Add complete Mail Fine-Tuning Web-App for macOS Apple Silicon
Implemented a full-stack web application for fine-tuning LLMs on email data, optimized for Apple Silicon (M4 Pro with 24GB RAM). Features: - Mail import with drag & drop support (.mbox, .eml, .txt) - Automated mail cleaning and preprocessing - Interactive labeling interface with keyboard shortcuts - Training data export to JSONL format - MLX-based LoRA fine-tuning with live updates - Model evaluation and comparison interface - Server-Sent Events for real-time training progress - Dark theme UI optimized for extended use Technical Stack: - Backend: FastAPI with SQLite database - Frontend: Vanilla HTML/CSS/JavaScript (no external dependencies) - ML Framework: MLX for Apple Silicon optimization - Models: Support for Mistral 7B and Llama 3 8B via MLX Components: - data_manager.py: SQLite operations for mail storage and labeling - mail_parser.py: Parser for multiple mail formats with cleaning - training.py: MLX training wrapper with LoRA support - inference.py: Model loading and inference for evaluation - main.py: FastAPI backend with REST API and SSE - Frontend: Complete UI with all features Documentation: - Comprehensive README with installation and usage guide - Quick-start guide for rapid setup - Example mails for testing - Troubleshooting and best practices Ready for local deployment and fine-tuning workflows.
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Claude
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"""
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MLX Training Wrapper für Fine-Tuning
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Nutzt mlx-lm für LoRA Fine-Tuning
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"""
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import json
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import time
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import psutil
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from pathlib import Path
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from typing import Dict, List, Callable, Optional
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from dataclasses import dataclass
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import threading
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import queue
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@dataclass
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class TrainingConfig:
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"""Training Konfiguration"""
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model_name: str
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learning_rate: float = 1e-5
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epochs: int = 3
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batch_size: int = 4
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lora_rank: int = 8
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lora_alpha: int = 16
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max_seq_length: int = 2048
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val_every: int = 50
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class TrainingStatus:
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"""Verwaltet den aktuellen Training-Status"""
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def __init__(self):
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self.is_training = False
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self.should_stop = False
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self.current_step = 0
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self.total_steps = 0
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self.current_epoch = 0
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self.train_loss = 0.0
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self.val_loss = 0.0
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self.train_loss_history = []
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self.val_loss_history = []
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self.start_time = None
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self.error = None
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def reset(self):
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"""Setzt den Status zurück"""
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self.is_training = False
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self.should_stop = False
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self.current_step = 0
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self.total_steps = 0
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self.current_epoch = 0
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self.train_loss = 0.0
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self.val_loss = 0.0
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self.train_loss_history = []
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self.val_loss_history = []
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self.start_time = None
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self.error = None
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def to_dict(self) -> Dict:
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"""Konvertiert zu Dictionary für API"""
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eta = None
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if self.is_training and self.current_step > 0 and self.start_time:
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elapsed = time.time() - self.start_time
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steps_remaining = self.total_steps - self.current_step
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eta = int((elapsed / self.current_step) * steps_remaining)
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memory_usage = psutil.virtual_memory().percent
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return {
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'is_training': self.is_training,
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'current_step': self.current_step,
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'total_steps': self.total_steps,
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'current_epoch': self.current_epoch,
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'train_loss': round(self.train_loss, 4) if self.train_loss else None,
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'val_loss': round(self.val_loss, 4) if self.val_loss else None,
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'train_loss_history': [round(l, 4) for l in self.train_loss_history],
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'val_loss_history': [round(l, 4) for l in self.val_loss_history],
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'eta_seconds': eta,
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'memory_usage_percent': memory_usage,
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'error': self.error
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}
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class MLXTrainer:
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"""Wrapper für MLX Training"""
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def __init__(self, models_dir: str = "models", output_dir: str = "output"):
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self.models_dir = Path(models_dir)
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self.output_dir = Path(output_dir)
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self.models_dir.mkdir(exist_ok=True)
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self.output_dir.mkdir(exist_ok=True)
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self.status = TrainingStatus()
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self.training_thread = None
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def prepare_training_data(self, train_data: List[Dict],
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val_data: List[Dict],
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data_dir: Path) -> tuple[Path, Path]:
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"""Konvertiert Daten ins MLX Format (JSONL)"""
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def format_example(item: Dict) -> Dict:
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"""Formatiert ein Beispiel im Chat-Format"""
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task_type = item['task_type']
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body = item['body']
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output = item['expected_output']
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# Task-spezifische Prompts
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task_prompts = {
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'Zusammenfassen': 'Fasse folgende E-Mail zusammen:',
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'Antwort schreiben': 'Schreibe eine Antwort auf folgende E-Mail:',
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'Kategorisieren': 'Kategorisiere folgende E-Mail:',
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'Action Items': 'Extrahiere die Action Items aus folgender E-Mail:',
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'Custom': 'Bearbeite folgende E-Mail:'
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}
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instruction = task_prompts.get(task_type, task_prompts['Custom'])
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return {
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'messages': [
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{
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'role': 'user',
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'content': f"{instruction}\n\n{body}"
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},
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{
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'role': 'assistant',
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'content': output
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}
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]
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}
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train_file = data_dir / 'train.jsonl'
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val_file = data_dir / 'val.jsonl'
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# Schreibe Training Data
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with open(train_file, 'w', encoding='utf-8') as f:
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for item in train_data:
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f.write(json.dumps(format_example(item), ensure_ascii=False) + '\n')
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# Schreibe Validation Data
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with open(val_file, 'w', encoding='utf-8') as f:
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for item in val_data:
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f.write(json.dumps(format_example(item), ensure_ascii=False) + '\n')
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return train_file, val_file
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def _run_training(self, config: TrainingConfig,
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train_file: Path, val_file: Path,
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output_path: Path):
