HealthGPT
【ICML 2025 Spotlight】 Official Repo for Paper ‘’HealthGPT : A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation‘’
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최종 버전 다운로드 (.zip)- chatUI.jpg
- Framework.png
- HealthGPT.png
- intro.png
- __init__.py
- com_infer.py
- com_infer.sh
- com_infer_phi4.py
- com_infer_phi4.sh
- com_infer_qwen2_5.py
- com_infer_qwen2_5.sh
- gen_infer.py
- gen_infer.sh
- utils.py
- answer_alpaca-13b.jsonl
- answer_bard.jsonl
- answer_gpt35.jsonl
- answer_llama-13b.jsonl
- answer_vicuna-13b.jsonl
- test_sqa_llava_13b_v0.json
- test_sqa_llava_lcs_558k_sqa_12e_vicuna_v1_3_13b.json
- review_alpaca-13b_vicuna-13b.jsonl
- review_bard_vicuna-13b.jsonl
- review_gpt35_vicuna-13b.jsonl
- review_llama-13b_vicuna-13b.jsonl
- caps_boxes_coco2014_val_80.jsonl
- model.jsonl
- prompt.jsonl
- question.jsonl
- reviewer.jsonl
- rule.json
- alpaca.png
- bard.jpg
- chatgpt.svg
- llama.jpg
- swords_FILL0_wght300_GRAD0_opsz48.svg
- vicuna.jpeg
- index.html
- script.js
- styles.css
- eval_gpt_review.py
- eval_gpt_review_bench.py
- eval_gpt_review_visual.py
- eval_pope.py
- eval_science_qa.py
- eval_science_qa_gpt4.py
- eval_science_qa_gpt4_requery.py
- eval_textvqa.py
- generate_webpage_data_from_table.py
- m4c_evaluator.py
- model_qa.py
- model_vqa.py
- model_vqa_loader.py
- model_vqa_mmbench.py
- model_vqa_science.py
- qa_baseline_gpt35.py
- run_llava.py
- summarize_gpt_review.py
- llava_llama.py
- llava_mistral.py
- llava_mpt.py
- llava_phi3.py
- llava_qwen.py
- builder.py
- clip_encoder.py
- builder.py
- __init__.py
- apply_delta.py
- builder.py
- consolidate.py
- llava_arch.py
- make_delta.py
- utils.py
- __init__.py
- lora.py
- p_tuning.py
- prefix_tuning.py
- prompt_tuning.py
- __init__.py
- adapters_utils.py
- config.py
- other.py
- save_and_load.py
- __init__.py
- mapping.py
- peft_model.py
- extreme_ironing.jpg
- waterview.jpg
- __init__.py
- cli.py
- controller.py
- gradio_web_server.py
- model_worker.py
- register_worker.py
- sglang_worker.py
- test_message.py
- __init__.py
- constants.py
- conversation.py
- mm_utils.py
- utils.py
- gqa.sh
- llavabench.sh
- mmbench.sh
- mmbench_cn.sh
- mme.sh
- mmvet.sh
- pope.sh
- qbench.sh
- qbench_zh.sh
- seed.sh
- sqa.sh
- textvqa.sh
- vizwiz.sh
- vqav2.sh
- finetune.sh
- finetune_lora.sh
- finetune_task.sh
- finetune_task_lora.sh
- pretrain.sh
- convert_gqa_for_eval.py
- convert_mmbench_for_submission.py
- convert_mmvet_for_eval.py
- convert_seed_for_submission.py
- convert_sqa_to_llava.py
- convert_sqa_to_llava_base_prompt.py
- convert_vizwiz_for_submission.py
- convert_vqav2_for_submission.py
- extract_mm_projector.py
- finetune.sh
- finetune_full_schedule.sh
- finetune_lora.sh
- finetune_qlora.sh
- finetune_sqa.sh
- merge_lora_weights.py
- pretrain.sh
- pretrain_xformers.sh
- sqa_eval_batch.sh
- sqa_eval_gather.sh
- upload_pypi.sh
- zero2.json
- zero3.json
- zero3_offload.json
- model.yaml
- extract_depth.py
- extract_segmentation.py
- extract_submodel.py
- make_samples.py
- make_scene_samples.py
- sample_conditional.py
- sample_fast.py
- objects_bbox.py
- objects_center_points.py
- utils.py
- ade20k.py
- annotated_objects_coco.py
- annotated_objects_dataset.py
- annotated_objects_open_images.py
- base.py
- coco.py
- custom.py
- faceshq.py
- helper_types.py
- image_transforms.py
- imagenet.py
- open_images_helper.py
- sflckr.py
- utils.py
- cond_transformer.py
- dummy_cond_stage.py
- vqgan.py
- model.py
- model.py
- __init__.py
- lpips.py
- segmentation.py
- vqperceptual.py
- coord.py
- mingpt.py
- permuter.py
- quantize.py
- util.py
- lr_scheduler.py
- util.py
- environment.yaml
- idx2img.py
- License.txt
- main.py
- setup.py
- .gitignore
- app.py
- config.py
- dockerignore.txt
- editorconfig.txt
- gitattributes.txt
- gitignore.txt
- LICENSE
- model.py
- README.md
- requirements.txt
- requirements_qwen_2_5.txt
- __init__.py
- infer_3d_volume.py
- infer_multi_image.py
- infer_single_image.py
- infer_text.py
- utils.py
- LICENSE
- README.md
- requirements.txt
- .gitignore
- LICENSE
- README.md
# 설치 가이드
1. 코드 내려받기
git clone https://github.com/ZJU4HealthCare/HealthGPT
깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.
cd HealthGPT
방금 내려받은 프로젝트 폴더 안으로 이동합니다.
2. Python
쉬움 추천사전 준비물
pip install -r HealthGPT-Pro/requirements.txt
requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.
python <실행할 파일명>.py # README에서 정확한 실행 파일명을 확인하세요
파이썬 스크립트(또는 모듈)를 실행합니다.
에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.
// repository documentation
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