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  Photo by Andrea Piacquadio at pexels Multiagent systems (MAS) are distributed systems comprising multiple autonomous agents that interact and cooperate to achieve common goals. Integrating open-source Large Language Models (LLMs) into MAS development enables agents to leverage advanced natural language processing (NLP) capabilities, enhancing their decision-making and communication. Key Components Open-Source LLM : Utilize open-source LLMs like Bloom, LLaMA, or LaMDA to equip agents with advanced NLP capabilities. Agent Framework : Choose a suitable agent framework (e.g., Python's Multiagent Framework, JADE, or PyAgent) to develop and manage agents. Communication Protocol: Establish a communication protocol (e.g., FIPA-ACL, KQML) for agents to exchange information and coordinate actions. Knowledge Representation: Define a knowledge representation scheme (e.g., ontologies, semantic networks) to facilitate agent understanding and sharing of information. Development Steps Agent D...

  Photo by energepic.com at pexel You can create your own coding auto-completion co-pilot using Hugging Face LangChain and Phi3 SLM ! Here's a breakdown of the steps involved: 1. Setting Up the Environment: Install the required libraries: Bash pip install langchain transformers datasets phi3 Download the Phi3 SLM model: Bash from transformers import AutoModelForSeq2SeqLM model_name = "princeton-ml/ph3_base" model = AutoModelForSeq2SeqLM.from_pretrained(model_name) 2. Preprocessing Code for LangChain: LangChain provides a AutoTokenizer class to preprocess code. Identify the programming language you want to support and install the corresponding tokenizer from Hugging Face. For example, for Python: Bash from langchain.llms import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained( "openai/gpt-code-code" ) Define a function to preprocess code into LangChain format. This might involve splitting the code into tokens, adding special tokens (e.g., start/e...