---
language:
- en
license: apache-2.0
size_categories:
- n<1K
task_categories:
- text-generation
- text2text-generation
tags:
- synthetic
- instruction-tuning
- bioinformatics
- data-engineering
- pandas
- bash
- aws
- cron
- json
- qlora
pretty_name: Bio-DevOps Synthetic Instructions
---

# Bio-DevOps Synthetic Instructions

This dataset contains synthetic instruction-following examples for biomedical-style data-engineering and scientific-computing workflows.

It was created for educational and portfolio use as part of a LoRA/QLoRA fine-tuning project using `Qwen/Qwen2.5-Coder-7B-Instruct`.

Related model:

[`AiLLMBS/qwen25-coder-bio-devops-lora`](https://huggingface.co/AiLLMBS/qwen25-coder-bio-devops-lora)

## Dataset Contents

The dataset includes synthetic examples for:

- Python CSV validation
- pandas duplicate checks
- bash mount checks
- AWS S3 sync command generation
- cron expression explanation
- FASTQ manifest generation
- reproducible workflow checklist generation
- structured JSON extraction from synthetic workflow messages

## Files

- `train.jsonl`
- `eval.jsonl`

Each row contains a chat-style instruction example with:

- `messages`
- `meta.task_type`
- `meta.expected_keywords`
- `meta.requires_json`

## Data Source

This dataset is synthetically generated by project scripts.

It does not contain:

- PHI
- private employer data
- proprietary tickets
- internal emails
- client-specific workflows
- copyrighted book text

The examples use synthetic sample IDs, site IDs, file paths, S3 bucket names, and workflow messages.

## Intended Use

This dataset is intended for:

- educational LoRA/QLoRA fine-tuning
- instruction-format experimentation
- synthetic code-assistant training examples
- portfolio demonstration of dataset creation and model evaluation
- safe local experimentation with coding-focused LLM adapters

## Not Intended For

This dataset is not intended for:

- clinical decision support
- medical diagnosis
- training on PHI
- reproducing private employer workflows
- production automation without human review

## Limitations

The dataset is synthetic and template-based. Models trained on it may overfit to repeated patterns and may not generalize well to diverse real-world workflows.

Generated code, shell commands, and AWS commands should always be reviewed before use.

## Recommended Evaluation

Models trained on this dataset should be evaluated with:

- held-out prompts
- keyword coverage
- JSON validity checks
- unit tests for generated Python code
- manual review of shell commands
- base-model vs adapter comparison
- failure-case analysis

## License

This dataset is released under the Apache-2.0 license.