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What is WhisperKit?
WhisperKit is a pure Swift package built by @argmaxinc that enables real-time Whisper inference on iPhone, Mac and even Apple Watch in two lines of code. It is built on Core ML and can be configured to run on the Neural Engine or GPU or both. WhisperKit models are published and distributed through the Hugging Face Hub. Anyone can publish a Whisper fine-tune and evaluate it on their own dataset using WhisperKit!
WhisperKit models: argmaxinc/whisperkit-coreml
Example App: https://testflight.apple.com/join/LPVOyJZW
Swift Package: https://github.com/argmaxinc/WhisperKit
Python toolkit: https://github.com/argmaxinc/whisperkittools
WhisperKit is a pure Swift package built by @argmaxinc that enables real-time Whisper inference on iPhone, Mac and even Apple Watch in two lines of code. It is built on Core ML and can be configured to run on the Neural Engine or GPU or both. WhisperKit models are published and distributed through the Hugging Face Hub. Anyone can publish a Whisper fine-tune and evaluate it on their own dataset using WhisperKit!
WhisperKit models: argmaxinc/whisperkit-coreml
Example App: https://testflight.apple.com/join/LPVOyJZW
Swift Package: https://github.com/argmaxinc/WhisperKit
Python toolkit: https://github.com/argmaxinc/whisperkittools
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#55 opened 12 months ago
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Self-Rewarding Language Models
paper page: Self-Rewarding Language Models (2401.10020)
Fine-tuning Llama 2 70B on three iterations of our approach yields a model that outperforms many existing systems on the AlpacaEval 2.0 leaderboard, including Claude 2, Gemini Pro, and GPT-4 0613
paper page: Self-Rewarding Language Models (2401.10020)
Fine-tuning Llama 2 70B on three iterations of our approach yields a model that outperforms many existing systems on the AlpacaEval 2.0 leaderboard, including Claude 2, Gemini Pro, and GPT-4 0613
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