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IT & Sysadmin Fine-Tuning Dataset

Linux, Docker, networking, cloud infrastructure

Fine-tune a model on this dataset

$19 for the dataset. Training is priced separately by model, starting at $47.

What is in this dataset

785 instruction-response pairs covering linux, docker, networking, cloud infrastructure. Each pair is a realistic question with a full written answer, in the JSONL format used for supervised fine-tuning. No synthetic filler, no duplicated rows.

Example pairs

Real rows from the dataset, shown in full.

My NGINX service on Ubuntu 22.04 fails to start with 'Failed to start A high performance web server and a reverse proxy server'. The journalctl shows 'bind() to 0.0.0.0:80 failed (98: Address already in use)'. How do I fix this?

The error indicates port 80 is already occupied. First, identify the process: `sudo ss -tlnp | grep :80`. Common culprits are Apache (`apache2`) or another NGINX instance. If it's Apache, stop and disable it: `sudo systemctl stop apache2 && sudo systemctl disable apache2`. If it's a lingering NGINX process, kill it: `sudo fuser -k 80/tcp`. Then verify no other services (like a Docker container) bind to 80: `sudo docker ps --format '{{.Names}} {{.Ports}}'`. After clearing the port, start NGINX: `sudo systemctl start nginx && sudo systemctl status nginx`. If the issue recurs, check for a misconfigured site that binds to the same port in…

Write a bash script that monitors disk usage for all mounted partitions and sends a warning email if any partition exceeds 80% usage. The script should accept a threshold as an argument and log results to /var/log/disk_monitor.log.

```bash #!/bin/bash # Usage: ./disk_monitor.sh [threshold_percent] THRESHOLD=${1:-80} LOG_FILE="/var/log/disk_monitor.log" mail_alert() { local partition="$1" local usage="$2" echo "Warning: Partition $partition is at ${usage}% usage." | mail -s "Disk Usage Alert: $partition" root echo "$(date): ALERT sent for $partition at ${usage}%" >> "$LOG_FILE" } df -h | grep '^/dev/' | while read -r line; do usage=$(echo "$line" | awk '{print $5}' | sed 's/%//') partition=$(echo "$line" | awk '{print $1}') mount=$(echo "$line"…

My PostgreSQL process is consuming 90% of CPU and 6GB of RAM on a 8GB server. I need to diagnose the cause without restarting the database. What commands should I run?

First, identify the specific queries causing load. Run as postgres user: `psql -c "SELECT pid, now() - pg_stat_activity.query_start AS duration, query, state FROM pg_stat_activity WHERE state != 'idle' ORDER BY duration DESC LIMIT 10;"`. Check CPU usage per query with: `top -b -n1 -p $(pgrep -d',' postgres) | grep postgres`. For memory, inspect shared buffers: `psql -c "SHOW shared_buffers;"` and work_mem. Use `htop` and sort by CPU/MEM. Also check for blocking locks: `psql -c "SELECT blocked_locks.pid AS blocked_pid, blocking_locks.pid AS blocking_pid FROM pg_catalog.pg_locks blocked_locks JOIN pg_catalog.pg_locks blocking_locks ON…

Who this is for

DevOps and sysadmin assistants working with Linux, Docker, and cloud infrastructure.

Which models this works with

The dataset is plain JSONL, so it works with any instruction-tuned open model. On Rebiha you can train it directly on:

Training uses LoRA, or QLoRA on a 4-bit base for larger models. Your base model's weights are never modified — training produces a separate adapter.

How it works

  1. Pick this dataset and a base model.
  2. Rebiha runs the job on a managed GPU. You set up nothing.
  3. Download your model: a ready-to-run GGUF, plus developer assets (adapter, tokenizer, configs).

Fine-tuning or RAG?

Use RAG when the model needs facts that change — your prices, your documents, today's inventory. Use fine-tuning when you want consistent style and structure without prompting for it every time. This dataset teaches the shape of a good answer in this domain, not facts to look up.

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