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Building and Training Your Own SLM

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Creating an independent Small Language Model (SLM) is a rewarding project that bridges the gap between deep learning theory and practical, local application. By keeping your model local, you retain full control over your data and system architecture. Below is a structured approach to building and training your own model from the ground up on a local Ubuntu environment. Before you begin, ensure your development environment is optimized for local computation. A robust setup with a capable GPU (such as an NVIDIA RTX series) and sufficient RAM is recommended for efficient training. Use Ubuntu for a stable, customizable development environment. Verify your environment with the following commands:    * Update your package lists:  sudo apt update.    * Install pip:  sudo apt install python3-pip    *I nstall necessary libraries:  pip install torch tiktoken An SLM relies on the quality of its input data. For a personal AI, curated, factual...
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Windows 10X on Hyper-V emulator

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Use this guide if you have an amd processor

For use with Hyper-V manager in windows 10 pro



First we need to enable virtualisation in bios settings, mine was called secure virtual machine = enabled

Go to control panel, programs, turn windows features on and off. Scroll down & check the Hyper-V tab


Search for Hyper-V manager in Cortana 

Create a new virtual machine 

Tap on New virtual machine and browse to the vhdx file


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