40 lines
1.9 KiB
Python
40 lines
1.9 KiB
Python
"""
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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(default_factory=list, description="Step numbers for gradient norm values")
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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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