Med vLLM (Config-first Repository)
This repository serves as a config-first landing for the Med vLLM stack.
It contains example configuration files and is intended to help users discover
and consume the MedicalModelConfig from the Hub via from_pretrained, and to
use these as starting points for training or inference in medical NLP tasks.
Contents
- NER config example (
examples/ner/) - Classification config example (
examples/classification/) - Generation config example (
examples/generation/)
Install
pip install medvllm
Quickstart (Python)
from medvllm.medical.config.models.medical_config import MedicalModelConfig
cfg = MedicalModelConfig.from_pretrained("Junaidi-AI/med-vllm")
print(cfg.task_type)
Or directly load a specific example folder if exported as a subfolder with its own config files.
Examples
- NER:
examples/ner/config.json|examples/ner/config.yaml - Classification:
examples/classification/config.json|examples/classification/config.yaml - Generation:
examples/generation/config.json|examples/generation/config.yaml
Use these as starting points and customize fields like task_type, classification_labels, medical_entity_types, and domain settings.
Tasks supported
- Named Entity Recognition (NER)
- Text Classification
- Text Generation
All tasks share a unified configuration schema via MedicalModelConfig.
Weights roadmap
This repo currently focuses on configs. Model weights/adapters will be added progressively:
- BioBERT/ClinicalBERT adapters
- Task-specific fine-tuned checkpoints (NER/Classification)
Follow the repo for updates or open a Discussion to request specific checkpoints.
Debug and logging
By default, verbose config debug prints are silenced. To enable them for troubleshooting, set:
export MEDVLLM_CONFIG_DEBUG=1
Medical Disclaimer
This repository and associated configurations are provided for research and engineering purposes only. They are not intended for clinical decision-making. Always involve qualified healthcare professionals and ensure compliance with applicable regulations (e.g., HIPAA, GDPR). Avoid using PHI/PII.
License
MIT
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