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The paper proposes a new method for improving Large Language Models (LLMs) without forgetting previous knowledge. The method involves expanding Transformer blocks and tuning them using new corpus. The resulting model, LLAMA PRO-8.3B, performs well in general tasks, programming, and mathematics, surpassing existing models in the LLaMA family. The findings contribute to the development of advanced language agents.
https://arxiv.org/abs//2401.02415
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
By Igor Melnyk5
33 ratings
The paper proposes a new method for improving Large Language Models (LLMs) without forgetting previous knowledge. The method involves expanding Transformer blocks and tuning them using new corpus. The resulting model, LLAMA PRO-8.3B, performs well in general tasks, programming, and mathematics, surpassing existing models in the LLaMA family. The findings contribute to the development of advanced language agents.
https://arxiv.org/abs//2401.02415
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

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