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English(EN) 🤖 Combining RAG with Continued Pretraining of LLMs For leaning purposes, I ran an experiment where I used Unsloth to train a model on a new domain using continu

实验结合 RAG 与 LLM 持续预训练以实现灵活性

作者 PulseAugur 编辑部 · [1 个来源] ·

进行了一项实验，将检索增强生成（RAG）与大型语言模型（LLM）的持续预训练相结合。该方法利用 Unsloth 通过持续预训练在新领域上训练模型，然后结合 RAG 步骤以注入动态数据，增强灵活性。 AI

影响 通过允许在特定领域预训练的同时注入动态数据，这种方法可以增强 LLM 的适应性。

排序理由 该条目描述了一个结合 RAG 与 LLM 持续预训练的实验，属于研究范畴。[lever_c_demoted from research: ic=1 ai=1.0]

在 Mastodon — fosstodon.org 阅读 →

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实验结合 RAG 与 LLM 持续预训练以实现灵活性

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该条目描述了一个结合 RAG 与 LLM 持续预训练的实验，属于研究范畴。[lever_c_demoted from research: ic=1 ai=1.0]
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  1. Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] ·

    🤖 将 RAG 与 LLM 的持续预训练相结合，用于学习目的，我进行了一项实验，使用 Unsloth 在新领域训练模型，通过持续

    🤖 Combining RAG with Continued Pretraining of LLMs For leaning purposes, I ran an experiment where I used Unsloth to train a model on a new domain using continued pretraining. Then to make it more flexible, I added a RAG step to inject dynamic data... 📰 Source: Artificial Intelli…

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