How to Setup Language Model Locally without Code — LM Studio.pptx

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Welcome to a groundbreaking tutorial on effortlessly setting up language models locally without a single line of code! In this step-by-step guide, we'll dive into the world of LM Studio, unlocking the secrets to seamless language model deployment on your own machine. Whether you're a seasoned developer or a complete beginner, this tutorial is designed to empower you with the skills to master local setup without the complexities of coding. Join us as we demystify the process, making AI accessible to everyone. Get ready to unleash the power of language models with LM Studio – your ticket to simplified, code-free AI magic! Video URL : https://youtu.be/_8LaGrbNr0chttps://youtu.be/_8LaGrbNr0c

Technology

Installation:
Download &
Install LM
Studio
• Visit the LM Studio
website to download
the Windows or mac
version. Once
downloaded, the file
can be opened to start
the installation
process.
(c) Cloudperceptor

With LM
Studio, you
can …
• Run LLMs on your laptop, entirely offline
• Use models through the in-app Chat UI or an OpenAI
compatible local server
• Download any compatible model files from
HuggingFace repositories
• Discover new & noteworthy LLMs in the app’s home
page
(c) Cloudperceptor

Summary
• In a software environment like LM Studio, a Local
Inference Server would allow you to run machine
learning models on your own hardware, and API Calls
would be the method by which you send data to and
receive data from these models.
(c) Cloudperceptor

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How to Setup Language Model Locally without Code — LM Studio.pptx

1. (c) Cloudperceptor

2. Installation: Download & Install LM Studio • Visit the LM Studio website to download the Windows or mac version. Once downloaded, the file can be opened to start the installation process. (c) Cloudperceptor

3. With LM Studio, you can … • Run LLMs on your laptop, entirely offline • Use models through the in-app Chat UI or an OpenAI compatible local server • Download any compatible model files from HuggingFace repositories • Discover new & noteworthy LLMs in the app’s home page (c) Cloudperceptor

4. Summary • In a software environment like LM Studio, a Local Inference Server would allow you to run machine learning models on your own hardware, and API Calls would be the method by which you send data to and receive data from these models. (c) Cloudperceptor

How to Setup Language Model Locally without Code — LM Studio.pptx

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