This repository contains a LoRA adapter fine-tuned from Qwen/Qwen2.5-Coder-7B-Instruct on a synthetic biomedical data-engineering instruction dataset.
Intended Use
This adapter is intended for educational and portfolio use. It is designed to help with tasks such as:
Python CSV validation
pandas data cleaning
bash scripting
AWS S3 command generation
cron explanation
FASTQ manifest generation
reproducible workflow checklists
Not Intended For
This model is not intended for:
clinical decision support
medical diagnosis
handling PHI
production automation without human review
private/proprietary workflow reproduction
Base Model
Base model: Qwen/Qwen2.5-Coder-7B-Instruct
Training Method
Method: QLoRA / LoRA adapter training
Quantization: 4-bit NF4
LoRA rank: 16
LoRA alpha: 32
LoRA dropout: 0.05
Hardware: local NVIDIA RTX 5090 32 GB
Dataset: synthetic instruction examples generated by project scripts
Dataset
Training dataset:
AiLLMBS/bio-devops-synthetic-instructions
The dataset is synthetic and was generated for educational LoRA/QLoRA fine-tuning. It does not contain PHI, private employer data, proprietary tickets, internal emails, client-specific workflows, or copyrighted book text.
The dataset includes synthetic examples for Python CSV validation, pandas duplicate checks, bash mount checks, AWS S3 command generation, cron expression explanation, FASTQ manifest generation, reproducible workflow checklist generation, and structured JSON extraction from synthetic workflow messages.
Evaluation
Final eval loss: 8.545802302251104e-06
Final eval perplexity: 1.0000085458388177
Average keyword coverage: 0.945
JSON validity rate: 1.0
These metrics are early portfolio metrics, not a claim of production quality. Generated code should be manually reviewed.
Example Prompt
Write a Python script that validates a sample manifest CSV. It should check for required columns sample_id, site_id, and file_path, then report missing values and duplicate sample IDs.
Limitations
This adapter may hallucinate commands, produce code with bugs, or omit safety checks. All generated code should be reviewed before use.
