• MyLLM:
• Fine-Tuned LLMs

My LLM Fine-Tuning and Copilot

My Free Personal LLM Copilot

1. Register to start using your free personal LLM copilot.
2. Use your copilot regularly to assist with your tasks in daily work and life.
3. Healthcare professionals can utilize the copilot to support every step of healthcare delivery.
4. Patients can use the copilot to help personalize their healthcare.

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Standard Fine-Tuned LLMs

1. Review the fine-tuned open-source disease prediction LLMs openly available on the platform.
2. Choose a fine-tuned LLM that fits your research and clinical needs.
3. For collaboration, contact us to access preclinical validation data for the fine-tuned LLM to support your research and publications.

Choose My Fine-Tuned LLM

Custom LLM Fine-Tuning

1. Contact us if you need a fine-tuned LLM for your specific requirements.
2. We will find-tune an open-source LLM with synthetic patient data and deploy it with a chatbot for testing under your control.
3. The LLM will be further fine-tuned with your patient data to achieve high accuracy for your patient population.

Contact Us for LLM Fine-Tuning

How to Fine-Tune My Open-Source LLMs?

We provide LLM Fine-Tuning Technology Services to help overcome potential bottlenecks in the clinical use of LLMs. Our innovative technology uses synthetic patient data to fine-tune Llama3.1-8B for any clinical tasks you may need. Additionally, we are developing standard fine-tuned Llama models for selected diseases, which are open for your research collaboration. These services empower you and clinical teams to evaluate the benefits of GenAI in clinical care, generate new evidence, and publish high-quality papers.

Our Collaboration Study Support for a Client Project Includes the Following Steps:

1. Fine-Tuning with Synthetic Patient Data:
   For the disease of interest to the clinical team, we assist in defining the prediction task, goals and measurable outcomes. Using synthetic patient data, we create an initial fine-tuned Llama3.1-8B model (i.e. "theoretical LLM") and provide preclinical validation data for your review.
2. Deploying the Theoretical LLM:
   We deploy the fine-tuned LLM along with a Gradio-powered chatbot on an AWS cloud server, allowing the clinical team to test it easily. The team retains full control over the data and LLM and can terminate the server as needed.
3. Validating with Retrospective Data:
   The clinical team validates the initial fine-tuned LLM using their retrospective patient data. We will guide the team in preparing datasets from electronic health records for training and validation.
4. Fine-Tuning with Real Patient Data:
   We further fine-tune the Llama3.1-8B model using real patient data and update the chatbot with the newly fine-tuned LLM.
5. Evaluating Clinical Impact:
   The clinical team validates the updated fine-tuned LLM with retrospective data. If successful, they evaluate its impact on prospective real-world data during routine clinical delivery. Comparing measurable outcomes before and after using LLM predictions can generate valuable evidence for the responsible use of GenAI in real-world clinical settings.
6. Publishing Papers:
   If needed, we can assist with analyzing study results, summarizing clinical evidence, and preparing initial manuscripts for submission to top journals.

How to Create and Train My Own LLM Copilot?

1. Start: Register on the platform to begin using your free personal LLM copilot in the cloud and assess its benefits. Select different LLMs within the copilot to compare performance. Healthcare professionals can use the copilot to assist with every step of healthcare delivery, while patients can use it for personalized care.
2. Deploy: When you need a private open-source LLM in your controlled environment, contact us. Our technical team will compare various open-source LLMs for you and deploy your chosen LLM with a chatbot in the cloud or on-premises.
3. Fine-Tune: Use your copilot daily and monitor its performance. We can apply our pioneering ML-enabled learning health system (ML-LHS) unit approach to fine-tune your LLM using your local patient data through continuous learning cycles. This process improves accuracy and gradually trains your LLM chatbot into a trusted, capable GenAI copilot.