Medical-LLM-14B

**Medical-LLM-14B**

The 14B parameter model represents the pinnacle of our medical language modeling capabilities, offering unparalleled depth in medical knowledge processing and clinical reasoning.

Building on that foundation, it introduces a dedicated reasoning mode that can follow multi-step clinical logic and justify its answers. Trained on an expanded, carefully curated corpus of medical literature and reinforced with chain-of-thought supervision, it excels at differential diagnosis, guideline-aware care planning and complex patient-note summarization.

This powerful model also handles the most complex medical cases, rare conditions and sophisticated clinical analyses, demonstrating exceptional accuracy in interpreting complicated medical literature, producing detailed clinical summaries and providing comprehensive responses to intricate medical queries. While requiring more computational resources, it delivers superior performance in critical medical tasks where accuracy and depth of understanding are paramount. Its advanced RAG optimization enables sophisticated integration with extensive medical databases and research repositories.

Choose this model for specialized medical institutions, research facilities, and scenarios where premium performance in complex medical tasks justifies the additional computational investment.

**Benchmarks and other characteristics:**
- Achieves 81.42% average score vs GPT-4s 82.85% and Med-PaLM-2s 84.08%
- Clinical knowledge score of 92.36% vs Med-PaLM-2s 88.3%
- Medical reasoning at 90% matches Med-PaLM-2s performance
- Higher accuracy than Meditron-70B while using 5x less parameters
- Suitable for deployment scenarios with compute constraints

**Performance metrics:**

**Medical-LLM-14B** model was evaluated using a chat-based completion workflow across two representative subtasks: **Question Answering** and **Summarization**. Both benchmarks were executed against the same dataset of 100 documents, processed in five invocations with 20 documents per request, ensuring consistent workload characteristics across tests.

The experiments were conducted under a **MULTIGPU_MEDIUM** configuration using four NVIDIA A10 GPUs with a combined memory capacity of 96 GB, and with the model configured for long-context inference (maximum context length of 16,384 tokens).

Under identical infrastructure and model settings:
- **Question Answering workload** achieved an average of 220 tokens per second
- **Summarization workload** achieved an average of 60 tokens per second

See this table for approximate [memory calculations required](https://nlp.johnsnowlabs.com/docs/en/LLMs/medical_llm#medical-llms-offering) to use this model.

**Additional Model Information**

- [John Snow Labs New Suite of Medical Language Models Advance Industry Benchmarks](https://www.johnsnowlabs.com/john-snow-labs-new-suite-of-medical-language-models-advance-industry-benchmarks/)
- [Measuring the Benefits of Healthcare Specific Large Language Models](https://www.nlpsummit.org/measuring-the-benefits-of-healthcare-specific-large-language-models/)

**How to run this model**:

1. Acquire a John Snow Labs Pay As You Go (PAYG) license from [Sales]([e-mail])
2. Import this listing.
3. See the attached notebook to deploy and use the model.

**Vendor support**
For any assistance, please reach out to [e-mail]

This model comes with optimized CPU and GPU builds. You can select which one to deploy via the notebook.