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README.md

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+---
+language:
+- en
+license: apache-2.0
+size_categories:
+- n<1K
+task_categories:
+- text-generation
+- question-answering
+- 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.