Instructions to use Programmerlb/medgemma1.5-4b-pneumonia-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Programmerlb/medgemma1.5-4b-pneumonia-lora with Transformers:

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

pipe = pipeline("text-generation", model="Programmerlb/medgemma1.5-4b-pneumonia-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 AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Programmerlb/medgemma1.5-4b-pneumonia-lora")
model = AutoModelForImageTextToText.from_pretrained("Programmerlb/medgemma1.5-4b-pneumonia-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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Programmerlb/medgemma1.5-4b-pneumonia-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Programmerlb/medgemma1.5-4b-pneumonia-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": "Programmerlb/medgemma1.5-4b-pneumonia-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Programmerlb/medgemma1.5-4b-pneumonia-lora

SGLang

How to use Programmerlb/medgemma1.5-4b-pneumonia-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 "Programmerlb/medgemma1.5-4b-pneumonia-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": "Programmerlb/medgemma1.5-4b-pneumonia-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Programmerlb/medgemma1.5-4b-pneumonia-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": "Programmerlb/medgemma1.5-4b-pneumonia-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Programmerlb/medgemma1.5-4b-pneumonia-lora with Docker Model Runner:
```
docker model run hf.co/Programmerlb/medgemma1.5-4b-pneumonia-lora
```

MedGemma 1.5 4B – Pneumonia (Merged)

This repository contains a merged fine-tuned MedGemma model focused on pneumonia-related clinical guidance.

⚠️ Disclaimer
Educational and research use only. Not for real medical diagnosis or treatment.

Base model

google/medgemma-1.5-4b-it

Quickstart (Transformers)

!pip install -U transformers accelerate sentencepiece safetensors

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

repo_id = "Programmerlb/medgemma1.5-4b-pneumonia-lora"

tokenizer = AutoTokenizer.from_pretrained(repo_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    device_map="auto",
    dtype=torch.float16,
)

prompt = "I have fever and productive cough for 5 days with shortness of breath. What should I do?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Downloads last month: 23

Safetensors

Model size

4B params

Tensor type

F16

Model tree for Programmerlb/medgemma1.5-4b-pneumonia-lora

Base model

google/medgemma-1.5-4b-it

Finetuned

(62)

this model