Think Different

  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...

  I am trying to preparing a Dataset for Fine-Tuning on Pathology Lab Data. 1. Dataset Collection    - Sources:  Gather data from pathology lab reports, medical journals, and any other relevant medical documents.    - Format:  Ensure that the data is in a readable format like CSV, JSON, or text files. 2. Data Preprocessing    - Cleaning:  Remove any irrelevant data, correct typos, and handle missing values.    - Formatting:  Convert the data into a format suitable for fine-tuning, usually pairs of input and output texts.    - Example Format:      - Input:  "Patient exhibits symptoms of hyperglycemia."      - Output:  "Hyperglycemia" 3. Tokenization    - Tokenize the text using the tokenizer that corresponds to the model you intend to fine-tune. Example Code for Dataset Preparation Using Pandas and Transformers for Preprocessing 1. Install Required Libraries: ...