Bio-LLM Evaluation Suite

Fine-tuning + safety benchmarking for clinical & genomic LLMs

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/projects/biollmeval

Bio-LLM Evaluation Suite

Python research harness for benchmarking open models on curated clinical and genomic corpora — PubMedQA baseline pipeline, LoRA-capable fine-tuning via HuggingFace PEFT, GPU-aware dry-run fallback, heuristic toxicity/hallucination/privacy metrics, PHI scanning for dataset onboarding, encrypted audit trail design, and CI across Python 3.9–3.11.

Project context05

Problem

Clinical and genomic LLM work cannot be evaluated only with generic benchmark scores. Teams need task-specific datasets, safety checks, PHI screening, hallucination heuristics, and repeatable fine-tuning paths before models can be trusted in biomedical settings.

Solution

Bio-LLM Evaluation Suite provides a Python harness for PubMedQA-style evaluation, LoRA-capable fine-tuning, GPU-aware dry runs, safety heuristics, PHI scanning, encrypted audit concepts, and CI coverage.

Challenges

The challenge is balancing research velocity with safety constraints: data onboarding needs privacy checks, training needs deterministic configuration, and evaluation needs to distinguish performance from biomedical risk.

Innovation

The suite treats biomedical model evaluation as an operational pipeline with model training, safety testing, privacy scanning, and auditability in one developer workflow.

Domain expertise

This reinforces Stefan's expertise in biomedical LLM evaluation, HuggingFace/PEFT training loops, Pydantic validation, PHI-aware tooling, safety heuristics, and CI-backed research infrastructure.

Case study evidence11

Outcomes

Gives biomedical AI teams a repeatable path for testing accuracy, privacy, hallucination risk, and fine-tuning behavior.
Separates benchmark performance from clinical safety signals so model readiness is not reduced to one score.
Creates an evaluation harness that can be used locally, in CI, and in research iteration loops.

Architecture decisions

Pydantic configs make dataset, training, LoRA, and evaluation settings explicit before a run begins.
PHI scanning and safety heuristics live beside training and evaluation rather than after deployment.
Dry-run paths let teams validate the pipeline even when GPU resources are unavailable.

Domain expertise signals

Biomedical evalsPHI screeningLoRA fine-tuningSafety heuristicsResearch CI

Technical deep dive09

Bio-LLM Evaluation Suite is not just an accuracy benchmark. It is a biomedical model-readiness harness that connects task evaluation, privacy screening, safety heuristics, fine-tuning configuration, and CI discipline.

Beyond accuracy

Biomedical systems need to track hallucination markers, toxic content, privacy leakage, and task correctness together. A model that scores well but leaks PHI or fabricates clinical claims is not ready.

Data governance

Dataset onboarding includes PHI scanning and explicit configuration because biomedical data cannot be treated like generic text. The harness makes data risk part of the developer workflow.

Training repeatability

LoRA and dry-run support help teams test the shape of training without assuming every machine has a GPU. Typed configs keep model, dataset, split, and training parameters reviewable.

CI posture

The suite is built to run quality checks continuously. That matters because evaluation harnesses become trustworthy only when regressions are caught before research conclusions depend on them.

What this proves

Pydantic run and training contracts
PHI scanner included in the model workflow
Safety metrics tracked beside task scores
Dry-run path for GPU-unavailable environments

3Python versions in CI

1baseline config

3safety heuristics

1PHI scanner

0GPU required (dry-run)

LoRAoptional fine-tune

Technology stack02

Python

CLI-first harness for dataset loading, training, evaluation, and compliance tooling.

Pyd

Pydantic v2

RunConfig, TrainingConfig, and LoRAConfig validated at startup — no silent misconfiguration.

Tools implemented06

bio_llm_eval CLI

Config → train → evaluate → JSON report; optional changelog and lab-report append.

PubMedQA loader

JSONL corpus load with train/val/test split for baseline benchmarking.

LoRA training loop

HuggingFace Transformers + PEFT + Accelerate; auto dry-run without GPU.

Safety heuristics

Toxicity, hallucination marker, and privacy-leak rates beyond accuracy alone.

PHI scanner

Pattern detection utility for dataset onboarding compliance checklist.

GitHub Actions CI

ruff, black, pytest, and dry-run pipeline across Python 3.9–3.11.