vim-airline
lean & mean status/tabline for vim that's light as air
Stars: 17696
Vim-airline is a lean and mean status/tabline plugin for Vim that provides a nice statusline at the bottom of each Vim window. It consists of several sections displaying information such as mode, environment status, filename, filetype, file encoding, and current position in the file. The plugin is highly customizable and integrates with various plugins, providing a tiny core with extensibility in mind. It is optimized for speed, supports multiple themes, and integrates seamlessly with other plugins. Vim-airline is written in 100% Vimscript, eliminating the need for Python. The plugin aims to be stable and includes a unit testing suite for reliability.
README:
Lean & mean status/tabline for vim that's light as air.
When the plugin is correctly loaded, there will be a nice statusline at the bottom of each vim window.
That line consists of several sections, each one displaying some piece of information. By default (without configuration) this line will look like this:
+-----------------------------------------------------------------------------+
|~ |
|~ |
|~ VIM - Vi IMproved |
|~ |
|~ version 8.2 |
|~ by Bram Moolenaar et al. |
|~ Vim is open source and freely distributable |
|~ |
|~ type :h :q<Enter> to exit |
|~ type :help<Enter> or <F1> for on-line help |
|~ type :help version8<Enter> for version info |
|~ |
|~ |
+-----------------------------------------------------------------------------+
| A | B | C X | Y | Z | [...] |
+-----------------------------------------------------------------------------+
The statusline is the colored line at the bottom, which contains the sections (possibly in different colors):
section | meaning (example) |
---|---|
A | displays the mode + additional flags like crypt/spell/paste (INSERT) |
B | Environment status (VCS information - branch, hunk summary (master), battery level) |
C | filename + read-only flag (~/.vim/vimrc RO) |
X | filetype (vim) |
Y | file encoding[fileformat] (utf-8[unix]) |
Z | current position in the file |
[...] | additional sections (warning/errors/statistics) from external plugins (e.g. YCM, syntastic, ...) |
The information in Section Z looks like this:
10% ☰ 10/100 ln : 20
This means:
10% - 10 percent down the top of the file
☰ 10 - current line 10
/100 ln - of 100 lines
: 20 - current column 20
For a better look, those sections can be colored differently, depending on various conditions (e.g. the mode or whether the current file is 'modified')
- Tiny core written with extensibility in mind (open/closed principle).
- Integrates with a variety of plugins, including: vim-bufferline, fugitive, flog, unite, ctrlp, minibufexpl, gundo, undotree, nerdtree, tagbar, vim-gitgutter, vim-signify, quickfixsigns, syntastic, eclim, lawrencium, virtualenv, tmuxline, taboo.vim, ctrlspace, vim-bufmru, vimagit, denite, vim.battery and more.
- Looks good with regular fonts and provides configuration points so you can use unicode or powerline symbols.
- Optimized for speed - loads in under a millisecond.
- Extensive suite of themes for popular color schemes including solarized (dark and light), tomorrow (all variants), base16 (all variants), molokai, jellybeans and others. Note these are now external to this plugin. More details can be found in the themes repository.
- Supports 7.2 as the minimum Vim version.
- The master branch tries to be as stable as possible, and new features are merged in only after they have gone through a full regression test.
- Unit testing suite.
This plugin follows the standard runtime path structure, and as such it can be installed with a variety of plugin managers:
Plugin Manager | Install with... |
---|---|
Pathogen |
git clone https://github.com/vim-airline/vim-airline ~/.vim/bundle/vim-airline Remember to run :Helptags to generate help tags |
NeoBundle | NeoBundle 'vim-airline/vim-airline' |
Vundle | Plugin 'vim-airline/vim-airline' |
Plug | Plug 'vim-airline/vim-airline' |
VAM | call vam#ActivateAddons([ 'vim-airline' ]) |
Dein | call dein#add('vim-airline/vim-airline') |
minpac | call minpac#add('vim-airline/vim-airline') |
pack feature (native Vim 8 package feature) |
git clone https://github.com/vim-airline/vim-airline ~/.vim/pack/dist/start/vim-airline Remember to run :helptags ~/.vim/pack/dist/start/vim-airline/doc to generate help tags |
manual | copy all of the files into your ~/.vim directory |
If you don't like the defaults, you can replace all sections with standard statusline
syntax. Give your statusline that you've built over the years a face lift.
Themes have moved to another repository as of this commit.
Install the themes as you would this plugin (Vundle example):
Plugin 'vim-airline/vim-airline'
Plugin 'vim-airline/vim-airline-themes'
See vim-airline-themes for more.
Sections and parts within sections can be configured to automatically hide when the window size shrinks.
Automatically displays all buffers when there's only one tab open.
