Image-Text-to-Text
PEFT
Safetensors
Vietnamese
medical
radiology
chest-xray
lora
vision-language
vietnamese
medgemma
conversational
Instructions to use nvtkienn/medgemma-4b_finetuned_175 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nvtkienn/medgemma-4b_finetuned_175 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/medgemma-4b-it") model = PeftModel.from_pretrained(base_model, "nvtkienn/medgemma-4b_finetuned_175") - Notebooks
- Google Colab
- Kaggle
medgemma-4b_finetuned_175 (LoRA)
LoRA adapter cho google/medgemma-4b-it, fine-tune sinh Impression (kết luận X-quang tiếng Việt) — dữ liệu nội bộ BV175 (đa bộ phận).
- Task: Impression Generation (Stage 2).
- Loss: cross-entropy (mask token assistant), Optim: AdamW, bf16.
- LoRA: r=16, alpha=32, dropout=0.05, target = proj LLM (đóng băng vision SigLIP).
- Data: 1210 train / 156 val / 157 test (split theo bệnh nhân).
Kết quả (test 157, impression)
| ROUGE-1 | ROUGE-2 | ROUGE-L | BLEU | |
|---|---|---|---|---|
| zero-shot | 26.8 | 9.4 | 17.8 | 0.9 |
| fine-tuned | 55.9 | 34.8 | 45.5 | 18.3 |
Dùng
from transformers import AutoProcessor, AutoModelForImageTextToText
from peft import PeftModel
base = "google/medgemma-4b-it" # gated: cần chấp nhận license + đăng nhập HF
proc = AutoProcessor.from_pretrained(base)
model = AutoModelForImageTextToText.from_pretrained(base, torch_dtype="bfloat16", device_map="cuda")
model = PeftModel.from_pretrained(model, "<user>/medgemma-4b_finetuned_175")
Base model MedGemma là gated (phải xin quyền trên HF).
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