Professor Codephreak

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Professor Codephreak Software Engineer Machine Learning Platform Architect

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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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together ai

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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workflow

workflow for providing solution from AGI as a response from reasoning

To provide a solution that processes user input through various reasoning methods, then integrates the decision-making with the Socratic reasoning process to provide a final AGI response, follow this workflow. This will involve updates to several modules and integrating logging and reasoning processes. Here’s the detailed workflow: Workflow Steps: Workflow Roadmap from UI to AGI Solution: By following this workflow, the system ensures that user input is processed through multiple reasoning methods, validated and refined […]

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