Instructions to use danushkv/medgemma-cholec-inst with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use danushkv/medgemma-cholec-inst with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("google/medgemma-1.5-4b-it")
model = PeftModel.from_pretrained(base_model, "danushkv/medgemma-cholec-inst")

Transformers

How to use danushkv/medgemma-cholec-inst with Transformers:

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

pipe = pipeline("text-generation", model="danushkv/medgemma-cholec-inst")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("danushkv/medgemma-cholec-inst", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use danushkv/medgemma-cholec-inst with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "danushkv/medgemma-cholec-inst"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "danushkv/medgemma-cholec-inst",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/danushkv/medgemma-cholec-inst

SGLang

How to use danushkv/medgemma-cholec-inst 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 "danushkv/medgemma-cholec-inst" \
    --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": "danushkv/medgemma-cholec-inst",
		"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 "danushkv/medgemma-cholec-inst" \
        --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": "danushkv/medgemma-cholec-inst",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use danushkv/medgemma-cholec-inst with Docker Model Runner:
```
docker model run hf.co/danushkv/medgemma-cholec-inst
```

medgemma-cholec-inst / preprocessor_config.json

danushkv

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	{
	"crop_size": null,
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	"device": null,
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	"do_convert_rgb": true,
	"do_normalize": true,
	"do_pad": null,
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	"do_rescale": true,
	"do_resize": true,
	"image_mean": [
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	"image_processor_type": "Gemma3ImageProcessorFast",
	"image_seq_length": 256,
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	"processor_class": "Gemma3Processor",
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	"return_tensors": null,
	"size": {
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	"width": 896
	}
	}