Instructions to use microsoft/MediPhi-MedCode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use microsoft/MediPhi-MedCode with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("microsoft/MediPhi-MedCode")
model = AutoModelForCausalLM.from_pretrained("microsoft/MediPhi-MedCode")
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 microsoft/MediPhi-MedCode with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/microsoft/MediPhi-MedCode

SGLang

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

Docker Model Runner
How to use microsoft/MediPhi-MedCode with Docker Model Runner:
```
docker model run hf.co/microsoft/MediPhi-MedCode
```

jpcorb20 commited on Dec 15, 2025

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@@ -193,14 +193,34 @@ This project may contain trademarks or logos for projects, products, or services
 ## Citation
-    @article{corbeil2025modular,
-        title={A Modular Approach for Clinical SLMs Driven by Synthetic Data with Pre-Instruction Tuning, Model Merging, and Clinical-Tasks Alignment},
-        author={Corbeil, Jean-Philippe and Dada, Amin and Attendu, Jean-Michel and Abacha, Asma Ben and Sordoni, Alessandro and Caccia, Lucas and Beaulieu, Fran{\c{c}}ois and Lin, Thomas and Kleesiek, Jens and Vozila, Paul},
-        journal={arXiv preprint arXiv:2505.10717},
-        year={2025}
     }
 ## Model Card Authors
 Jean-Philippe Corbeil

 ## Citation
+    @inproceedings{corbeil-etal-2025-modular,
+        title = "A Modular Approach for Clinical {SLM}s Driven by Synthetic Data with Pre-Instruction Tuning, Model Merging, and Clinical-Tasks Alignment",
+        author = "Corbeil, Jean-Philippe  and
+          Dada, Amin  and
+          Attendu, Jean-Michel  and
+          Ben Abacha, Asma  and
+          Sordoni, Alessandro  and
+          Caccia, Lucas  and
+          Beaulieu, Francois  and
+          Lin, Thomas  and
+          Kleesiek, Jens  and
+          Vozila, Paul",
+        editor = "Che, Wanxiang  and
+          Nabende, Joyce  and
+          Shutova, Ekaterina  and
+          Pilehvar, Mohammad Taher",
+        booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+        month = jul,
+        year = "2025",
+        address = "Vienna, Austria",
+        publisher = "Association for Computational Linguistics",
+        url = "https://aclanthology.org/2025.acl-long.950/",
+        doi = "10.18653/v1/2025.acl-long.950",
+        pages = "19352--19374",
+        ISBN = "979-8-89176-251-0",
+        abstract = "High computation costs and latency of large language models such as GPT-4 have limited their deployment in clinical settings. Small language models (SLMs) offer a cost-effective alternative, but their limited capacity requires biomedical domain adaptation, which remains challenging. An additional bottleneck is the unavailability and high sensitivity of clinical data. To address these challenges, we propose a novel framework for adapting SLMs into high-performing clinical models. We introduce the MediPhi collection of 3.8B-parameter SLMs developed with our novel framework: pre-instruction tuning of experts on relevant medical and clinical corpora (PMC, Medical Guideline, MedWiki, etc.), model merging, and clinical-tasks alignment. To cover most clinical tasks, we extended the CLUE benchmark to CLUE+, doubling its size. Our expert models deliver relative improvements on this benchmark over the base model without any task-specific fine-tuning: 64.3{\%} on medical entities, 49.5{\%} on radiology reports, and 44{\%} on ICD-10 coding (outperforming GPT-4-0125 by 14{\%}). We unify the expert models into MediPhi via model merging, preserving gains across benchmarks. Furthermore, we built the MediFlow collection, a synthetic dataset of 2.5 million high-quality instructions on 14 medical NLP tasks, 98 fine-grained document types, and JSON format support. Alignment of MediPhi using supervised fine-tuning and direct preference optimization achieves further gains of 18.9{\%} on average."
     }
 ## Model Card Authors
 Jean-Philippe Corbeil