{
  "bundle_format_version": 1,
  "config_schema_version": 1,
  "extras": {
    "evaluation_scaled_log10_wavelength": {
      "__aet_sidecar__": {
        "format": "safetensors_v1",
        "layout": "single_array_v1",
        "path": "extras/evaluation_scaled_log10_wavelength.safetensors"
      }
    },
    "fixed_parameter_values": {
      "[C/Fe]": 0.0,
      "[N/Fe]": 0.0,
      "[O/Fe]": 0.0,
      "[a/Fe]": 0.0,
      "[r/Fe]": 0.0,
      "[s/Fe]": 0.0
    },
    "normalised_intensity_observed_range": null,
    "notes": "The source array named 'flux' is treated as line intensity. The source array 'continuum' is treated as continuum intensity. The bundle records min-max scaling metadata; log10 transforms for wavelengths and outputs are explicit user-side preprocessing.",
    "output_parameterization": "scaled_log10_flux_and_continuum",
    "parameter_source_units": {
      "[Fe/H]": "dex",
      "logg": "dex",
      "mu": "dimensionless",
      "teff": "K",
      "vmicro": "km/s"
    },
    "preprocessing_recipe": {
      "outputs": "The model predicts min-max scaled log10 flux and min-max scaled log10 continuum. Invert the min-max transform first; apply 10**y outside the bundle if physical intensities are needed.",
      "parameters": "Scale raw parameter vector with (x - parameter_min) / (parameter_max - parameter_min).",
      "wavelengths": "Apply log10 to physical wavelength first, then min-max scale with the log10_wavelength bounds."
    },
    "source_log10_wavelength": {
      "__aet_sidecar__": {
        "format": "safetensors_v1",
        "layout": "single_array_v1",
        "path": "extras/source_log10_wavelength.safetensors"
      }
    },
    "source_log10_wavelength_by_channel": {
      "continuum": {
        "__aet_sidecar__": {
          "format": "safetensors_v1",
          "layout": "single_array_v1",
          "path": "extras/source_log10_wavelength_by_channel/continuum.safetensors"
        }
      },
      "lines": {
        "__aet_sidecar__": {
          "format": "safetensors_v1",
          "layout": "single_array_v1",
          "path": "extras/source_log10_wavelength_by_channel/lines.safetensors"
        }
      }
    },
    "source_parameter_max": [
      8500.0,
      5.0,
      0.5,
      4.0,
      0.0,
      0.0,
      0.0,
      0.0,
      0.0,
      0.0,
      1.0
    ],
    "source_parameter_min": [
      3500.0,
      0.5,
      -4.5,
      1.0,
      0.0,
      0.0,
      0.0,
      0.0,
      0.0,
      0.0,
      0.01001800037920475
    ],
    "source_parameter_names": [
      "teff",
      "logg",
      "[Fe/H]",
      "vmicro",
      "[a/Fe]",
      "[C/Fe]",
      "[N/Fe]",
      "[O/Fe]",
      "[r/Fe]",
      "[s/Fe]",
      "mu"
    ],
    "source_scaled_log10_wavelength": {
      "__aet_sidecar__": {
        "format": "safetensors_v1",
        "layout": "single_array_v1",
        "path": "extras/source_scaled_log10_wavelength.safetensors"
      }
    },
    "source_scaled_log10_wavelength_by_channel": {
      "continuum": {
        "__aet_sidecar__": {
          "format": "safetensors_v1",
          "layout": "single_array_v1",
          "path": "extras/source_scaled_log10_wavelength_by_channel/continuum.safetensors"
        }
      },
      "lines": {
        "__aet_sidecar__": {
          "format": "safetensors_v1",
          "layout": "single_array_v1",
          "path": "extras/source_scaled_log10_wavelength_by_channel/lines.safetensors"
        }
      }
    },
    "source_store": "/g/data/y89/mj8805/new_fe_grid.zarr",
    "source_stores": [
      "/g/data/y89/mj8805/new_fe_grid.zarr"
    ],
    "source_wavelength": {
      "__aet_sidecar__": {
        "format": "safetensors_v1",
        "layout": "single_array_v1",
        "path": "extras/source_wavelength.safetensors"
      }
    },
    "source_wavelength_by_channel": {
      "continuum": {
        "__aet_sidecar__": {
          "format": "safetensors_v1",
          "layout": "single_array_v1",
          "path": "extras/source_wavelength_by_channel/continuum.safetensors"
        }
      },
      "lines": {
