---
license: mit
task_categories:
- text-generation
language:
- en
tags:
- bash
- shell
- code
- instruction-tuning
- sft
- command-line
size_categories:
- 10K<n<100K
pretty_name: Bash Instruction-Tuning Dataset
configs:
- config_name: default
  data_files: bash_dataset.jsonl
---

# Bash Instruction-Tuning Dataset (~55k)

A synthetic instruction-tuning dataset that pairs natural-language requests with
correct Bash solutions, built for fine-tuning small LLMs to translate plain
requests into runnable shell commands, pipelines, and scripts.

Each example is a chat conversation (system / user / assistant) plus two metadata
fields (`category`, `utility`) for slicing and analysis.

## Format

One JSON object per line (`bash_dataset.jsonl`):

```json
{
  "messages": [
    {"role": "system",    "content": "You are a Bash expert."},
    {"role": "user",      "content": "Show the last 20 lines of error.log."},
    {"role": "assistant", "content": "tail -n 20 error.log"}
  ],
  "category": "single",
  "utility": "tail"
}
```

- `messages` — the training conversation. The system prompt is one of 5 equivalent
  Bash-assistant prompts (rotated so the model doesn't overfit a single string).
- `category` — `single` | `pipeline` | `script` (see below).
- `utility` — the primary command of the solution (e.g. `grep`, `awk`, `find`,
  `for`), used to enforce a per-command cap and to analyze coverage.

Scripts (`category == "script"`) contain real multi-line Bash (JSON-escaped `\n`),
typically with `set -euo pipefail`, functions, `getopts`, `trap` cleanup, loops,
here-docs, etc.

## Statistics

| Metric | Value |
|---|---|
| Examples | 55,000 |
| `single` (one-shot commands) | 22,000 (40%) |
| `pipeline` (pipes, `&&`/`\|\|`, `$(...)`, `xargs`) | 19,250 (35%) |
| `script` (multi-line) | 13,750 (25%) |
| Distinct primary utilities | 89 |
| Max share of any one utility | 4.00% (hard cap 2,200/util) |
| Unique user (NL) strings | 92.9% |
| Unique assistant (Bash) strings | 75.3% |

**Validity**: 100% pass `bash -n` (0 syntax errors). Static analysis with
shellcheck flags no warning-level findings on **96.3%** of a 4,000-example sample;
almost all remaining findings are a single style nit (`SC2010`, `ls | grep`).

**Coverage of system/info utilities** that small models often get wrong
(counts include single + pipeline + script uses):

| util | n | util | n | util | n |
|---|---|---|---|---|---|
| journalctl | 2200 | lsof | 648 | ss | 481 |
| pstree | 753 | renice | 365 | nice | 293 |
| vmstat | 288 | netstat | 234 | iostat | 183 |
| w | 158 | dmesg | 154 | free | 90 |
| uptime | 34 | hostname | 25 | | |

(`uptime` / `hostname` are lower because their genuine idiomatic command space is
small; they are covered with real flag variants rather than padded phrasings.)

## How it was built

Generated by a recipe engine (`generate.py`, included). Each recipe family emits
`(request, command)` pairs by combining hand-written phrasing templates
(imperative / question / casual) with realistic parameter pools (plausible file
names, directories, ports, services, users, patterns). The generator enforces:

- category quotas (40 / 35 / 25),
- a 4% per-utility cap so no command dominates (a real failure mode of earlier runs),
- minimum floors for the info/system utilities above,
- SHA-1 deduplication of `(request, command)` pairs,
- a `bash -n` syntax gate on every command before it is written.

## Loading

```python
from datasets import load_dataset
ds = load_dataset("json", data_files="bash_dataset.jsonl", split="train")
print(ds[0]["messages"])

# analyze by slice
import collections
print(collections.Counter(ds["category"]))
print(collections.Counter(ds["utility"]).most_common(15))
```

The dataset viewer will expose `messages`, `category`, and `utility` as columns.

## Intended use & limitations

**Good for**: teaching a small model to map natural-language requests to correct
single Bash commands, short pipelines, and small scripts — including the
system/info utilities listed above.

**Limitations (be aware before relying on it):**

- **Synthetic / semi-templated.** Variety comes from recombining templates and
  token pools, not from human authorship. Phrasing shapes repeat (e.g. most
  script requests open with "Write a script that…"); ~25% of commands recur with
  different phrasings.
- **Validity ≠ full semantic correctness.** `bash -n` + shellcheck prove the
  commands parse and are lint-clean; they do not prove every command perfectly
  satisfies its request. Some pairs are plausible-but-approximate (e.g. an `awk`
  column index assumes a particular log layout).
- **Single-turn, one canonical answer.** No explanations, alternatives, negative
  examples, or multi-turn dialogue.
- **Not executed at scale on real Linux.** Many commands (`systemctl`,
  `journalctl`, `apt`, `vmstat`, …) are idiomatic but were validated statically,
  not run.

## Files

- `bash_dataset.jsonl` — the dataset (55,000 rows).
- `generate.py` — the generator (reproducible; resumable via `progress.json`).
- `README.md` — this card.