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Maybe it is a bug but lets say if it is a prompt that is initiating the conversation on all the LLMs, how tough can it be to just have a frontend wrapper with different LLM names with a backend logic just shuffling the initial response from only one wrapper into multiple responses and I am making more ๐ฐ
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๐ For the first time in LLM History! Introducing Agentic Compound LLMs in AnyLLM. What are agentic LLM's ? Agentic LLMs have access to all the 10+ llms in anyllm and know when you use any one of them to perform a specific task. They also have acces
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Master LLMs without paying a rupee. LLMCourse is a complete learning track to go from zero to LLM expert โ using just Google Colab. Youโll learn: โ The core math, Python, and neural network concepts โ How to train your own LLMs โ How to build and
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Retrieval-Augmented Generation (RAG) is a GenAI framework that enhances large language models (LLMs) by incorporating information from external knowledge bases, improving accuracy, relevance, and reliability of generated responses. Here's a more det
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3B LLM outperforms 405B LLM ๐คฏ Similarly, a 7B LLM outperforms OpenAI o1 & DeepSeek-R1 ๐คฏ ๐คฏ LLM: llama 3 Datasets: MATH-500 & AIME-2024 This has done on research with compute optimal Test-Time Scaling (TTS). Recently, OpenAI o1 shows that Test-
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๐๐ฎ๐ป๐ด๐๐ต๐ฎ๐ถ๐ป: ๐ง๐ต๐ฒ ๐๐ฎ๐๐๐ฒ๐๐-๐๐ฟ๐ผ๐๐ถ๐ป๐ด ๐๐ฟ๐ฎ๐บ๐ฒ๐๐ผ๐ฟ๐ธ ๐ณ๐ผ๐ฟ ๐๐๐ถ๐น๐ฑ๐ถ๐ป๐ด ๐๐ ๐๐ด๐ฒ๐ป๐๐ LangChain is quickly emerging as the go to framework for developing AI agents and intelligent LLM-powered applications. In a short
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Every day you skip reading this book = a missed opportunity. AI Engineering by @chipro is gold: โข Build with LLMs โข RAG & agents โข Dataset engineering โข Evaluation metrics that matter This isnโt just theory. Itโs how to build. #AI #LLM #RAG #Tec
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