        "__aet_sidecar__": {
          "format": "safetensors_v1",
          "layout": "single_array_v1",
          "path": "extras/source_wavelength_by_channel/lines.safetensors"
        }
      }
    },
    "training": {
      "continuum_stride": 1,
      "dataset_output_keys": [
        "lines",
        "continuum"
      ],
      "model_output_channel_names": [
        "log_flux_lines",
        "log_flux_continuum"
      ],
      "n_training_samples": 135000,
      "n_validation_samples": 15000,
      "n_wavelength_per_step": 2048,
      "source_wavelength_samples_by_channel": {
        "continuum": 2001,
        "lines": 2001
      },
      "validation_policy": "first 10.0% of spectra by concatenated source index"
    }
  },
  "fingerprint_evaluation": {
    "atol": 1e-07,
    "inputs": {
      "filename": "fingerprint_evaluation/inputs.safetensors",
      "format": "safetensors_v1",
      "layout": "numeric_dict_tree_v1",
      "space": "canonical_input_dict_trees_v1"
    },
    "kind": "canonical_inputs_outputs_v1",
    "outputs": {
      "filename": "fingerprint_evaluation/outputs.safetensors",
      "format": "safetensors_v1",
      "layout": "numeric_dict_tree_v1",
      "space": "canonical_output_dict_trees_v1"
    },
    "rtol": 1e-05,
    "selection_strategy": "midpoint_from_input_domain_then_reference_scaling_inputs_v1"
  },
  "fit_method": "gradient",
  "model_family_id": "transformer_payne_v1",
  "model_init": {
    "hints": {
      "parameter_dim": 5
    },
    "representation": "model-local init hints only"
  },
  "provenance": {
    "created_at": "2026-05-15T02:55:23.587126+00:00",
    "dependencies": {
      "flax": "0.12.7",
      "jax": "0.10.0",
      "numpy": "2.4.4",
      "optax": "0.2.8"
    },
    "git_commit": null,
    "platform": "Linux-4.18.0-553.117.1.el8.nci.x86_64-x86_64-with-glibc2.28",
    "python_version": "3.12.13",
    "toolkit": "astro_emulators_toolkit",
    "toolkit_version": "0.1.0"
  },
  "release": {
    "name": "maja-new-fe-intensity-tpayne-small-hpc",
    "status": "released",
    "version": "0.1.0"
  },
  "resolved": {
    "model": {
      "name": "transformer_payne",
      "params": {
        "activation": "gelu",
        "alpha_att": 1.0,
        "alpha_emb": 1.0,
        "bias_attention": false,
        "bias_dense": false,
        "bias_feed_forward": null,
        "bias_output_head": null,
        "bias_parameter_embedding": null,
        "channels": 2,
        "dim": 48,
        "dim_ff_multiplier": 2,
        "dim_head": 16,
        "dtype": "float32",
        "emb_init": "si",
        "ff_init": "si",
        "head_init": "si",
        "init_att_o": "si",
        "init_att_q": "si",
        "max_period": 1.0,
        "min_period": 0.0001,
        "no_layers": 3,
        "no_tokens": 8,
        "output_activation": "linear",
        "reference_depth": null,
        "reference_width": null,
        "sigma": 1.0
      }
    },
    "solver": {
      "name": "gradient",
      "params": {}
    },
    "task": {
      "name": "regression",
      "params": {
        "loss": "mse",
        "loss_weights": null,
        "metric_axes": {
          "channel": [
            0
          ],
          "global": "all"
        },
        "metrics": [
          "mse",
          "mae"
        ]
      }
    }
  },
  "runtime_contract": {
    "affine_leaf_specs": {
      "inputs/parameters": {
        "last_axis": 5,
        "mode": "scalar_or_last_axis"
      },
      "inputs/wavelengths": {
        "mode": "scalar_only"
      },
      "outputs/flux": {
        "last_axis": 2,
        "mode": "scalar_or_last_axis"
      }
    },
    "role_paths": {
      "output_leaf": "outputs/flux",
      "parameter_leaf": "inputs/parameters",
      "wavelength_leaf": "inputs/wavelengths"
    },
    "surface": "canonical_dict_trees_v1",
    "transformer_payne_channels": [
      {
        "dataset_key": "lines",
        "name": "log_flux_lines"
      },
      {
        "dataset_key": "continuum",
        "name": "log_flux_continuum"
      }
    ]
  },
  "spec": {
    "input_domain": {
      "kind": "box_v1",
