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https://github.com/Ladebeze66/llm_ticket3.git
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91 lines
4.4 KiB
Python
91 lines
4.4 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 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 Optional
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from typing_extensions import NotRequired, TypedDict
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class TrainingParametersInTypedDict(TypedDict):
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r"""The fine-tuning hyperparameter settings used in a fine-tune job."""
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training_steps: NotRequired[Nullable[int]]
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r"""The number of training steps to perform. A training step refers to a single update of the model weights during the fine-tuning process. This update is typically calculated using a batch of samples from the training dataset."""
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learning_rate: NotRequired[float]
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r"""A parameter describing how much to adjust the pre-trained model's weights in response to the estimated error each time the weights are updated during the fine-tuning process."""
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weight_decay: NotRequired[Nullable[float]]
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r"""(Advanced Usage) Weight decay adds a term to the loss function that is proportional to the sum of the squared weights. This term reduces the magnitude of the weights and prevents them from growing too large."""
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warmup_fraction: NotRequired[Nullable[float]]
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r"""(Advanced Usage) A parameter that specifies the percentage of the total training steps at which the learning rate warm-up phase ends. During this phase, the learning rate gradually increases from a small value to the initial learning rate, helping to stabilize the training process and improve convergence. Similar to `pct_start` in [mistral-finetune](https://github.com/mistralai/mistral-finetune)"""
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epochs: NotRequired[Nullable[float]]
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fim_ratio: NotRequired[Nullable[float]]
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seq_len: NotRequired[Nullable[int]]
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class TrainingParametersIn(BaseModel):
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r"""The fine-tuning hyperparameter settings used in a fine-tune job."""
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training_steps: OptionalNullable[int] = UNSET
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r"""The number of training steps to perform. A training step refers to a single update of the model weights during the fine-tuning process. This update is typically calculated using a batch of samples from the training dataset."""
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learning_rate: Optional[float] = 0.0001
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r"""A parameter describing how much to adjust the pre-trained model's weights in response to the estimated error each time the weights are updated during the fine-tuning process."""
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weight_decay: OptionalNullable[float] = UNSET
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r"""(Advanced Usage) Weight decay adds a term to the loss function that is proportional to the sum of the squared weights. This term reduces the magnitude of the weights and prevents them from growing too large."""
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warmup_fraction: OptionalNullable[float] = UNSET
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r"""(Advanced Usage) A parameter that specifies the percentage of the total training steps at which the learning rate warm-up phase ends. During this phase, the learning rate gradually increases from a small value to the initial learning rate, helping to stabilize the training process and improve convergence. Similar to `pct_start` in [mistral-finetune](https://github.com/mistralai/mistral-finetune)"""
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epochs: OptionalNullable[float] = UNSET
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fim_ratio: OptionalNullable[float] = UNSET
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seq_len: OptionalNullable[int] = UNSET
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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_steps",
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"learning_rate",
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"weight_decay",
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"warmup_fraction",
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"epochs",
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"fim_ratio",
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"seq_len",
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]
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nullable_fields = [
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"training_steps",
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"weight_decay",
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"warmup_fraction",
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"epochs",
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"fim_ratio",
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"seq_len",
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]
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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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