This document summarises commonly used Vim keyboard shortcuts.
Sentences are short. Descriptions are concise. Use this as a quick reference.
Command | Description |
---|---|
alias |
Create an alias for a shell command (shortcut). |
awk |
Pattern scanning and processing language; search and process text. |
basename |
Strip directory and suffix from filenames. |
cal |
Display a calendar. |
cat |
Concatenate and display file content. |
chgrp |
Change group ownership of a file or directory. |
sudo journalctl -u anydesk.service -n 200 --no-pager -o cat | sed -n '1,200p' | |
# quick scan for license/commercial hints | |
sudo journalctl -u anydesk.service --no-pager | grep -i -E 'license|commercial|trial|offline|error|tls|certificate' || true | |
# raw network | |
ping -c 4 8.8.8.8 | |
# DNS resolution of AnyDesk status host | |
dig +short status.anydesk.com || nslookup status.anydesk.com |
This document describes steps to configure a Lenovo ThinkStation P2 pre-installed with Windows for dual-booting with Ubuntu.
Follow the steps carefully. Back up important data before proceeding.
- A full backup of important files.
- A USB flash drive (≥ 8 GB).
- Ubuntu ISO image (matching your preferred Ubuntu release).
- Rufus utility for creating bootable USB on Windows.
This document lists common Docker commands.
Each entry includes a short description and an example.
Command | Description | Example |
---|---|---|
docker --version |
Show Docker client version. | docker --version |
docker info |
Show system-wide Docker information. | docker info |
docker login |
Log in to a container registry. | docker login ghcr.io |
Setup WSL2
Below is a step-by-step procedure to install WSL 2 and Ubuntu 24.04 on a Windows 10/11 machine.
-
Verify Windows Version and Virtualization
-
Confirm that your Windows build is at least 19044 (Windows 10, version 21H2) or later.
-
Ensure that CPU virtualization (Intel VT-x or AMD-V) is enabled in BIOS/UEFI.
-
This document provides a step-by-step guide to setting up a Python virtual environment and installing TensorFlow v2.18.1 with GPU support on a machine running Ubuntu 24.04 with a compatible NVIDIA GPU.
Using a virtual environment is highly recommended to isolate your project's dependencies and avoid conflicts with other Python projects on your system.
To use TensorFlow with CUDA, you must have a compatible NVIDIA GPU and the correct software installed.
This guide will walk you through the process of setting up Unsloth with NVIDIA GPU support within your Windows Subsystem for Linux 2 (WSL2) environment, specifically using an Ubuntu distribution. This setup is crucial for efficiently fine-tuning and running large language models locally.
Before you begin in WSL2, ensure your Windows host machine has the latest NVIDIA GPU drivers installed. WSL2 relies on these drivers for GPU passthrough.
- Action: Download and install the latest drivers for your NVIDIA GPU from the official NVIDIA website: https://www.nvidia.com/drivers
- Verification: After installation, it's a good idea to restart your Windows machine.
First save your model to 16bit via:
model.save_pretrained_merged("merged_model", tokenizer, save_method = "merged_16bit",)
Compile llama.cpp from source like below:
apt-get update
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
git clone https://github.com/ggerganov/llama.cpp