This is disabled by default; add the following to your vimrc to enable the extension:
let g:airline#extensions#tabline#enabled = 1
Separators can be configured independently for the tabline, so here is how you can define "straight" tabs:
let g:airline#extensions#tabline#left_sep = ' '
let g:airline#extensions#tabline#left_alt_sep = '|'
In addition, you can also choose which path formatter airline uses. This affects how file paths are
displayed in each individual tab as well as the current buffer indicator in the upper right.
To do so, set the formatter
field with:
let g:airline#extensions#tabline#formatter = 'default'
Here is a complete list of formatters with screenshots:
vim-airline integrates with a variety of plugins out of the box. These extensions will be lazily loaded if and only if you have the other plugins installed (and of course you can turn them off).
hunks (vim-gitgutter, vim-signify, coc-git & gitsigns.nvim)
vim-airline also supplies some supplementary stand-alone extensions. In addition to the tabline extension mentioned earlier, there is also:
The statusline can alternatively be drawn on top, making room for other plugins to use the statusline:
The example shows a custom statusline setting, that imitates Vims default statusline, while allowing
to call custom functions. Use :let g:airline_statusline_ontop=1
to enable it.
Every section is composed of parts, and you can reorder and reconfigure them at will.
Sections can contain accents, which allows for very granular control of visuals (see configuration here).
Completely transform the statusline to your liking. Build out the statusline as you see fit by extracting colors from the current colorscheme's highlight groups.
There's already powerline, why yet another statusline?
- 100% vimscript; no python needed.
What about vim-powerline?
- vim-powerline has been deprecated in favor of the newer, unifying powerline, which is under active development; the new version is written in python at the core and exposes various bindings such that it can style statuslines not only in vim, but also tmux, bash, zsh, and others.
I wrote the initial version on an airplane, and since it's light as air it turned out to be a good name. Thanks for flying vim!
:help airline
For the nice looking powerline symbols to appear, you will need to install a patched font. Instructions can be found in the official powerline documentation. Prepatched fonts can be found in the powerline-fonts repository.
Finally, you can add the convenience variable let g:airline_powerline_fonts = 1
to your vimrc which will automatically populate the g:airline_symbols
dictionary with the powerline symbols.
Solutions to common problems can be found in the Wiki.
Whoa! Everything got slow all of a sudden...
vim-airline strives to make it easy to use out of the box, which means that by default it will look for all compatible plugins that you have installed and enable the relevant extension.
Many optimizations have been made such that the majority of users will not see any performance degradation, but it can still happen. For example, users who routinely open very large files may want to disable the tagbar
extension, as it can be very expensive to scan for the name of the current function.
The minivimrc project has some helper mappings to troubleshoot performance related issues.
If you don't want all the bells and whistles enabled by default, you can define a value for g:airline_extensions
. When this variable is defined, only the extensions listed will be loaded; an empty array would effectively disable all extensions (e.g. :let g:airline_extensions = []
).
Also, you can enable caching of the various syntax highlighting groups. This will try to prevent some of the more expensive :hi
calls in Vim, which seem to be expensive in the Vim core at the expense of possibly not being one hundred percent correct all the time (especially if you often change highlighting groups yourself using :hi
commands). To set this up do :let g:airline_highlighting_cache = 1
. A :AirlineRefresh
will however clear the cache.
In addition you might want to check out the dark_minimal theme, which does not change highlighting groups once they are defined. Also please check the FAQ for more information on how to diagnose and fix the problem.
A full list of screenshots for various themes can be found in the Wiki.
The project is currently being maintained by Christian Brabandt and Bailey Ling.
If you are interested in becoming a maintainer (we always welcome more maintainers), please go here.
MIT License. Copyright (c) 2013-2021 Bailey Ling & Contributors.
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for vim-airline
Similar Open Source Tools
vim-airline
Vim-airline is a lean and mean status/tabline plugin for Vim that provides a nice statusline at the bottom of each Vim window. It consists of several sections displaying information such as mode, environment status, filename, filetype, file encoding, and current position in the file. The plugin is highly customizable and integrates with various plugins, providing a tiny core with extensibility in mind. It is optimized for speed, supports multiple themes, and integrates seamlessly with other plugins. Vim-airline is written in 100% Vimscript, eliminating the need for Python. The plugin aims to be stable and includes a unit testing suite for reliability.
evalverse
Evalverse is an open-source project designed to support Large Language Model (LLM) evaluation needs. It provides a standardized and user-friendly solution for processing and managing LLM evaluations, catering to AI research engineers and scientists. Evalverse supports various evaluation methods, insightful reports, and no-code evaluation processes. Users can access unified evaluation with submodules, request evaluations without code via Slack bot, and obtain comprehensive reports with scores, rankings, and visuals. The tool allows for easy comparison of scores across different models and swift addition of new evaluation tools.
