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medgemma-oncology-lora
Phiphi216/medgemma-oncology-lora
medgemma-oncology-lora is a image-text-to-text model from Phiphi216. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
This repository contains a LoRA fine-tuned oncology agent built on top of MedGemma 1.5 4B IT.
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Public
Updated Jun 3, 2026
Repo size
384 MB
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At a glance
- Task
- Image-Text-to-Text
- Library
- transformers
- License
- apache-2.0
- Access
- Public
- Created
- Jun 3, 2026
- Updated
- Jun 3, 2026
- SHA
- 946adde0
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Base models
Datasets
Model card
From the Hugging Face model README
MedGemma Oncology Agent
This repository contains a LoRA fine-tuned oncology agent built on top of MedGemma 1.5 4B IT.
The model is trained to analyze chest CT scan slices together with structured clinical records and predict six-year lung cancer risk.
The model was fine-tuned using Unsloth and PEFT LoRA on a custom oncology dataset.
Model Details
| Item | Value |
|---|---|
| Base Model | unsloth/medgemma-1.5-4b-it |
| Domain | Oncology |
| Task | Lung Cancer Risk Prediction |
| Finetuning Method | LoRA |
| Developer | Phiphi216 |
| License | Apache 2.0 |
Dataset
Dataset:
- Phiphi216/oncology
Number of samples:
- 2,000 training examples
Each sample contains:
- CT scan identifier
- Patient identifier
- Prompt
- Response
Response format:
{
"lung_cancer_risk": "Cancer within follow-up"
}
Training Procedure
Base Model
unsloth/medgemma-1.5-4b-it
LoRA Configuration
r: 16
lora_alpha: 16
lora_dropout: 0.0
finetune_vision_layers: false
finetune_language_layers: true
finetune_attention_modules: true
finetune_mlp_modules: true
optimizer: adamw_torch_fused
learning_rate: 2e-4
weight_decay: 1e-3
lr_scheduler_type: cosine
max_grad_norm: 0.3
per_device_train_batch_size: 2
gradient_accumulation_steps: 2
effective_batch_size: 4
max_seq_length: 25808
max_steps: 125
warmup_ratio: 0
- Task
- Image-Text-to-Text
- Library
- transformers
- License
- apache-2.0
- Languages
- en
- Created
- Jun 3, 2026
- Updated
- Jun 3, 2026