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| Name | Fine-Tune Llama2 | Step by Step Guide to Customizing Your Own LLM |
| Video Uploader | Video From DataCamp |
| Upload Date | This Video Uploaded At 18-11-2023 00:30:59 |
| Video Discription | The advent of large language models has taken the AI world by storm. Outside of proprietary foundation models like GPT-4, open-source models are playing a pivotal role in driving the AI revolution forward, democratizing access for anyone looking to leverage these models in production. One of the biggest challenges in generating high-quality output from open-source models rests in fine-tuning, where we improve their outputs based on a series of instructions. In this session, we take a step-by-step approach to fine-tune a Llama 2 model on a custom dataset. First, we build our own dataset using techniques to remove duplicates and analyze the number of tokens. Then, we fine-tune the Llama 2 model using state-of-the art techniques from the Axolotl library. Finally, we see how to run our fine-tuned model and evaluate its performance. Key Takeaways: - How to build an instruction dataset - How to fine-tune a Llama 2 model - How to use and evaluate the trained model Additional Resources: Solution notebook (dataset): https://bit.ly/47itC1U ///// Solution Model: https://bit.ly/3QHrh9L [SKILL TRACK] AI Fundamentals: https://bit.ly/3MQ0E1s [SKILL TRACK] AI Business Fundamentals: https://bit.ly/46zSAsN [BLOG] Introduction to Meta AI’s LLaMA: https://bit.ly/47lv0Bc [BLOG] Fine-Tuning LLaMA 2: A Step-by-Step Guide to Customizing the Large Language Model: https://bit.ly/46zSAsN [BLOG] Llama.cpp Tutorial: A Complete Guide to Efficient LLM Inference and Implementation: https://bit.ly/47kXQl2 |
| Category | Entertainment |
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