stable-diffusion.cpp
The stable-diffusion.cpp repository provides an implementation for inferring stable diffusion in pure C/C++. It offers features such as support for different versions of stable diffusion, lightweight and dependency-free implementation, various quantization support, memory-efficient CPU inference, GPU acceleration, and more. Users can download the built executable program or build it manually. The repository also includes instructions for downloading weights, building from scratch, using different acceleration methods, running the tool, converting weights, and utilizing various features like Flash Attention, ESRGAN upscaling, PhotoMaker support, and more. Additionally, it mentions future TODOs and provides information on memory requirements, bindings, UIs, contributors, and references.
LLM-Pruner
LLM-Pruner is a tool for structural pruning of large language models, allowing task-agnostic compression while retaining multi-task solving ability. It supports automatic structural pruning of various LLMs with minimal human effort. The tool is efficient, requiring only 3 minutes for pruning and 3 hours for post-training. Supported LLMs include Llama-3.1, Llama-3, Llama-2, LLaMA, BLOOM, Vicuna, and Baichuan. Updates include support for new LLMs like GQA and BLOOM, as well as fine-tuning results achieving high accuracy. The tool provides step-by-step instructions for pruning, post-training, and evaluation, along with a Gradio interface for text generation. Limitations include issues with generating repetitive or nonsensical tokens in compressed models and manual operations for certain models.
PowerInfer
PowerInfer is a high-speed Large Language Model (LLM) inference engine designed for local deployment on consumer-grade hardware, leveraging activation locality to optimize efficiency. It features a locality-centric design, hybrid CPU/GPU utilization, easy integration with popular ReLU-sparse models, and support for various platforms. PowerInfer achieves high speed with lower resource demands and is flexible for easy deployment and compatibility with existing models like Falcon-40B, Llama2 family, ProSparse Llama2 family, and Bamboo-7B.
zipnn
ZipNN is a lossless and near-lossless compression library optimized for numbers/tensors in the Foundation Models environment. It automatically prepares data for compression based on its type, allowing users to focus on core tasks without worrying about compression complexities. The library delivers effective compression techniques for different data types and structures, achieving high compression ratios and rates. ZipNN supports various compression methods like ZSTD, lz4, and snappy, and provides ready-made scripts for file compression/decompression. Users can also manually import the package to compress and decompress data. The library offers advanced configuration options for customization and validation tests for different input and compression types.
flashinfer
FlashInfer is a library for Language Languages Models that provides high-performance implementation of LLM GPU kernels such as FlashAttention, PageAttention and LoRA. FlashInfer focus on LLM serving and inference, and delivers state-the-art performance across diverse scenarios.
ktransformers
KTransformers is a flexible Python-centric framework designed to enhance the user's experience with advanced kernel optimizations and placement/parallelism strategies for Transformers. It provides a Transformers-compatible interface, RESTful APIs compliant with OpenAI and Ollama, and a simplified ChatGPT-like web UI. The framework aims to serve as a platform for experimenting with innovative LLM inference optimizations, focusing on local deployments constrained by limited resources and supporting heterogeneous computing opportunities like GPU/CPU offloading of quantized models.
FlexFlow
FlexFlow Serve is an open-source compiler and distributed system for **low latency**, **high performance** LLM serving. FlexFlow Serve outperforms existing systems by 1.3-2.0x for single-node, multi-GPU inference and by 1.4-2.4x for multi-node, multi-GPU inference.
EmbodiedScan
EmbodiedScan is a holistic multi-modal 3D perception suite designed for embodied AI. It introduces a multi-modal, ego-centric 3D perception dataset and benchmark for holistic 3D scene understanding. The dataset includes over 5k scans with 1M ego-centric RGB-D views, 1M language prompts, 160k 3D-oriented boxes spanning 760 categories, and dense semantic occupancy with 80 common categories. The suite includes a baseline framework named Embodied Perceptron, capable of processing multi-modal inputs for 3D perception tasks and language-grounded tasks.
DALM
The DALM (Domain Adapted Language Modeling) toolkit is designed to unify general LLMs with vector stores to ground AI systems in efficient, factual domains. It provides developers with tools to build on top of Arcee's open source Domain Pretrained LLMs, enabling organizations to deeply tailor AI according to their unique intellectual property and worldview. The toolkit contains code for fine-tuning a fully differential Retrieval Augmented Generation (RAG-end2end) architecture, incorporating in-batch negative concept alongside RAG's marginalization for efficiency. It includes training scripts for both retriever and generator models, evaluation scripts, data processing codes, and synthetic data generation code.
camel
CAMEL is an open-source library designed for the study of autonomous and communicative agents. We believe that studying these agents on a large scale offers valuable insights into their behaviors, capabilities, and potential risks. To facilitate research in this field, we implement and support various types of agents, tasks, prompts, models, and simulated environments.
