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unslothai--unsloth/studio/backend/models/responses.py
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chore: import upstream snapshot with attribution
2026-07-13 12:59:56 +08:00

60 lines
2.3 KiB
Python

# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""Pydantic response models for training and model management routes
(previously returned as raw dicts)."""
from pydantic import BaseModel, Field
from typing import Optional, List
# --- Training route response models ---
class TrainingStopResponse(BaseModel):
"""Response for stopping a training job"""
status: str = Field(..., description = "Current status: 'stopped' or 'idle'")
message: str = Field(..., description = "Human-readable status message")
class TrainingMetricsResponse(BaseModel):
"""Response for training metrics history"""
loss_history: List[float] = Field(default_factory = list, description = "Loss values per step")
lr_history: List[float] = Field(default_factory = list, description = "Learning rate per step")
step_history: List[int] = Field(default_factory = list, description = "Step numbers")
grad_norm_history: List[float] = Field(default_factory = list, description = "Gradient norm values")
grad_norm_step_history: List[int] = Field(
default_factory = list, description = "Step numbers for gradient norm values"
)
current_loss: Optional[float] = Field(None, description = "Most recent loss value")
current_lr: Optional[float] = Field(None, description = "Most recent learning rate")
current_step: Optional[int] = Field(None, description = "Most recent step number")
# --- Model management route response models ---
class LoRABaseModelResponse(BaseModel):
"""Response for getting a LoRA's base model"""
lora_path: str = Field(..., description = "Path to the LoRA adapter")
base_model: str = Field(..., description = "Base model identifier")
class VisionCheckResponse(BaseModel):
"""Response for checking if a model is a vision model"""
model_name: str = Field(..., description = "Model identifier")
is_vision: bool = Field(..., description = "Whether the model is a vision model")
class EmbeddingCheckResponse(BaseModel):
"""Response for checking if a model is an embedding model"""
model_name: str = Field(..., description = "Model identifier")
is_embedding: bool = Field(
..., description = "Whether the model is an embedding/sentence-transformer model"
)