TrialPanorama/LLaMA-3-8B-TP
TrialPanorama/LLaMA-3-8B-TP is a 3.2 billion parameter language model fine-tuned from Meta-Llama-3-8B-Instruct. Developed by TrialPanorama, it specializes in clinical trial applications, particularly for sample size estimation. The model utilizes a two-stage fine-tuning process, including Supervised Fine-Tuning (SFT) and Reinforcement Learning with Verifiable Reward (RLVR), to enhance its domain-specific knowledge and performance.
Loading preview...
Overview
TrialPanorama/LLaMA-3-8B-TP is a specialized language model, fine-tuned from the Meta-Llama-3-8B-Instruct base model, designed for applications within clinical research. It leverages a 3.2 billion parameter architecture and a 32768 token context length to process and generate relevant information.
Key Capabilities
- Clinical Trial Specialization: The model is specifically trained on the TrialPanorama dataset, which comprises one million clinical trials, making it highly proficient in this domain.
- Sample Size Estimation: A primary application is estimating required sample sizes for clinical trials, providing both the estimate and reasoning.
- Two-Stage Fine-tuning: Its development involved a unique two-stage process:
- Supervised Fine-Tuning (SFT): For injecting domain-specific knowledge.
- Reinforcement Learning with Verifiable Reward (RLVR): To further refine its outputs and ensure verifiability.
Good For
- Researchers and professionals in clinical trials needing assistance with sample size estimation.
- Applications requiring domain-specific knowledge in clinical research.
- Integration into systems that benefit from a specialized LLM for medical and pharmaceutical contexts.