OpenAdapt
OpenAdapt is an open-source software adapter between Large Multimodal Models (LMMs) and traditional desktop and web Graphical User Interfaces (GUIs). It aims to automate repetitive GUI workflows by leveraging the power of LMMs. OpenAdapt records user input and screenshots, converts them into tokenized format, and generates synthetic input via transformer model completions. It also analyzes recordings to generate task trees and replay synthetic input to complete tasks. OpenAdapt is model agnostic and generates prompts automatically by learning from human demonstration, ensuring that agents are grounded in existing processes and mitigating hallucinations. It works with all types of desktop GUIs, including virtualized and web, and is open source under the MIT license.
Fira
Fira is a memory-efficient training framework for Large Language Models (LLMs) that enables full-rank training under low-rank constraint. It introduces a method for training with full-rank gradients of full-rank weights, achieved with just two lines of equations. The framework includes pre-training and fine-tuning functionalities, packaged as a Python library for easy use. Fira utilizes Adam optimizer by default and provides options for weight decay. It supports pre-training LLaMA models on the C4 dataset and fine-tuning LLaMA-7B models on commonsense reasoning tasks.
RWKV-Runner
RWKV Runner is a project designed to simplify the usage of large language models by automating various processes. It provides a lightweight executable program and is compatible with the OpenAI API. Users can deploy the backend on a server and use the program as a client. The project offers features like model management, VRAM configurations, user-friendly chat interface, WebUI option, parameter configuration, model conversion tool, download management, LoRA Finetune, and multilingual localization. It can be used for various tasks such as chat, completion, composition, and model inspection.
airflint
Airflint is a tool designed to enforce best practices for all your Airflow Directed Acyclic Graphs (DAGs). It is currently in the alpha stage and aims to help users adhere to recommended practices when working with Airflow. Users can install Airflint from PyPI and integrate it into their existing Airflow environment to improve DAG quality. The tool provides rules for function-level imports and jinja template syntax usage, among others, to enhance the development process of Airflow DAGs.
For similar tasks
vim-airline
Vim-airline is a lean and mean status/tabline plugin for Vim that provides a nice statusline at the bottom of each Vim window. It consists of several sections displaying information such as mode, environment status, filename, filetype, file encoding, and current position in the file. The plugin is highly customizable and integrates with various plugins, providing a tiny core with extensibility in mind. It is optimized for speed, supports multiple themes, and integrates seamlessly with other plugins. Vim-airline is written in 100% Vimscript, eliminating the need for Python. The plugin aims to be stable and includes a unit testing suite for reliability.
For similar jobs
kaito
Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.
ai-on-gke
This repository contains assets related to AI/ML workloads on Google Kubernetes Engine (GKE). Run optimized AI/ML workloads with Google Kubernetes Engine (GKE) platform orchestration capabilities. A robust AI/ML platform considers the following layers: Infrastructure orchestration that support GPUs and TPUs for training and serving workloads at scale Flexible integration with distributed computing and data processing frameworks Support for multiple teams on the same infrastructure to maximize utilization of resources
tidb
TiDB is an open-source distributed SQL database that supports Hybrid Transactional and Analytical Processing (HTAP) workloads. It is MySQL compatible and features horizontal scalability, strong consistency, and high availability.
nvidia_gpu_exporter
Nvidia GPU exporter for prometheus, using `nvidia-smi` binary to gather metrics.
tracecat
Tracecat is an open-source automation platform for security teams. It's designed to be simple but powerful, with a focus on AI features and a practitioner-obsessed UI/UX. Tracecat can be used to automate a variety of tasks, including phishing email investigation, evidence collection, and remediation plan generation.
openinference
OpenInference is a set of conventions and plugins that complement OpenTelemetry to enable tracing of AI applications. It provides a way to capture and analyze the performance and behavior of AI models, including their interactions with other components of the application. OpenInference is designed to be language-agnostic and can be used with any OpenTelemetry-compatible backend. It includes a set of instrumentations for popular machine learning SDKs and frameworks, making it easy to add tracing to your AI applications.
BricksLLM
BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students
kong
Kong, or Kong API Gateway, is a cloud-native, platform-agnostic, scalable API Gateway distinguished for its high performance and extensibility via plugins. It also provides advanced AI capabilities with multi-LLM support. By providing functionality for proxying, routing, load balancing, health checking, authentication (and more), Kong serves as the central layer for orchestrating microservices or conventional API traffic with ease. Kong runs natively on Kubernetes thanks to its official Kubernetes Ingress Controller.