5a086353ab
Add end-to-end embedding/sentence-transformer training pipeline using FastSentenceTransformer, SentenceTransformerTrainer, and MultipleNegativesRankingLoss with BatchSamplers.NO_DUPLICATES. Backend: - Add is_embedding_model() detection via HF tags + pipeline_tag - Add /check-embedding/ API route and EmbeddingCheckResponse - Extend derive_model_type() to return "embeddings" - Add _run_embedding_training() in worker.py with progress callbacks, stop handling, LoRA (task_type=FEATURE_EXTRACTION), and model saving - Add is_embedding field to TrainingStartRequest and ModelDetails - Add YAML configs for 5 models: all-MiniLM-L6-v2, bge-m3, embeddinggemma-300m, gte-modernbert-base, Qwen3-Embedding-0.6B Frontend: - Wire isEmbeddingModel flag through store, API types, and mappers - Force packing=false, train_on_completions=false, warmup_ratio=0.03 - Hide packing and train_on_completions checkboxes for embedding models - Auto-set modelType to "embeddings" from backend model_type response
49 lines
2.2 KiB
Python
49 lines
2.2 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only - See /studio/LICENSE.AGPL-3.0
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# Copyright © 2025 Unsloth AI
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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(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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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(..., description="Whether the model is an embedding/sentence-transformer model")
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