Instructions to use TachyHealthResearch/medgemma-27b-medical-coding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TachyHealthResearch/medgemma-27b-medical-coding with Transformers:

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

pipe = pipeline("text-generation", model="TachyHealthResearch/medgemma-27b-medical-coding")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("TachyHealthResearch/medgemma-27b-medical-coding", dtype="auto")

PEFT
How to use TachyHealthResearch/medgemma-27b-medical-coding with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use TachyHealthResearch/medgemma-27b-medical-coding with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TachyHealthResearch/medgemma-27b-medical-coding"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TachyHealthResearch/medgemma-27b-medical-coding",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TachyHealthResearch/medgemma-27b-medical-coding

SGLang

How to use TachyHealthResearch/medgemma-27b-medical-coding 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 "TachyHealthResearch/medgemma-27b-medical-coding" \
    --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": "TachyHealthResearch/medgemma-27b-medical-coding",
		"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 "TachyHealthResearch/medgemma-27b-medical-coding" \
        --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": "TachyHealthResearch/medgemma-27b-medical-coding",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TachyHealthResearch/medgemma-27b-medical-coding with Docker Model Runner:
```
docker model run hf.co/TachyHealthResearch/medgemma-27b-medical-coding
```

medgemma-27b-medical-coding

Model Description

This is a LoRA adapter fine-tuned on medgemma27B for medical coding tasks. The model is specifically designed to:

Extract diseases and medical conditions from discharge summaries
Identify medical procedures and interventions
Assign appropriate medical codes (ICD-10, CPT, HCPCS)
Process clinical documentation with high accuracy

Base Model: medgemma27B
Fine-tuning Method: LoRA (Low-Rank Adaptation)

Training Details

LoRA Configuration

Rank (r): 8
Alpha: 16
Dropout: 0.1
Target Modules: q_proj, up_proj, v_proj, gate_proj

Usage

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("TachyHealthResearch/medgemma-27b-medical-coding")

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "medgemma27B",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "TachyHealthResearch/medgemma-27b-medical-coding")

Example Usage

# Define the system prompt for medical coding
system_prompt = """You are an expert medical coding specialist. 
Analyze the discharge summary to extract diseases, procedures, and assign appropriate medical codes. 
Return the response in JSON format with this structure:
{"diseases": ["disease1", "disease2"], "icd10_codes": ["code1", "code2"], 
"procedures": ["procedure1", "procedure2"], "cpt_codes": ["code1", "code2"], 
"hcpcs_codes": ["code1", "code2"]}"""

# Example discharge summary
discharge_summary = """
Patient admitted with chest pain and shortness of breath. 
Diagnosed with acute myocardial infarction and congestive heart failure.
Underwent percutaneous coronary intervention with stent placement.
"""

# Prepare the conversation
messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": f"Please analyze this discharge summary:\n\n{discharge_summary}"}
]

# Apply chat template and generate
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        temperature=0.1,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id,
    )

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
generated_response = response[len(text):].strip()
print("Generated Medical Codes:")
print(generated_response)

Model Performance

This model has been specifically fine-tuned for medical coding tasks and demonstrates strong performance in:

Disease extraction from clinical text
Medical procedure identification
Medical code assignment (ICD-10, CPT, HCPCS)
Structured JSON response generation

Intended Use

Primary Use Cases

Medical coding automation
Clinical documentation analysis
Healthcare data processing

Limitations

Always verify generated codes with qualified medical coding professionals
Performance may vary on clinical documents significantly different from training data
Intended for use in appropriate healthcare environments only

License

This model is released under the Apache 2.0 License.

Citation

If you use this model in your research or applications, please cite:

@misc{TachyHealthResearch_medgemma_27b_medical_coding_2024},
  title = {medgemma-27b-medical-coding: Medical Coding Model},
  author = {TachyHealthResearch},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/TachyHealthResearch/medgemma-27b-medical-coding}
}

Important: This model is intended for research and healthcare applications. Always ensure proper validation and human oversight when using AI models in medical contexts.

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