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Fine-Tuning AI Models: Unlocking the Potential of Llama 2, Code Llama, and OpenHermes
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In this episode, we dive deep into the world of fine-tuning AI language models, breaking down the processes and techniques behind optimizing models like Llama 2, Code Llama, and OpenHermes. We'll explore the critical role of high-quality instruction datasets and walk you through a step-by-step guide on fine-tuning Llama 2 using Google Colab. Learn about the key libraries, parameters, and how to go beyond notebooks with more advanced scripts.
We also take a closer look at the fine-tuning of Code Llama with the Axolotl tool, covering everything from setting up a cloud-based GPU service to merging the trained model and uploading it to Hugging Face. Whether you're just starting with AI models or looking to level up your game, this episode has you covered.
Finally, we'll explore Direct Preference Optimization (DPO), a cutting-edge technique that significantly improved the performance of OpenHermes-2.5. DPO, a variation of Reinforcement Learning from Human Feedback (RLHF), shows how preference data can help models generate more accurate and relevant answers. Tune in for practical insights, code snippets, and tips to help you explore and optimize AI models.
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