mirror of
https://github.com/Ladebeze66/llm_ticket3.git
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102 lines
4.3 KiB
Python
102 lines
4.3 KiB
Python
"""Code generated by Speakeasy (https://speakeasy.com). DO NOT EDIT."""
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from __future__ import annotations
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from .githubrepositoryin import GithubRepositoryIn, GithubRepositoryInTypedDict
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from .trainingfile import TrainingFile, TrainingFileTypedDict
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from .trainingparametersin import TrainingParametersIn, TrainingParametersInTypedDict
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from .wandbintegration import WandbIntegration, WandbIntegrationTypedDict
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from mistralai.types import BaseModel, Nullable, OptionalNullable, UNSET, UNSET_SENTINEL
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from pydantic import model_serializer
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from typing import List, Optional
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from typing_extensions import NotRequired, TypedDict
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JobInIntegrationsTypedDict = WandbIntegrationTypedDict
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JobInIntegrations = WandbIntegration
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JobInRepositoriesTypedDict = GithubRepositoryInTypedDict
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JobInRepositories = GithubRepositoryIn
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class JobInTypedDict(TypedDict):
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model: str
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r"""The name of the model to fine-tune."""
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hyperparameters: TrainingParametersInTypedDict
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r"""The fine-tuning hyperparameter settings used in a fine-tune job."""
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training_files: NotRequired[List[TrainingFileTypedDict]]
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validation_files: NotRequired[Nullable[List[str]]]
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r"""A list containing the IDs of uploaded files that contain validation data. If you provide these files, the data is used to generate validation metrics periodically during fine-tuning. These metrics can be viewed in `checkpoints` when getting the status of a running fine-tuning job. The same data should not be present in both train and validation files."""
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suffix: NotRequired[Nullable[str]]
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r"""A string that will be added to your fine-tuning model name. For example, a suffix of \"my-great-model\" would produce a model name like `ft:open-mistral-7b:my-great-model:xxx...`"""
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integrations: NotRequired[Nullable[List[JobInIntegrationsTypedDict]]]
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r"""A list of integrations to enable for your fine-tuning job."""
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repositories: NotRequired[List[JobInRepositoriesTypedDict]]
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auto_start: NotRequired[bool]
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r"""This field will be required in a future release."""
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class JobIn(BaseModel):
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model: str
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r"""The name of the model to fine-tune."""
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hyperparameters: TrainingParametersIn
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r"""The fine-tuning hyperparameter settings used in a fine-tune job."""
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training_files: Optional[List[TrainingFile]] = None
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validation_files: OptionalNullable[List[str]] = UNSET
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r"""A list containing the IDs of uploaded files that contain validation data. If you provide these files, the data is used to generate validation metrics periodically during fine-tuning. These metrics can be viewed in `checkpoints` when getting the status of a running fine-tuning job. The same data should not be present in both train and validation files."""
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suffix: OptionalNullable[str] = UNSET
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r"""A string that will be added to your fine-tuning model name. For example, a suffix of \"my-great-model\" would produce a model name like `ft:open-mistral-7b:my-great-model:xxx...`"""
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integrations: OptionalNullable[List[JobInIntegrations]] = UNSET
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r"""A list of integrations to enable for your fine-tuning job."""
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repositories: Optional[List[JobInRepositories]] = None
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auto_start: Optional[bool] = None
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r"""This field will be required in a future release."""
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@model_serializer(mode="wrap")
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def serialize_model(self, handler):
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optional_fields = [
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"training_files",
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"validation_files",
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"suffix",
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"integrations",
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"repositories",
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"auto_start",
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]
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nullable_fields = ["validation_files", "suffix", "integrations"]
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null_default_fields = []
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serialized = handler(self)
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m = {}
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for n, f in self.model_fields.items():
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k = f.alias or n
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val = serialized.get(k)
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serialized.pop(k, None)
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optional_nullable = k in optional_fields and k in nullable_fields
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is_set = (
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self.__pydantic_fields_set__.intersection({n})
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or k in null_default_fields
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) # pylint: disable=no-member
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if val is not None and val != UNSET_SENTINEL:
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m[k] = val
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elif val != UNSET_SENTINEL and (
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not k in optional_fields or (optional_nullable and is_set)
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):
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m[k] = val
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return m
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