Instructions to use Phiphi216/medgemma-oncology-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Phiphi216/medgemma-oncology-lora with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="Phiphi216/medgemma-oncology-lora")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Phiphi216/medgemma-oncology-lora", device_map="auto")

PEFT
How to use Phiphi216/medgemma-oncology-lora with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Phiphi216/medgemma-oncology-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Phiphi216/medgemma-oncology-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Phiphi216/medgemma-oncology-lora",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Phiphi216/medgemma-oncology-lora

SGLang

How to use Phiphi216/medgemma-oncology-lora with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Phiphi216/medgemma-oncology-lora" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Phiphi216/medgemma-oncology-lora",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Phiphi216/medgemma-oncology-lora" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Phiphi216/medgemma-oncology-lora",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use Phiphi216/medgemma-oncology-lora with Docker Model Runner:
```
docker model run hf.co/Phiphi216/medgemma-oncology-lora
```

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

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for Phiphi216/medgemma-oncology-lora

Base model

google/medgemma-1.5-4b-it

Finetuned

unsloth/medgemma-1.5-4b-it

Adapter

(3)

this model

Phiphi216
/

medgemma-oncology-lora