delete tmp directory
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Roland Tannous
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"""
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Pydantic schemas for Model Management API
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"""
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from pydantic import BaseModel, Field
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from typing import Optional, List, Dict, Any
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class ModelDetails(BaseModel):
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"""Detailed model configuration and metadata"""
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model_name: str = Field(..., description="Model identifier")
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config: Dict[str, Any] = Field(..., description="Model configuration dictionary")
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is_vision: bool = Field(False, description="Whether model is a vision model")
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is_lora: bool = Field(False, description="Whether model is a LoRA adapter")
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base_model: Optional[str] = Field(None, description="Base model if this is a LoRA adapter")
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class LoRAInfo(BaseModel):
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"""LoRA adapter information"""
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display_name: str = Field(..., description="Display name for the LoRA")
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adapter_path: str = Field(..., description="Path to the LoRA adapter")
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base_model: Optional[str] = Field(None, description="Base model identifier")
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class LoRAScanResponse(BaseModel):
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"""Response schema for scanning trained LoRA adapters"""
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loras: List[LoRAInfo] = Field(default_factory=list, description="List of found LoRA adapters")
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outputs_dir: str = Field(..., description="Directory that was scanned")
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# Refactor Pydantic API Models
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## Summary
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Cleans up and restructures the Pydantic models for the training and model management APIs to reduce redundancy, improve naming clarity, and align with the frontend architecture.
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---
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## Training Models (`studio/backend/models/training.py`)
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| Before | After | Change |
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|--------|-------|--------|
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| `TrainingStartResponse` | `TrainingJobResponse` | Rename - clearer that it represents a created job |
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| `TrainingStatusResponse` | `TrainingStatus` | Rename + add `phase` field with explicit pipeline stages |
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| `TrainingProgressResponse` | `TrainingProgress` | Rename + add `epoch`, `elapsed_seconds`, `eta_seconds` |
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| `TrainingMetricsResponse` | — | **Removed** - frontend stores history in IndexedDB |
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**Key improvements:**
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- `TrainingStatus` unifies status polling and streaming with explicit `phase` literals: `idle`, `loading_model`, `loading_dataset`, `configuring`, `training`, `completed`, `error`, `stopped`
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- Renamed `is_active` → `is_training_running` for clarity
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- `TrainingProgress` now includes timing info (`elapsed_seconds`, `eta_seconds`) for better UX
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---
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## Model Management (`studio/backend/models/models.py`)
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| Before | After | Change |
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|--------|-------|--------|
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| `ModelSearchRequest` | — | **Removed** - search handled via query params |
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| `ModelSearchResponse` | — | **Removed** |
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| `ModelListResponse` | — | **Removed** |
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| `ModelInfo` | — | **Removed** - redundant with ModelDetails |
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| `ModelConfigResponse` | `ModelDetails` | Rename |
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**Remaining models:** `ModelDetails`, `LoRAInfo`, `LoRAScanResponse`
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---
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## Breaking Changes
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- All renamed/removed models will require updates in any code referencing them
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- Frontend TypeScript types should be regenerated
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"""
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Pydantic schemas for Training API
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"""
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from pydantic import BaseModel, Field
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from typing import Optional, List, Literal
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class TrainingStartRequest(BaseModel):
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"""Request schema for starting training"""
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# Model parameters
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model_name: str = Field(..., description="Model identifier (e.g., 'unsloth/llama-3-8b-bnb-4bit')")
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training_type: str = Field(..., description="Training type: 'LoRA/QLoRA' or 'Full Finetuning'")
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hf_token: Optional[str] = Field(None, description="HuggingFace token")
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load_in_4bit: bool = Field(True, description="Load model in 4-bit quantization")
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max_seq_length: int = Field(2048, description="Maximum sequence length")
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# Dataset parameters
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hf_dataset: Optional[str] = Field(None, description="HuggingFace dataset identifier")
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local_datasets: List[str] = Field(default_factory=list, description="List of local dataset paths")
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format_type: str = Field(..., description="Dataset format type")
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# Training parameters
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num_epochs: int = Field(1, description="Number of training epochs")
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learning_rate: str = Field("2e-4", description="Learning rate")
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batch_size: int = Field(1, description="Batch size")
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gradient_accumulation_steps: int = Field(1, description="Gradient accumulation steps")
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warmup_steps: Optional[int] = Field(None, description="Warmup steps")
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warmup_ratio: Optional[float] = Field(None, description="Warmup ratio")
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max_steps: Optional[int] = Field(None, description="Maximum training steps")
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save_steps: int = Field(100, description="Steps between checkpoints")
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weight_decay: float = Field(0.01, description="Weight decay")
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random_seed: int = Field(42, description="Random seed")
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packing: bool = Field(False, description="Enable sequence packing")
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optim: str = Field("adamw_8bit", description="Optimizer")
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lr_scheduler_type: str = Field("linear", description="Learning rate scheduler type")
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# LoRA parameters
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use_lora: bool = Field(True, description="Use LoRA (derived from training_type)")
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lora_r: int = Field(16, description="LoRA rank")
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lora_alpha: int = Field(16, description="LoRA alpha")
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lora_dropout: float = Field(0.0, description="LoRA dropout")
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target_modules: List[str] = Field(default_factory=list, description="Target modules for LoRA")
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gradient_checkpointing: str = Field("", description="Gradient checkpointing setting")
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use_rslora: bool = Field(False, description="Use RSLoRA")
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use_loftq: bool = Field(False, description="Use LoftQ")
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train_on_completions: bool = Field(False, description="Train on completions only")
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# Vision-specific LoRA parameters
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finetune_vision_layers: bool = Field(False, description="Finetune vision layers")
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finetune_language_layers: bool = Field(False, description="Finetune language layers")
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finetune_attention_modules: bool = Field(False, description="Finetune attention modules")
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finetune_mlp_modules: bool = Field(False, description="Finetune MLP modules")
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# Logging parameters
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enable_wandb: bool = Field(False, description="Enable Weights & Biases logging")
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wandb_token: Optional[str] = Field(None, description="W&B token")
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wandb_project: Optional[str] = Field(None, description="W&B project name")
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enable_tensorboard: bool = Field(False, description="Enable TensorBoard logging")
