3ce187da02Raise ruff line-length to 100 and extend the local pre-commit format pipeline (def-signature magic-comma normalization, short multi-line assert collapse, kwarg '=' spacing, blank-line-after-short-import removal, adjacent string-literal / f-string+plain merge, redundant-pass pruning). Every transform re-checks the file AST and is dropped if it would differ; the whole-repo reformat is verified AST-identical per file and idempotent.
61 lines
2.3 KiB
Python
61 lines
2.3 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
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# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
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"""
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Pydantic response schemas for endpoints that previously returned raw dicts.
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These are small response models for training and model management routes.
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"""
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from pydantic import BaseModel, Field
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from typing import Optional, List
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# --- Training route response models ---
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class TrainingStopResponse(BaseModel):
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"""Response for stopping a training job"""
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status: str = Field(..., description = "Current status: 'stopped' or 'idle'")
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message: str = Field(..., description = "Human-readable status message")
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class TrainingMetricsResponse(BaseModel):
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"""Response for training metrics history"""
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loss_history: List[float] = Field(default_factory = list, description = "Loss values per step")
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lr_history: List[float] = Field(default_factory = list, description = "Learning rate per step")
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step_history: List[int] = Field(default_factory = list, description = "Step numbers")
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grad_norm_history: List[float] = Field(default_factory = list, description = "Gradient norm values")
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grad_norm_step_history: List[int] = Field(
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default_factory = list, description = "Step numbers for gradient norm values"
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)
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current_loss: Optional[float] = Field(None, description = "Most recent loss value")
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current_lr: Optional[float] = Field(None, description = "Most recent learning rate")
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current_step: Optional[int] = Field(None, description = "Most recent step number")
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# --- Model management route response models ---
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class LoRABaseModelResponse(BaseModel):
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"""Response for getting a LoRA's base model"""
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lora_path: str = Field(..., description = "Path to the LoRA adapter")
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base_model: str = Field(..., description = "Base model identifier")
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class VisionCheckResponse(BaseModel):
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"""Response for checking if a model is a vision model"""
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model_name: str = Field(..., description = "Model identifier")
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is_vision: bool = Field(..., description = "Whether the model is a vision model")
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class EmbeddingCheckResponse(BaseModel):
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"""Response for checking if a model is an embedding model"""
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model_name: str = Field(..., description = "Model identifier")
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is_embedding: bool = Field(
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..., description = "Whether the model is an embedding/sentence-transformer model"
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)
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