AI Agents - Introduction to LLM and AI Terminologies
LLM LLM is a model, which means an equation. Example: y = mx + c y = m1x^3 + m2x^2 + m3x + m4 A model is actually made up of weights . In any model, e.g., ChatGPT model or Gemini model, they would have used a large amount of input to train the model. Input means a large amount of text/image data that is available on the internet. The input would have been fed into the Transformer architecture to get the output, which is the model. Weights are floating-point numbers that represent the model's learned parameters. A 10B or 100B parameter model means how many parameters (weights) are present inside the model. We cannot store a large-parameter model on our computer due to inadequate storage and computational power. Storage and CPU/GPU power decide what size of model can be run on a computer. To run a model locally, we can use one of the following tools: Llama.cpp Ollama LM Studio Open Weight Model vs Open Source Model An open-weight model shares its model weights. So, we can run them, fine-tune them, and host them on a local system. Here, the training code, data, and full methodology are not shared. Whereas, in an open-source model , the weights, training code, data, and sometimes the dataset are shared. Why Do We Need to Use LLMs? LLM is a next-word predictor . Suppose we ask: "Hi, how..." The answer can be: How are you? How do you do? How is your life? etc. These are possibilities. Here, most of the time, the answer will be "How are you?" because if a word has more presence, it has a higher possibility of occurring. Each possibility will have a score between 0 and 1 . We have 3 controlling parameters to control the output generated by the LLM. 1. Temperature Usually set from 0–1 . It controls the randomness of the model. If the value is 0–0.3 , which is low, it means generating the most likely words, i.e., facts or commonly occurring words. If the value is high, the model will choose less likely words. We use this high value in storytelling and creative writing . 2. To