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+# API for pre-training and fine-tuning
+
+Contributors: @fkiraly
+
+## High-level summary 
+
+### The Aim
+
+`sktime` now has a number of estimators that can do pre-training, fine-tuning, global learning, and cross-learning.
+
+However, the current API design for these use cases has a few problems, and repeatedly issues have been opened for a rework.
+
+This STEP is about finalizing a good interface for:
+
+* global forecasting
+* pre-training
+* fine-tuning of foundation models
+* zero-shot use of foundation models
+
+References:
+
+* conceptual design issue: https://github.com/sktime/sktime/issues/6580
+* umbrella issue foundation models: https://github.com/sktime/sktime/issues/6177
+* newer issue: https://github.com/sktime/sktime/issues/7838
+
+### requirements
+
+* design covers the above use cases with a simple interface
+* composability - use of sensible pipelines, tuning, etc should be simple and not require major surgery in current compositors
+* downwards and upwards compatibility - design should not impact current extension contracts
+* maintainability: maintaining the framework and estimators with the above capabilities should be simple
+
+### The proposed solution
+
+Our proposed solution adds a new state, and a simple switch.
+
+No new public methods are added beyond this, and signatures of methods are not modified.
+
+Estimators get a third state, "pretraining phase", besides unfitted and fitted.
+
+The solution is best illustrated in the basic vignette below.
+
+### Discussion of current solutions
+
+There are multiple current solutions, all have problems:
+
+#### Global forecasting
+
+Forecasters inheriting from `_BaseGlobalForecaster`.
+
+A `y` is added in the `predict` methods. If this is passed, the `fit` is interpreted
+as a pretraining pass.
+
+Problems:
+
+* some models need to know at `fit` time whether the data is for pretraining.
+Examples: global reduction approaches. Broadcasting.
+* as a general design principle, all `predict` methods would need to be changed to
+allow for addition of the `fit` arguments. This clashes in cases where arguments
+of the same name are present both in `fit` and `predict`, e.g., the `X` in forecasting,
+or all arguments for transformations.
+
+#### Pre-training foundation models
+
+Foundation models currently come in two different, contradictory forms:
+
+* those that carry out fine-tuning in `fit`
+* those that pass the context in `fit`, e.g., zero-shot models
+
+Problems: This is inconsistent, and it does not seem to be possible - without an `__init__` arg
+that acts as a switch, or in different classes, to have the same weights in the same class
+be part of a zero-shot or fine-tuning algorithm.
+
+
+## Design: pretraining vignette
+
+Presenting user facing API. For delineation against current designs:
+
+* no new arguments are added to `predict`-like methods
+* a new `pretrain` method is added which can be called before `fit`
+
+
+### basic usage vignette
+
+An alternative idea would be adding an new method
+
+```python
+
+y_pretrain, X_pretrain = load_pretrain_data()
+
+f = MyForecasterCls(params)
+
+f.pretrain(y=y_pretrain, X=X_pretrain)
+
+# usual vignette starts here
+
+y, X = load_data()
+
+f.fit(y, X, fh=[1, 2, 3])
+
+f.predict()
+f.predict_intervals()
+```
+
+
+
+With optional serialization after pre-training:
+
+```python
+
+# optional: serialize
+
+f.save(checkpoint_name)
+
+# restart
+
+f = load(checkpoint_name)
+```
+
+
+### Alternative vignette 1
+
+An alternative idea would be adding an arg to `fit`:
+
+```python
+
+y_pretrain, X_pretrain = load_pretrain_data()
+
+f = MyForecasterCls(params)
+
+f.fit(y=y_pretrain, X=X_pretrain, pretrain=True)
+
+# usual vignette starts here
+
+y, X = load_data()
+
+f.fit(y, X, fh=[1, 2, 3])
+
+f.predict()
+f.predict_intervals()
+```
+
+### Alternative vignette 2
+
+An alternative idea would be adding an flag switcher method, similar to vignette 1
+
+```python
+
+y_pretrain, X_pretrain = load_pretrain_data()
+
+f = MyForecasterCls(params)
+
+f.pretrain()
+
+f.fit(y=y_pretrain, X=X_pretrain)
+
+# fh is optional, but some models require this
+
+f.pretrain("off")
+
+# usual vignette starts here
+y, X = load_data()
+
+f.fit(y, X, fh=[1, 2, 3])
+
+f.predict()
+f.predict_intervals()
+```
+
+
+### Mapping use cases on the vignette
+
+The following map on the "pre-train" phase:
+
+* Training for global forecasting
+* fine-tuning
+* pre-training of any other kind
+
+Zero-shot models do not have pre-training, but `fit` needs to be called,
+it is used only to read in the context (there is no `y` in `predict`).
+
+
+## Design: concepts and internals
+
+### Conceptual model, state diagram
+
+Estimators get a third state, from two:
+
+* blueprint/pristine
+    * definition: directly after `__init__`
+    * even if a pretrained neural network is constructed with a checkpoint reference, we consider the `sktime` model a blueprint.
+* pretrained (new)
+    * definition: pretrained attributes are present, at least one call `pretrain`
+* fitted
+    * at least one call of `fit`
+
+The definition of pretrained is: pretrained attributes are present, definition as below.
+
+Blueprint transitions to pretrained or directly to fitted.
+
+Fitted cannot transition back to pretrained.
+
+### Pretrained attributes and state attributes
+
+Pretrained attributes, by convention, start with an underscore and end in an underscore.
+
+They should not be present after `__init__`.
+
+A `pretrain` call may write only to pretrained attributes.
+
+An attribute `_is_pretrained` is added, this tracks whether the model is pretrained.
+
+### Tags
+
+A tag `capability:pretrain` is introduced, and signifies models with non-trivial pretrained state.
+
+The default behaviour is not an error raised, but the empty operation (a `pass`).
+
+### Extension contract
+
+An optional extender method `_pretrain` is added. This method returns `self`.
+
+### Optional: checkpoint serialization for deep learning models
+
+Some neural network models may have a `save_checkpoint` method.
+
+This allows to serialize checkpoints directly for use in `__init__`.
+
+Not all models will have this method.
+
+usage:
+
+```python
+
+f = MyDLmodel(checkpoint=my_ckpt_path)
+
+f.pretrain(y)
+
+f.save_checkpoint(my_new_ckpt_path)
+
+# later, it can be loaded in new kernel:
+
+f = MyDLmodel(checkpoint=my_new_ckpt_path)
+```