| ---
|
| 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.
|
|
|
