fix: restore models directory files deleted during restructure

.gitignore

@@ -20,7 +20,7 @@ unsloth_compiled_cache/
 outputs/
 *.gguf
 *.safetensors
-models/
+/models/
 
 # IDE / Editors
 .vscode/

studio/backend/models/__init__.py

@@ -0,0 +1,37 @@
+"""
+Pydantic models for API request/response schemas
+"""
+from .training import (
+    TrainingStartRequest,
+    TrainingStartResponse,
+    TrainingStatusResponse,
+    TrainingMetricsResponse,
+    TrainingProgressResponse,
+)
+from .models import (
+    ModelSearchRequest,
+    ModelSearchResponse,
+    ModelListResponse,
+    ModelConfigResponse,
+    LoRAScanResponse,
+    LoRAInfo,
+    ModelInfo,
+)
+
+__all__ = [
+    # Training schemas
+    "TrainingStartRequest",
+    "TrainingStartResponse",
+    "TrainingStatusResponse",
+    "TrainingMetricsResponse",
+    "TrainingProgressResponse",
+    # Model management schemas
+    "ModelSearchRequest",
+    "ModelSearchResponse",
+    "ModelListResponse",
+    "ModelConfigResponse",
+    "LoRAScanResponse",
+    "LoRAInfo",
+    "ModelInfo",
+]
+

studio/backend/models/models.py

@@ -0,0 +1,56 @@
+"""
+Pydantic schemas for Model Management API
+"""
+from pydantic import BaseModel, Field
+from typing import Optional, List, Dict, Any
+
+
+class ModelSearchRequest(BaseModel):
+    """Request schema for searching HuggingFace models"""
+    query: str = Field(..., description="Search query")
+    hf_token: Optional[str] = Field(None, description="HuggingFace token for authenticated searches")
+
+
+class ModelInfo(BaseModel):
+    """Model information"""
+    id: str = Field(..., description="Model identifier")
+    name: Optional[str] = Field(None, description="Display name")
+    description: Optional[str] = Field(None, description="Model description")
+    size: Optional[str] = Field(None, description="Model size")
+    is_vision: bool = Field(False, description="Whether model is a vision model")
+    is_lora: bool = Field(False, description="Whether model is a LoRA adapter")
+
+
+class ModelSearchResponse(BaseModel):
+    """Response schema for model search"""
+    models: List[ModelInfo] = Field(default_factory=list, description="List of matching models")
+    total: int = Field(0, description="Total number of results")
+
+
+class ModelListResponse(BaseModel):
+    """Response schema for listing available models"""
+    models: List[ModelInfo] = Field(default_factory=list, description="List of available models")
+    default_models: List[str] = Field(default_factory=list, description="List of default model IDs")
+
+
+class ModelConfigResponse(BaseModel):
+    """Response schema for model configuration"""
+    model_name: str = Field(..., description="Model identifier")
+    config: Dict[str, Any] = Field(..., description="Model configuration dictionary")
+    is_vision: bool = Field(False, description="Whether model is a vision model")
+    is_lora: bool = Field(False, description="Whether model is a LoRA adapter")
+    base_model: Optional[str] = Field(None, description="Base model if this is a LoRA adapter")
+
+
+class LoRAInfo(BaseModel):
+    """LoRA adapter information"""
+    display_name: str = Field(..., description="Display name for the LoRA")
+    adapter_path: str = Field(..., description="Path to the LoRA adapter")
+    base_model: Optional[str] = Field(None, description="Base model identifier")
+
+
+class LoRAScanResponse(BaseModel):
+    """Response schema for scanning trained LoRA adapters"""
+    loras: List[LoRAInfo] = Field(default_factory=list, description="List of found LoRA adapters")
+    outputs_dir: str = Field(..., description="Directory that was scanned")
+

