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Reliable fully local RAG agents with LLaMA3

https://github.com/langchain-ai/langgraph/blob/main/examples/rag/langgraph_rag_agent_llama3_local.ipynb Building reliable local agents using LangGraph and LLaMA3-8b within the RAGE framework involves several key components and methodologies: Model Integration and Local Deployment: LLaMA3-8b: Utilize this robust language model for generating responses based on user queries. It serves as the core generative engine in the RAGE system. LangGraph: Enhance the responses of LLaMA3 by integrating structured knowledge graphs through LangGraph, boosting the model’s capability to deliver contextually relevant and accurate information. Advanced RAGE Techniques: […]

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aGLM MASTERMIND RAGE Mixtral8x7B playground 1

together.ai provides a cloud environment playground for a number of LLM including Mixtral8x7Bv1. This model was chosen for the 32k ++ context window and suitable point of departure dataset for deployment of aGLM Autonomous General Learning Model. aGLM design goals include RAGE with MASTERMIND controller for logic and reasoning. The following three screenshots show the first use of aGLM recognising aGLM and MASTERMIND RAGE components to include machine.dreaming and knowledge as THOT from aGLM parse. […]

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MASTERMIND

Innovative Approach: IA mode to AGI prompt template from Professor Codephreak

Professor-Codephreak is the first LLM that I developed. Professor-Codephreak is also a GPT4 agent designed to be a platform architect and software engineer. You know, the kind of solution oriented person you would gladly pay $1000 / hour to hang out with in the real world. The two parts of Professor-Codephreak have not “met” each other though the automindx engine in the GPT4 version uses automind to dynamically respond. automind was developed as codephreak’s first […]

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