Instructions to use surfiniaburger/medgemma-4b-dipg-safety-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use surfiniaburger/medgemma-4b-dipg-safety-v2 with Transformers:

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

pipe = pipeline("text-generation", model="surfiniaburger/medgemma-4b-dipg-safety-v2")

# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("surfiniaburger/medgemma-4b-dipg-safety-v2")
model = AutoModelForCausalLM.from_pretrained("surfiniaburger/medgemma-4b-dipg-safety-v2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use surfiniaburger/medgemma-4b-dipg-safety-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "surfiniaburger/medgemma-4b-dipg-safety-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "surfiniaburger/medgemma-4b-dipg-safety-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/surfiniaburger/medgemma-4b-dipg-safety-v2

SGLang

How to use surfiniaburger/medgemma-4b-dipg-safety-v2 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 "surfiniaburger/medgemma-4b-dipg-safety-v2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "surfiniaburger/medgemma-4b-dipg-safety-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "surfiniaburger/medgemma-4b-dipg-safety-v2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "surfiniaburger/medgemma-4b-dipg-safety-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use surfiniaburger/medgemma-4b-dipg-safety-v2 with Docker Model Runner:
```
docker model run hf.co/surfiniaburger/medgemma-4b-dipg-safety-v2
```

medgemma-4b-dipg-safety-v2 / config.json

surfiniaburger

Upload GRPO-trained MedGemma 4B (DIPG Safety v2)

e4b2e72 verified 5 months ago

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	{
	"architectures": [
	"Gemma3ForCausalLM"
	],
	"model_type": "gemma3",
	"torch_dtype": "bfloat16",
	"vocab_size": 262208,
	"hidden_size": 2560,
	"intermediate_size": 10240,
	"num_hidden_layers": 34,
	"num_attention_heads": 8,
	"num_key_value_heads": 4,
	"head_dim": 256,
	"hidden_activation": "gelu_pytorch_tanh",
	"max_position_embeddings": 8192,
	"rms_norm_eps": 1e-06,
	"tie_word_embeddings": true,
	"transformers_version": "4.49.0"
	}