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tensorboard_dir: Optional[str] = Field(None, description="TensorBoard directory")
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class TrainingJobResponse(BaseModel):
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"""Immediate response when training is initiated"""
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job_id: str = Field(..., description="Unique training job identifier")
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status: Literal["queued", "error"] = Field(..., description="Initial job status")
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message: str = Field(..., description="Human-readable status message")
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error: Optional[str] = Field(None, description="Error details if status is 'error'")
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class TrainingStatus(BaseModel):
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"""Current training job status - works for streaming or polling"""
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job_id: str = Field(..., description="Training job identifier")
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phase: Literal[
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"idle",
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"loading_model",
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"loading_dataset",
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"configuring",
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"training",
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"completed",
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"error",
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"stopped"
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] = Field(..., description="Current phase of training pipeline")
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is_training_running: bool = Field(..., description="True if training loop is actively running")
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message: str = Field(..., description="Human-readable status message")
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error: Optional[str] = Field(None, description="Error details if phase is 'error'")
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details: Optional[dict] = Field(None, description="Phase-specific info, e.g. {'model_size': '8B'}")
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class TrainingProgress(BaseModel):
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"""Training progress metrics - for streaming or polling"""
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job_id: str = Field(..., description="Training job identifier")
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step: int = Field(..., description="Current training step")
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total_steps: int = Field(..., description="Total training steps")
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loss: float = Field(..., description="Current loss value")
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learning_rate: float = Field(..., description="Current learning rate")
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progress_percent: float = Field(..., description="Progress percentage (0.0 to 100.0)")
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epoch: Optional[int] = Field(None, description="Current epoch")
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elapsed_seconds: Optional[float] = Field(None, description="Time elapsed since training started")
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eta_seconds: Optional[float] = Field(None, description="Estimated time remaining")
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# Training Pydantic Models Recommendation
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## Current State
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| Model | Purpose |
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|-------|---------|
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| `TrainingStartRequest` | Request payload to start training |
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| `TrainingStartResponse` | Immediate response after `/start` |
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| `TrainingStatusResponse` | Response for `/status` polling |
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| `TrainingMetricsResponse` | Historical metrics |
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| `TrainingProgressResponse` | Per-step progress data |
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---
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## Proposed Models
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### 1. `TrainingStartRequest` — Keep as-is
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Well-structured, no changes needed.
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---
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### 2. `TrainingJobResponse` (rename from `TrainingStartResponse`)
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Returned **once** when `/train/start` is called.
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```python
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class TrainingJobResponse(BaseModel):
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"""Immediate response when training is initiated"""
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job_id: str
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status: Literal["queued", "error"]
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message: str
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error: Optional[str] = None
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```
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---
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### 3. `TrainingStatus` (unifies `TrainingStatusResponse` + `TrainingPhaseUpdate`)
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Single model for **both streaming and polling**.
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```python
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class TrainingStatus(BaseModel):
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"""Current training job status - works for streaming or polling"""
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job_id: str
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phase: Literal[
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"idle",
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"loading_model",
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"loading_dataset",
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"configuring",
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"training",
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"completed",
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"error",
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"stopped"
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]
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is_training_running: bool # True if training loop is actively running
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message: str
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error: Optional[str] = None
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details: Optional[dict] = None # Phase-specific info, e.g. {"model_size": "8B"}
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```
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**Usage:**
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- **Streaming**: Push when phase changes
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- **Polling**: Return from `GET /train/status/{job_id}`
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---
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### 4. `TrainingProgress` (rename from `TrainingProgressResponse`)
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Per-step metrics during active training.
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```python
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class TrainingProgress(BaseModel):
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"""Training progress metrics - for streaming or polling"""
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step: int
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total_steps: int
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loss: float
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learning_rate: float
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progress_percent: float
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epoch: Optional[int] = None
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elapsed_seconds: Optional[float] = None
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eta_seconds: Optional[float] = None
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```
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---
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### 5. `TrainingMetricsResponse` — Remove
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Frontend stores history in IndexedDB.
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---
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## Summary
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| Current | Proposed | Action |
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|---------|----------|--------|
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| `TrainingStartRequest` | `TrainingStartRequest` | Keep |
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| `TrainingStartResponse` | `TrainingJobResponse` | Rename |
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| `TrainingStatusResponse` | `TrainingStatus` | Rename + enhance |
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| `TrainingMetricsResponse` | — | **Remove** |
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| `TrainingProgressResponse` | `TrainingProgress` | Rename + enhance |
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---
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## API Flow
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```
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POST /train/start
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└─► TrainingJobResponse { job_id, status: "queued" }
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StreamingResponse / Polling:
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└─► TrainingStatus { phase: "loading_model", is_training_running: false }
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└─► TrainingStatus { phase: "loading_dataset", is_training_running: false }
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└─► TrainingStatus { phase: "training", is_training_running: true }
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└─► TrainingProgress { step: 1, loss: 2.5, ... }
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└─► TrainingProgress { step: 2, loss: 2.3, ... }
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└─► TrainingStatus { phase: "completed", is_training_running: false }
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```
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