Instructions to use OpenMedZoo/MedGo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenMedZoo/MedGo with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenMedZoo/MedGo")
model = AutoModelForCausalLM.from_pretrained("OpenMedZoo/MedGo")
messages = [
    {"role": "user", "content": "Who are you?"},
]
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OpenMedZoo/MedGo with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/OpenMedZoo/MedGo

SGLang

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

Docker Model Runner
How to use OpenMedZoo/MedGo with Docker Model Runner:
```
docker model run hf.co/OpenMedZoo/MedGo
```

Model's actual performance falls far short of claims

by kityra - opened Dec 22, 2025

Discussion

kityra

Dec 22, 2025

After testing, the performance was very poor. Even requesting JSON output could cause the model to get stuck in loops. The gap between this and your advertised claims is huge. Was the actual model not released

https://mp.weixin.qq.com/s?__biz=MjM5NzQ5MzE5Mg==&mid=2651674219&idx=1&sn=18d6ec0586111f6c344aae5f7d2fea70&chksm=bc35872cecbb6bcd14df7e7b4b6374168f3ce112b927eb535c4753f7d54cceb08d65d0c411fa&scene=27

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