--- 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}} } ```