Datasets:
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---
license: apache-2.0
task_categories:
- text-generation
language:
- ru
- ar
- en
- zh
- de
- fr
- es
- pt
- ja
- ko
tags:
- linux
- shell
- commands
- terminal
- multilingual
- development
- system-administration
pretty_name: LinLM
size_categories:
- n<1K
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: ru
dtype: string
- name: ch
dtype: string
- name: eng
dtype: string
- name: de
dtype: string
- name: fr
dtype: string
- name: es
dtype: string
- name: pt
dtype: string
- name: ja
dtype: string
- name: ko
dtype: string
- name: ar
dtype: string
- name: completion
dtype: string
- name: category
dtype: string
splits:
- name: train
num_bytes: 328500
num_examples: 910
download_size: 160032
dataset_size: 328500
---
## LinLM Dataset
A curated synthetic dataset for Linux command inference
Natural language description -> shell commands
Features:
- Supports 10+ languages
- Arch Linux commands recognition
- Fine-tune LLM for development, system administration, file operations, Git, Docker, and more
## Usage
```python
from datasets import load_dataset
dataset = load_dataset("missvector/linux-commands")
def format_for_training(example):
return {
"prompt": f"Convert to Linux command: {example['eng']}",
"completion": example['completion']
}
training_data = dataset['train'].map(format_for_training)
```
## Out-of-Scope Use
- Not for production deployment without additional validation
- Commands should be reviewed before execution
## Related Projects
- [llama-dynamic-context](https://github.com/vifirsanova/llama-dynamic-context) - command inference tool tested with this dataset
## Citation
If you use this dataset in your research, please cite:
```bibtex
@misc{linuxcommands2024,
author = {V. Firsanova},
title = {LinLM Dataset},
year = {2025},
publisher = {Hugging Face Datasets},
howpublished = {\url{https://huggingface.co/datasets/missvector/linux-commands}}
}
``` |