rajivmehtapy/shell-script-specialist-dataset

Full-Spectrum Shell Script Specialist Dataset

This dataset contains 1,000 curated, unique ChatML conversation records engineered to fine-tune a specialist language model for Production-Grade Shell Scripting (Bash 5+, POSIX `/bin/sh`, `jq`, `awk`, `sed`).

It was used to train the rajivmehtapy/gemma-4-e4b-shell-specialist model using Unsloth.

Dataset Splits

Modules Covered

1. 1.Production Bash 5+ Automation (35%): Archival rotations, health probes with exponential backoff, systemd service watchdogs, disk/memory threshold monitors.
2. 2.Minimal POSIX `/bin/sh` Portability (20%): Alpine Linux / Docker entrypoints, socket polling (nc -z), POSIX parameter expansions, zero Bashisms.
3. 3.Advanced CLI Stream Wrangling (25%): Complex jq queries with atomic file replacements, awk columnar parsing, sed stream updates, null-delimited find / xargs pipelines.
4. 4.Defensive Hardening & Bug Refactoring (20%): Auditing fragile bash snippets, eliminating unquoted variable risks, adding set -euo pipefail and trap handlers.

Data Schema (ChatML JSONL)

{
  "messages": [
    {
      "role": "user",
      "content": "Write a bash script to archive and remove .log files older than 14 days in /var/log with dry-run support."
    },
    {
      "role": "assistant",
      "content": "#!/usr/bin/env bash\nset -euo pipefail\n\nDRY_RUN=false\n[[ \"${1:-}\" == \"--dry-run\" ]] && DRY_RUN=true\n..."
    }
  ]
}

Usage with Hugging Face Datasets

from datasets import load_dataset

dataset = load_dataset("rajivmehtapy/shell-script-specialist-dataset")
print(dataset)
print(dataset["train"][0])

Next Steps: DPO & GRPO Alignment

When you spin up your next machine for DPO and GRPO, you can immediately resume using the following one-liners:

1. Pulling the Policy Model on the New Machine

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="rajivmehtapy/gemma-4-e4b-shell-specialist",
    max_seq_length=1024,
    load_in_4bit=True,
)

2. Pulling the Dataset on the New Machine

from datasets import load_dataset

dataset = load_dataset("rajivmehtapy/shell-script-specialist-dataset")

3. Ready for DPO

• —Use the SFT model as the reference policy.
• —Provide (prompt, chosen, rejected) triplets (where chosen contains defensive standards like set -euo pipefail and rejected contains common bash antipatterns).
• —Train with trl.DPOTrainer.

4. Ready for GRPO

• —Use the SFT model as the actor model.
• —Set up automated rule-based reward functions (shellcheck returncode, exit code in Docker sandbox, security parameter validation).
• —Train with trl.GRPOTrainer.

Everything is backed up and ready for your next phase!