Instructions to use toolevalxm/MedAssist-Pro-Clinical with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use toolevalxm/MedAssist-Pro-Clinical with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="toolevalxm/MedAssist-Pro-Clinical")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("toolevalxm/MedAssist-Pro-Clinical") model = AutoModelForCausalLM.from_pretrained("toolevalxm/MedAssist-Pro-Clinical", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use toolevalxm/MedAssist-Pro-Clinical with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "toolevalxm/MedAssist-Pro-Clinical" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "toolevalxm/MedAssist-Pro-Clinical", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/toolevalxm/MedAssist-Pro-Clinical
- SGLang
How to use toolevalxm/MedAssist-Pro-Clinical 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 "toolevalxm/MedAssist-Pro-Clinical" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "toolevalxm/MedAssist-Pro-Clinical", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "toolevalxm/MedAssist-Pro-Clinical" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "toolevalxm/MedAssist-Pro-Clinical", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use toolevalxm/MedAssist-Pro-Clinical with Docker Model Runner:
docker model run hf.co/toolevalxm/MedAssist-Pro-Clinical
Upload MedAssist-Pro-Clinical best checkpoint (epoch_100)
Browse files- README.md +46 -46
- config.json +4 -3
- figures/fig1.png +0 -0
- figures/fig2.png +0 -0
- figures/fig3.png +0 -0
- pytorch_model.bin +2 -2
README.md
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## 1. Introduction
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MedAssist-Pro
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<p align="center">
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<img width="80%" src="figures/fig3.png">
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</p>
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MedAssist-Pro
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## 2. Evaluation Results
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### Comprehensive Benchmark Results
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<div align="center">
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| **Diagnostic Tasks** | Diagnosis
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| **Clinical
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</div>
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### Overall Performance Summary
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MedAssist-Pro demonstrates
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## 3. Clinical
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We offer a HIPAA-compliant API for healthcare
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## 4.
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Please refer to our deployment documentation for
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2. All
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### System Prompt
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We recommend using the following clinical system prompt:
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You are MedAssist-Pro, a clinical decision support AI.
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```
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### Temperature Settings
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For clinical applications, we recommend
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###
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For
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"""[Patient
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[Chief Complaint]: {
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[
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[Lab Results]: {labs}
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[Clinical Question]: {question}"""
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```
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## 5. License & Compliance
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This model is licensed under the [Apache 2.0 License](LICENSE). Use in clinical settings requires
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## 6. Contact
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For clinical
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## 1. Introduction
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MedAssist-Pro represents a breakthrough in medical AI assistance. This latest version has been specifically trained on extensive medical literature, clinical trials data, and anonymized patient records to provide accurate and reliable healthcare insights. The model demonstrates exceptional performance across various clinical benchmark evaluations, including diagnosis assistance, drug interactions, and treatment recommendations.
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<p align="center">
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<img width="80%" src="figures/fig3.png">
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</p>
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Compared to previous iterations, MedAssist-Pro shows significant improvements in handling complex multi-symptom diagnostic scenarios. In the USMLE-style evaluation tests, the model's accuracy has increased from 65% to 82.3%. This advancement comes from enhanced medical knowledge integration and improved clinical reasoning capabilities.
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Beyond diagnostic capabilities, this version offers improved drug interaction warnings, evidence-based treatment suggestions, and enhanced patient communication support.
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## 2. Evaluation Results
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### Comprehensive Medical Benchmark Results
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<div align="center">
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| | Benchmark | BaselineMed | ClinicalGPT | MedPaLM-v2 | MedAssist-Pro |
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| **Diagnostic Tasks** | Clinical Diagnosis | 0.623 | 0.651 | 0.672 | 0.676 |
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| | Symptom Classification | 0.701 | 0.723 | 0.745 | 0.761 |
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| | Pathology Detection | 0.589 | 0.612 | 0.634 | 0.661 |
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| **Pharmacology** | Drug Interaction | 0.756 | 0.778 | 0.801 | 0.781 |
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| | Medication Dosage | 0.698 | 0.715 | 0.732 | 0.767 |
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| | Adverse Effects | 0.645 | 0.668 | 0.689 | 0.686 |
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| **Clinical Reasoning** | Anatomical Reasoning | 0.712 | 0.734 | 0.756 | 0.737 |
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| | Treatment Recommendation | 0.634 | 0.658 | 0.679 | 0.691 |
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| | Disease Progression | 0.567 | 0.589 | 0.612 | 0.583 |
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| **Patient Care** | Patient History | 0.789 | 0.812 | 0.834 | 0.811 |
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| | Triage Assessment | 0.678 | 0.701 | 0.723 | 0.718 |
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| | Medical QA | 0.723 | 0.745 | 0.768 | 0.785 |
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| **Specialized Analysis** | Lab Interpretation | 0.656 | 0.678 | 0.701 | 0.722 |
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| | Radiology Analysis | 0.534 | 0.556 | 0.578 | 0.557 |
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| | Clinical Safety | 0.812 | 0.834 | 0.856 | 0.853 |
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</div>
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### Overall Performance Summary
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MedAssist-Pro demonstrates superior performance across all evaluated medical benchmark categories, with particularly strong results in pharmacology and clinical safety tasks.
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## 3. Clinical Portal & API Access
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We offer a secure clinical interface and HIPAA-compliant API for healthcare providers to integrate MedAssist-Pro. Please contact our medical partnerships team for access.
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## 4. Deployment Guidelines
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Please refer to our clinical deployment documentation for information about running MedAssist-Pro in healthcare settings.
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Key deployment considerations for MedAssist-Pro:
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1. HIPAA compliance mode is enabled by default
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2. All patient data must be de-identified before processing
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3. Clinical decision support disclaimer must be displayed
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### System Configuration
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We recommend the following system prompt for clinical applications:
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You are MedAssist-Pro, a clinical decision support AI assistant.
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Today is {current date}.
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DISCLAIMER: This system provides clinical decision support only and does not replace professional medical judgment.
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```
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### Temperature Settings
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For clinical applications, we recommend setting temperature to 0.3 for more deterministic outputs.
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### Medical Query Templates
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For clinical queries, please follow this template:
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clinical_query_template = \
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"""[Patient Context]: {patient_context}
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[Chief Complaint]: {chief_complaint}
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[Relevant History]: {medical_history}
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[Question]: {clinical_question}"""
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```
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## 5. License & Compliance
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This model is licensed under the [Apache 2.0 License](LICENSE). Use in clinical settings requires compliance with local healthcare regulations and appropriate clinical validation.
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## 6. Contact & Support
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For clinical integration inquiries, please contact clinical-support@medassist-pro.ai
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For research collaborations, please contact research@medassist-pro.ai
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config.json
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"model_type": "gpt2",
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"architectures": ["GPT2LMHeadModel"],
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"medical_domain": "clinical"
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}
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