Dive deep into the world of Large Language Model (LLM) parameters with this comprehensive tutorial. Whether you're using Ollama or another LLM tool, this video will give you an overview of the essential parameters you need to understand to get the most out of your AI models.

## What you will learn:
– Detailed explanation of important parameters such as temperature, context size (num_ctx) and seed
– Advanced sampling techniques including top_k, top_p and mirostat
– How to manage iteration and creativity in model outcomes
– Practical tips for optimizing model performance and memory usage

## Highlights:
– Extensive discussion of temperature and its impact on modeling creativity
– How to maximize context size in Ollama for models like LLaMA 3.1
– Understand and use filler words, repeated punishments and sampling methods
– Investigation of mirostat parameters and their effect on text coherence and diversity
– Tips for configuring parameters in Ollama's model file and command line interface

Whether you are a beginner looking to understand the basics or an advanced user looking to refine your models, this video will provide valuable insights into the inner workings of LLMs. Learn how to balance coherence, creativity, and performance to get the best results for your AI projects.

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#AI #MachineLearning #Ollama #LLM #Artificial Intelligence #TechTutorial

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00:00 Introduction
00:22 The list of parameters
00:39 Start with temperature
02:10 Context Size
03:07 Expanding Context in Ollama
03:48 Where to find the Max Size
04:43 Filler sentences
05:04 Other repeat parameters
06:00 Above_k
06:13 Above_P
06:35 am
07:01 Tail-free sampling
07:32 Seed
08:47 Using Mirostat
09:14 Confusion and surprise
10:40 Number Prediction

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