studio/backend/models/training.py

@@ -0,0 +1,96 @@
+"""
+Pydantic schemas for Training API
+"""
+from pydantic import BaseModel, Field
+from typing import Optional, List
+
+
+class TrainingStartRequest(BaseModel):
+    """Request schema for starting training"""
+    # Model parameters
+    model_name: str = Field(..., description="Model identifier (e.g., 'unsloth/llama-3-8b-bnb-4bit')")
+    training_type: str = Field(..., description="Training type: 'LoRA/QLoRA' or 'Full Finetuning'")
+    hf_token: Optional[str] = Field(None, description="HuggingFace token")
+    load_in_4bit: bool = Field(True, description="Load model in 4-bit quantization")
+    max_seq_length: int = Field(2048, description="Maximum sequence length")
+
+    # Dataset parameters
+    hf_dataset: Optional[str] = Field(None, description="HuggingFace dataset identifier")
+    local_datasets: List[str] = Field(default_factory=list, description="List of local dataset paths")
+    format_type: str = Field(..., description="Dataset format type")
+
+    # Training parameters
+    num_epochs: int = Field(1, description="Number of training epochs")
+    learning_rate: str = Field("2e-4", description="Learning rate")
+    batch_size: int = Field(1, description="Batch size")
+    gradient_accumulation_steps: int = Field(1, description="Gradient accumulation steps")
+    warmup_steps: Optional[int] = Field(None, description="Warmup steps")
+    warmup_ratio: Optional[float] = Field(None, description="Warmup ratio")
+    max_steps: Optional[int] = Field(None, description="Maximum training steps")
+    save_steps: int = Field(100, description="Steps between checkpoints")
+    weight_decay: float = Field(0.01, description="Weight decay")
+    random_seed: int = Field(42, description="Random seed")
+    packing: bool = Field(False, description="Enable sequence packing")
+    optim: str = Field("adamw_8bit", description="Optimizer")
+    lr_scheduler_type: str = Field("linear", description="Learning rate scheduler type")
+
+    # LoRA parameters
+    use_lora: bool = Field(True, description="Use LoRA (derived from training_type)")
+    lora_r: int = Field(16, description="LoRA rank")
+    lora_alpha: int = Field(16, description="LoRA alpha")
+    lora_dropout: float = Field(0.0, description="LoRA dropout")
+    target_modules: List[str] = Field(default_factory=list, description="Target modules for LoRA")
+    gradient_checkpointing: str = Field("", description="Gradient checkpointing setting")
+    use_rslora: bool = Field(False, description="Use RSLoRA")
+    use_loftq: bool = Field(False, description="Use LoftQ")
+    train_on_completions: bool = Field(False, description="Train on completions only")
+
+    # Vision-specific LoRA parameters
+    finetune_vision_layers: bool = Field(False, description="Finetune vision layers")
+    finetune_language_layers: bool = Field(False, description="Finetune language layers")
+    finetune_attention_modules: bool = Field(False, description="Finetune attention modules")
+    finetune_mlp_modules: bool = Field(False, description="Finetune MLP modules")
+
+    # Logging parameters
+    enable_wandb: bool = Field(False, description="Enable Weights & Biases logging")
+    wandb_token: Optional[str] = Field(None, description="W&B token")
+    wandb_project: Optional[str] = Field(None, description="W&B project name")
+    enable_tensorboard: bool = Field(False, description="Enable TensorBoard logging")
+    tensorboard_dir: Optional[str] = Field(None, description="TensorBoard directory")
+
+
+class TrainingStartResponse(BaseModel):
+    """Response schema for training start"""
+    status: str = Field(..., description="Status: 'started' or 'error'")
+    job_id: Optional[str] = Field(None, description="Training job ID")
+    message: str = Field(..., description="Status message")
+    error: Optional[str] = Field(None, description="Error message if status is 'error'")
+
+
+class TrainingStatusResponse(BaseModel):
+    """Response schema for training status"""
+    status: str = Field(..., description="Status: 'idle', 'preparing', 'training', 'stopping', 'error'")
+    is_active: bool = Field(..., description="Whether training is currently active (actual training running)")
+    message: str = Field(..., description="Status message")
+    current_step: Optional[int] = Field(None, description="Current training step")
+    total_steps: Optional[int] = Field(None, description="Total training steps")
+
+
+class TrainingMetricsResponse(BaseModel):
+    """Response schema for training metrics"""
+    loss_history: List[float] = Field(default_factory=list, description="Loss values")
+    lr_history: List[float] = Field(default_factory=list, description="Learning rate values")
+    step_history: List[int] = Field(default_factory=list, description="Step numbers")
+    current_loss: Optional[float] = Field(None, description="Current loss value")
+    current_lr: Optional[float] = Field(None, description="Current learning rate")
+    current_step: Optional[int] = Field(None, description="Current step")
+
+
+class TrainingProgressResponse(BaseModel):
+    """Response schema for training progress updates"""
+    step: int = Field(..., description="Current step")
+    loss: float = Field(..., description="Current loss")
+    learning_rate: float = Field(..., description="Current learning rate")
+    status_message: str = Field(..., description="Status message")
+    progress_percent: Optional[float] = Field(None, description="Progress percentage")
+

studio/backend/models/users.py

@@ -0,0 +1,32 @@
+"""Pydantic models for user-related API endpoints.
+
+This module defines the data models used for user authentication and management
+in the FastAPI application.
+"""
+
+from pydantic import BaseModel
+
+
+class User(BaseModel):
+    """Basic user model containing username."""
+
+    username: str
+
+
+class UserInDB(BaseModel):
+    """User model with password for database storage."""
+
+    password: str
+
+
+class Token(BaseModel):
+    """Authentication token model with access token and type."""
+
+    access_token: str
+    token_type: str
+
+
+class TokenData(BaseModel):
+    """Token payload model containing username."""
+
+    username: str | None = None