Claude
1456995462
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.
17 lines
413 B
Plaintext
17 lines
413 B
Plaintext
Subject: Frage zu Invoice #2847
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From: michael.schmidt@example.com
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To: support@company.de
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Date: 2024-03-16
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Hallo,
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ich habe eine Frage zur Rechnung #2847 vom 15. März.
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Der Betrag scheint nicht mit unserem ursprünglichen Angebot übereinzustimmen.
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Laut Angebot sollten es 1.250€ sein, auf der Rechnung stehen aber 1.450€.
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Könnten Sie das bitte prüfen und mir Bescheid geben?
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Vielen Dank
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Michael Schmidt
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