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docs: link HF repo in README; chore: format via pre-commit; config: gate debug prints behind MEDVLLM_CONFIG_DEBUG
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‎.hf_export/med-vllm-config/README.md‎

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

‎.hf_export/med-vllm-config/examples/classification/config.json‎

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{
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"_serializing": true,
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"activation_recompute": false,
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"adapter_config": null,
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"adapter_type": null,
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"allow_tf32": false,
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"anatomical_regions": [
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"head"
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],
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"attention_impl": null,
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"batch_size": 16,
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"cache_ttl": 3600,
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"classification_labels": [
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"diagnosis",
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"treatment",
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"follow-up"
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],
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"config_version": "1.0.0",
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"cudnn_benchmark": null,
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"document_types": [
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"clinical_note",
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"discharge_summary",
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"radiology_report"
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],
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"domain_adaptation": false,
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"domain_adaptation_lambda": 0.1,
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"domain_specific_vocab": null,
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"emit_trace": false,
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"enable_flash_attention": null,
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"enable_memory_pooling": false,
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"enable_mixed_precision": false,
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"enable_profiling": false,
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"enable_uncertainty_estimation": false,
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"enforce_eager": false,
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"entity_linking": {
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"confidence_threshold": 0.8,
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"enabled": false,
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"knowledge_bases": [
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"umls",
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"snomed_ct",
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"loinc"
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]
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},
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"eos": -1,
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"file_path": null,
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"flash_attention_config": null,
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"gpu_memory_utilization": 0.9,
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"grad_checkpointing": false,
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"hf_config": "namespace(model_type='unknown', max_position_embeddings=4096)",
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"imaging_modalities": [
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"xray"
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],
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"kvcache_block_size": 256,
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"max_entity_span_length": 10,
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"max_medical_seq_length": 4096,
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"max_model_len": 4096,
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"max_num_batched_tokens": 32768,
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"max_num_seqs": 512,
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"max_retries": 3,
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"medical_entity_types": [
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"disease"
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],
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"medical_specialties": [
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"cardiology"
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],
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"memory_efficient": true,
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"mixed_precision_dtype": "fp16",
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"model": "emilyalsentzer/Bio_ClinicalBERT",
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"model_type": "medical_llm",
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"ner_confidence_threshold": 0.5,
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"num_kvcache_blocks": -1,
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"pool_device": "auto",
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"pool_max_bytes": null,
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"pretrained_model_name_or_path": "emilyalsentzer/Bio_ClinicalBERT",
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"profiler_device": "auto",
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"quantization_bits": null,
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"quantization_method": null,
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"recompute_granularity": null,
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"regulatory_compliance": [
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"hipaa",
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"gdpr"
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],
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"request_timeout": 30,
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"section_headers": [
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"history_of_present_illness",
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"past_medical_history",
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"medications",
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"allergies",
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"family_history",
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"social_history",
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"review_of_systems",
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"physical_exam",
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"assessment_and_plan"
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],
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"task_type": "classification",
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"tensor_parallel_size": 1,
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"torch_matmul_precision": null,
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"trace_dir": "profiles",
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"uncertainty_threshold": 0.7,
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"use_cuda_graphs": false,
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"use_medical_adapter": true,
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"version": "0.1.0"
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}