      "max_tree": {
        "parameters": [
          8500.0,
          5.0,
          0.5,
          4.0,
          1.0
        ],
        "wavelengths": 3.7007037171450192
      },
      "min_tree": {
        "parameters": [
          3500.0,
          0.5,
          -4.5,
          1.0,
          0.01001800037920475
        ],
        "wavelengths": 3.6989700043360187
      },
      "storage": {
        "filename": "input_domain.safetensors",
        "format": "safetensors_v1",
        "layout": "split_minmax_tree_v1"
      },
      "value_space": "physical_input_dict_tree_v1"
    },
    "inputs": {
      "channel_meanings_tree": {
        "parameters": [
          "effective temperature",
          "surface gravity",
          "metallicity [Fe/H]",
          "microturbulence velocity",
          "cosine of viewing angle"
        ],
        "wavelengths": null
      },
      "channel_names_tree": {
        "parameters": [
          "teff",
          "logg",
          "[Fe/H]",
          "vmicro",
          "mu"
        ],
        "wavelengths": null
      },
      "channel_units_tree": {
        "parameters": [
          "dimensionless",
          "dimensionless",
          "dimensionless",
          "dimensionless",
          "dimensionless"
        ],
        "wavelengths": null
      },
      "leaf_meanings_tree": {
        "parameters": "min-max scaled model input parameters; see reference_scaling_inputs for raw parameter bounds and bundle_extras.fixed_parameter_values for any source-grid parameters held fixed outside the model input",
        "wavelengths": "min-max scaled log10 wavelength; the user applies log10 before reference_scaling_inputs"
      },
      "leaf_units_tree": {
        "parameters": null,
        "wavelengths": "dimensionless"
      },
      "structure_tree": {
        "parameters": null,
        "wavelengths": null
      }
    },
    "outputs": {
      "channel_meanings_tree": {
        "flux": [
          "min-max scaled log10 line intensity from source array flux",
          "min-max scaled log10 continuum intensity from source array continuum"
        ]
      },
      "channel_names_tree": {
        "flux": [
          "log_flux_lines",
          "log_flux_continuum"
        ]
      },
      "channel_units_tree": {
        "flux": [
          "dimensionless",
          "dimensionless"
        ]
      },
      "leaf_meanings_tree": {
        "flux": "two min-max scaled log10 intensity channels from the Maja archive arrays flux and continuum"
      },
      "leaf_units_tree": {
        "flux": "dimensionless"
      },
      "structure_tree": {
        "flux": null
      }
    },
    "reference_scaling_inputs": {
      "applies_to": "inputs",
      "kind": "affine_minmax_v1",
      "max_tree": {
        "parameters": [
          8500.0,
          5.0,
          0.5,
          4.0,
          1.0
        ],
        "wavelengths": 3.7007037171450192
      },
      "min_tree": {
        "parameters": [
          3500.0,
          0.5,
          -4.5,
          1.0,
          0.01001800037920475
        ],
        "wavelengths": 3.6989700043360187
      },
      "source_space": "physical_input_dict_tree_v1",
      "storage": {
        "filename": "reference_scaling_inputs.safetensors",
        "format": "safetensors_v1",
        "layout": "split_minmax_tree_v1"
      },
      "target_space": "canonical_input_dict_tree_v1"
    },
    "reference_scaling_outputs": {
      "applies_to": "outputs",
      "kind": "affine_minmax_v1",
      "max_tree": {
        "flux": [
          7.281722068786621,
          7.281800746917725
        ]
      },
      "min_tree": {
        "flux": [
          2.2898988723754883,
          4.439985275268555
        ]
      },
      "source_space": "canonical_output_dict_tree_v1",
      "storage": {
        "filename": "reference_scaling_outputs.safetensors",
        "format": "safetensors_v1",
        "layout": "split_minmax_tree_v1"
      },
      "target_space": "physical_output_dict_tree_v1"
    },
    "spec_version": 1
  },
  "weights_layout": "params_plus_model_state_v1"
}