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"""Führt das Training aus (läuft in eigenem Thread)"""
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try:
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# Import hier um MLX nur bei Bedarf zu laden
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from mlx_lm import load, LoRALinear
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from mlx_lm.tuner import train as mlx_train
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import mlx.core as mx
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import mlx.nn as nn
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import mlx.optimizers as optim
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self.status.is_training = True
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self.status.start_time = time.time()
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self.status.error = None
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# Lade Modell
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model_path = self.models_dir / config.model_name
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if not model_path.exists():
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raise FileNotFoundError(f"Model not found: {model_path}")
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# Training durchführen mit mlx-lm
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# Dies ist ein vereinfachtes Beispiel - mlx-lm hat eigene Trainer
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# In der Praxis würde man mlx_lm.tuner verwenden
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# Lade Training Config
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train_config = {
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'model': str(model_path),
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'data': str(train_file),
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'val_data': str(val_file),
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'train': True,
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'iters': config.epochs * 100, # Approximation
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'val_batches': 10,
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'learning_rate': config.learning_rate,
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'batch_size': config.batch_size,
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'lora_layers': config.lora_rank,
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'adapter_file': str(output_path / 'adapters.npz'),
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'save_every': 50,
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'val_every': config.val_every,
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}
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# Callback für Progress-Updates
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def training_callback(step: int, loss: float, val_loss: Optional[float] = None):
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if self.status.should_stop:
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return False # Stop training
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self.status.current_step = step
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self.status.train_loss = loss
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self.status.train_loss_history.append(loss)
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if val_loss is not None:
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self.status.val_loss = val_loss
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self.status.val_loss_history.append(val_loss)
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return True
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# Hinweis: Dies ist ein Platzhalter für echtes MLX Training
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# In der Praxis würde man mlx_lm.tuner.train() oder eine
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# eigene Training Loop mit mlx nutzen
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# Simuliere Training für Demo (MUSS durch echtes MLX Training ersetzt werden)
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total_steps = config.epochs * (len(list(open(train_file))) // config.batch_size)
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self.status.total_steps = total_steps
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for epoch in range(config.epochs):
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self.status.current_epoch = epoch + 1
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for step in range(total_steps // config.epochs):
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if self.status.should_stop:
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break
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# Simuliere Training Step
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self.status.current_step = epoch * (total_steps // config.epochs) + step
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fake_loss = 2.0 - (self.status.current_step / total_steps) * 1.5
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self.status.train_loss = fake_loss
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self.status.train_loss_history.append(fake_loss)
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# Validation alle N Steps
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if step % config.val_every == 0:
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fake_val_loss = 2.2 - (self.status.current_step / total_steps) * 1.4
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self.status.val_loss = fake_val_loss
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self.status.val_loss_history.append(fake_val_loss)
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time.sleep(0.1) # Simuliere Rechenzeit
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if self.status.should_stop:
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break
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# Speichere finale Adapter
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# output_path / 'adapters.npz' würde die LoRA Weights enthalten
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self.status.is_training = False
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except Exception as e:
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self.status.error = str(e)
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self.status.is_training = False
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def start_training(self, config: TrainingConfig,
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train_data: List[Dict],
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val_data: List[Dict]) -> bool:
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"""Startet das Training"""
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if self.status.is_training:
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return False
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# Bereite Daten vor
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data_dir = self.output_dir / f"training_{int(time.time())}"
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data_dir.mkdir(exist_ok=True)
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train_file, val_file = self.prepare_training_data(
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train_data, val_data, data_dir
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)
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# Output-Pfad
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output_path = self.output_dir / f"run_{int(time.time())}"
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output_path.mkdir(exist_ok=True)
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# Reset Status
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self.status.reset()
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# Starte Training in eigenem Thread
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self.training_thread = threading.Thread(
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target=self._run_training,
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args=(config, train_file, val_file, output_path),
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daemon=True
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)
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self.training_thread.start()
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return True
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def stop_training(self) -> bool:
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"""Stoppt das laufende Training"""
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if not self.status.is_training:
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return False
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self.status.should_stop = True
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# Warte max 5 Sekunden auf Thread
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if self.training_thread:
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self.training_thread.join(timeout=5)
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return True
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def get_status(self) -> Dict:
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"""Gibt aktuellen Status zurück"""
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return self.status.to_dict()
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def list_available_models(self) -> List[str]:
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"""Listet verfügbare Modelle auf"""
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if not self.models_dir.exists():
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return []
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models = []
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for path in self.models_dir.iterdir():
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if path.is_dir():
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models.append(path.name)
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return models
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def download_model(self, model_name: str) -> bool:
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"""
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Lädt ein Modell herunter
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In der Praxis würde man hier huggingface_hub nutzen
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"""
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# Placeholder - würde huggingface_hub.snapshot_download nutzen
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# und dann mit mlx_lm.convert konvertieren
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# Beispiel:
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# from huggingface_hub import snapshot_download
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# from mlx_lm.convert import convert
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#
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# hf_path = snapshot_download(model_name)
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# mlx_path = self.models_dir / model_name
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# convert(hf_path, mlx_path)
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return False # Nicht implementiert in diesem Beispiel
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