YAML Metadata Warning:The task_categories "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

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

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.

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