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lav.io | ||
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bair.berkeley.edu
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| | | | | [AI summary] The article introduces Koala, a dialogue model trained by fine-tuning Meta's LLaMA on dialogue data from the web, with a focus on interactions with large closed-source models like ChatGPT. The model's performance is compared to ChatGPT and Stanford's Alpaca, showing competitive results. The paper emphasizes the importance of high-quality training data for smaller models and highlights the potential for open-source models to match the performance of closed-source ones. However, it also acknowledges the limitations and safety concerns of Koala, including potential for misinformation and biases, and emphasizes its research prototype status for academic use. | |
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requester.mturk.com
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www.mturk.com
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www.khanna.law
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| | | You want to train a deep neural network. You have the data. It's labeled and wrangled into a useful format. What do you do now? | ||
