Medgemma-4B-NPU2

Instructions to use OpenFlowLM/Medgemma-4B-NPU2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenFlowLM/Medgemma-4B-NPU2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="OpenFlowLM/Medgemma-4B-NPU2")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("OpenFlowLM/Medgemma-4B-NPU2")
model = AutoModelForMultimodalLM.from_pretrained("OpenFlowLM/Medgemma-4B-NPU2", device_map="auto")
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 = tokenizer.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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OpenFlowLM/Medgemma-4B-NPU2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenFlowLM/Medgemma-4B-NPU2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenFlowLM/Medgemma-4B-NPU2",
		"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/OpenFlowLM/Medgemma-4B-NPU2

SGLang

How to use OpenFlowLM/Medgemma-4B-NPU2 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 "OpenFlowLM/Medgemma-4B-NPU2" \
    --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": "OpenFlowLM/Medgemma-4B-NPU2",
		"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 "OpenFlowLM/Medgemma-4B-NPU2" \
        --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": "OpenFlowLM/Medgemma-4B-NPU2",
		"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"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use OpenFlowLM/Medgemma-4B-NPU2 with Docker Model Runner:
```
docker model run hf.co/OpenFlowLM/Medgemma-4B-NPU2
```

Medgemma-4B-NPU2

OpenFlowLM Q4NX conversion of Atomic-Germ/Medgemma-4B-NPU2 for AMD XDNA NPU inference.

This repository contains a quantized Q4NX port of the model, compiled for the OpenFlowLM (OFLM) runtime. It is not a GGUF file.

Item	Value
Source model	`Atomic-Germ/Medgemma-4B-NPU2`
Source GGUF	`medgemma-4b-it.i1-Q4_1.gguf`
Weights	`model.q4nx` (3.51 GB)
Modality	language
OFLM version	`0.1.0`
Converted	2026-10-01

Reproduce this conversion

This container was produced by exactly this command, so a fine-tune of the same base can be packed the same way:

oflm pack -i /home/atomic-germ/.cache/huggingface/hub/models--mradermacher--medgemma-4b-it-i1-GGUF/snapshots/e72473b68139c8f77427dd21270eff3b2b80c1f9/medgemma-4b-it.i1-Q4_1.gguf -o /home/atomic-germ/Projects/OpenFlowLM-Next/Medgemma-4B-NPU2 -s Atomic-Germ/Medgemma-4B-NPU2

Install and run

This repository works with oflm-add, a small installer that copies the model into the OpenFlowLM user directory and registers the tag. It never modifies the system OpenFlowLM install.

pip install oflm-add or uv tool install oflm-add

uv tool install oflm-add
oflm-add Atomic-Germ/Medgemma-4B-NPU2 --family qwen3.5 --xclbin-from Medgemma-4B-NPU2
OFLM_CONFIG_PATH="$HOME/.config/oflm/model_list.json" OFLM_XCLBIN_PATH="$HOME/.config/oflm" oflm run Medgemma-4B-NPU2

Files

File	Description
`model.q4nx`	Quantized weights (Q8_0 / Q4_1 / BF16)
`config.json`	OFLM runtime configuration
`tokenizer.json`	Tokenizer vocabulary
`tokenizer_config.json`	Tokenizer configuration
`chat_template.jinja`	Chat template

Source model card

See the original model card: Atomic-Germ/Medgemma-4B-NPU2

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