FairLLaVA — Pretrained Checkpoints

Fairness-aware LoRA adapters for medical vision–language models, from the FairLLaVA paper.

• Code: github.com/bhosalems/FairLLaVA
• Paper: arxiv.org/abs/2603.26008

FairLLaVA minimizes the mutual information between the model's visual features and patient demographic attributes (age, sex, race), producing demographic-invariant representations while preserving clinical accuracy. The adapters here plug into a standard LoRA fine-tuning loop and are released on three medical benchmarks.

Checkpoints

Subdir	Dataset	Base LLM	Vision Tower	Task
mimic-cxr/	MIMIC-CXR	lmsys/vicuna-7b-v1.5	BiomedCLIP-CXR-518	Chest X-ray report generation
padchest/	PadChest	lmsys/vicuna-7b-v1.5	BiomedCLIP-CXR-518	Chest X-ray report generation
ham10000/	HAM10000	liuhaotian/llava-v1.5-7b	CLIP ViT-L/14-336	Dermoscopy VQA

Each subdirectory contains the LoRA adapter (adapter_model.safetensors, adapter_config.json, non_lora_trainables.bin), the matching multimodal projector (mm_projector.bin), and the tokenizer files, so the path can be loaded as model_path directly by llava.model.builder.load_pretrained_model.

Quick start

from huggingface_hub import snapshot_download
from llava.model.builder import load_pretrained_model

# Download just one dataset's checkpoint
local_dir = snapshot_download(
    repo_id="mbhosale/FairLLaVA",
    allow_patterns="mimic-cxr/*",
)
model_path = f"{local_dir}/mimic-cxr"

tokenizer, model, image_processor, ctx_len = load_pretrained_model(
    model_path,
    model_base="lmsys/vicuna-7b-v1.5",
    model_name="llavarad",
)

See the full inference example in inference.py.

Ethics

These checkpoints are released for research and educational use only. They are not approved or validated for clinical or diagnostic use and must not be used to make medical decisions or to inform patient care. Each downstream dataset is governed by its own data-use agreement (PhysioNet for MIMIC-CXR, BIMCV for PadChest, ISIC / Harvard Dataverse for HAM10000).

Citation

@article{bhosale2026fairllava,
  title={FairLLaVA: Fairness-Aware Parameter-Efficient Fine-Tuning for Large Vision-Language Assistants},
  author={Bhosale, Mahesh and Wasi, Abdul and Srivastava, Shantam and Latif, Shifa and Luan, Tianyu and Gao, Mingchen and Doermann, David and Gong, Xuan},
  journal={arXiv preprint arXiv:2603.26008},
  year={2026}
}