‎.hf_export/med-vllm-config/examples/classification/config.yaml‎

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_serializing: true
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activation_recompute: false
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adapter_config: null
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adapter_type: null
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allow_tf32: false
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anatomical_regions:
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- head
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attention_impl: null
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batch_size: 16
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cache_ttl: 3600
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classification_labels:
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- diagnosis
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- treatment
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- follow-up
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config_version: 1.0.0
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cudnn_benchmark: null
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document_types:
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- clinical_note
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- discharge_summary
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- radiology_report
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domain_adaptation: false
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domain_adaptation_lambda: 0.1
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domain_specific_vocab: null
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emit_trace: false
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enable_flash_attention: null
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enable_memory_pooling: false
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enable_mixed_precision: false
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enable_profiling: false
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enable_uncertainty_estimation: false
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enforce_eager: false
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entity_linking:
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confidence_threshold: 0.8
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enabled: false
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knowledge_bases:
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- umls
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- snomed_ct
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- loinc
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eos: -1
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file_path: null
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flash_attention_config: null
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gpu_memory_utilization: 0.9
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grad_checkpointing: false
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hf_config: namespace(model_type='unknown', max_position_embeddings=4096)
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imaging_modalities:
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- xray
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kvcache_block_size: 256
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max_entity_span_length: 10
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max_medical_seq_length: 4096
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max_model_len: 4096
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max_num_batched_tokens: 32768
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max_num_seqs: 512
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max_retries: 3
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medical_entity_types:
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- disease
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medical_specialties:
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- cardiology
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memory_efficient: true
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mixed_precision_dtype: fp16
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model: emilyalsentzer/Bio_ClinicalBERT
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model_type: medical_llm
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ner_confidence_threshold: 0.5
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num_kvcache_blocks: -1
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pool_device: auto
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pool_max_bytes: null
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pretrained_model_name_or_path: emilyalsentzer/Bio_ClinicalBERT
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profiler_device: auto
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quantization_bits: null
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quantization_method: null
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recompute_granularity: null
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regulatory_compliance:
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- hipaa
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- gdpr
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request_timeout: 30
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section_headers:
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- history_of_present_illness
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- past_medical_history
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- medications
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- allergies
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- family_history
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- social_history
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- review_of_systems
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- physical_exam
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- assessment_and_plan
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task_type: classification
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tensor_parallel_size: 1
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torch_matmul_precision: null
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trace_dir: profiles
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uncertainty_threshold: 0.7
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use_cuda_graphs: false
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use_medical_adapter: true
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version: 0.1.0

‎.hf_export/med-vllm-config/examples/generation/config.json‎

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{
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"_serializing": true,
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"activation_recompute": false,
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"adapter_config": null,
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"adapter_type": null,
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"allow_tf32": false,
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"anatomical_regions": [
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"head"
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],
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"attention_impl": null,
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"batch_size": 1,
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"cache_ttl": 3600,
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"classification_labels": [],
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"config_version": "1.0.0",
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"cudnn_benchmark": null,
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"document_types": [
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"clinical_note",
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"discharge_summary",
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"radiology_report"
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],
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"domain_adaptation": false,
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"domain_adaptation_lambda": 0.1,
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"domain_specific_vocab": null,
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"emit_trace": false,
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"enable_flash_attention": null,
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"enable_memory_pooling": false,
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"enable_mixed_precision": false,
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"enable_profiling": false,
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"enable_uncertainty_estimation": false,
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"enforce_eager": false,
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"entity_linking": {
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"confidence_threshold": 0.8,
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"enabled": false,
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"knowledge_bases": [
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"umls",
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"snomed_ct",
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"loinc"
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]
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},
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"eos": -1,
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"file_path": null,
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"flash_attention_config": null,
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"gpu_memory_utilization": 0.9,
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"grad_checkpointing": false,
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"hf_config": "namespace(model_type='unknown', max_position_embeddings=4096)",
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"imaging_modalities": [
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"xray"
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],
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"kvcache_block_size": 256,
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"max_entity_span_length": 10,
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"max_medical_seq_length": 1024,
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"max_model_len": 1024,
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"max_num_batched_tokens": 32768,
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"max_num_seqs": 512,
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"max_retries": 3,
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"medical_entity_types": [
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"disease"
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],
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"medical_specialties": [
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"cardiology"
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],
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"memory_efficient": true,
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"mixed_precision_dtype": "fp16",
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"model": "gpt2",
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"model_type": "medical_llm",
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"ner_confidence_threshold": 0.5,
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"num_kvcache_blocks": -1,
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"pool_device": "auto",
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"pool_max_bytes": null,
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"pretrained_model_name_or_path": "gpt2",
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"profiler_device": "auto",
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"quantization_bits": null,
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"quantization_method": null,
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"recompute_granularity": null,
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"regulatory_compliance": [
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"hipaa",
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"gdpr"
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],
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"request_timeout": 30,
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"section_headers": [
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"history_of_present_illness",
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"past_medical_history",
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"medications",
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"allergies",
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"family_history",
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"social_history",
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"review_of_systems",
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"physical_exam",
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"assessment_and_plan"
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],
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"task_type": "generation",
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"tensor_parallel_size": 1,
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"torch_matmul_precision": null,
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"trace_dir": "profiles",
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"uncertainty_threshold": 0.7,
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"use_cuda_graphs": false,
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"use_medical_adapter": true,
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"version": "0.1.0"
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}

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