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Retrieval-Augmented Generation Architecture Large Language Models (LLMs) often struggle with static training data limitations, leading to issues with outdated information and hallucinations. Retrieval-Augmented Generation (RAG) addresses this by grounding model responses in external, up-to-date knowledge bases. This technique operates in three ...

Setting Up the Development Environment on InternLM Studio Begin by launching a develompent instance and configuring the runtime environment. Once inside, replicate the base environment for running InternLM by creating a custom conda environment named InternLM2_Huixiangdou: studio-conda -o internlm-base -t InternLM2_Huixiangdou After cloning th ...

Retrieval-Augmented Generation Architecture Retrieval-Augmented Generation (RAG) enhances generative models by dynamically fetching relevant context from external knowledge stores before synthesizing a resposne. This methodology addresses core limitations of standalone large language models, including factual hallucination, temporal knowledge d ...