope": "Shape and numerical validity only; not distribution quality or tail accuracy.",

tedm-origin/metrics.json

@@ -1,6 +1,6 @@
 {
-  "best_epoch": 47,
-  "validation_loss": 1.7500572730697816,
+  "best_epoch": 32,
+  "validation_loss": 2.061470013478586,
   "test_loss_samples": 10000,
-  "test_loss": 4.618289811999192
+  "test_loss": 6.193141571252055
 }

tedm-origin/model.safetensors

@@ -1,3 +1,3 @@
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tedm-origin/provenance.json

@@ -1,5 +1,5 @@
 {
-  "recorded_at_utc": "2026-10-04T00:14:58.315595+00:00",
+  "recorded_at_utc": "2026-10-04T04:31:35.427923+00:00",
   "python": "3.12.11",
   "platform": "Linux-5.14.0-570.131.1.el9_6.x86_64-x86_64-with-glibc2.34",
   "device": "cuda",