Instructions to use majdabd33/trialmatchai-gemma2-reranker-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use majdabd33/trialmatchai-gemma2-reranker-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-2-2b") model = PeftModel.from_pretrained(base_model, "majdabd33/trialmatchai-gemma2-reranker-lora") - Notebooks
- Google Colab
- Kaggle
TrialMatchAI 路 gemma-2-2b criterion reranker adapter (LoRA)
A LoRA adapter for the google/gemma-2-2b family, fine-tuned for the second-level criterion reranking stage of TrialMatchAI, an end-to-end system that matches patients to the clinical trials they are eligible for.
After first-level retrieval surfaces candidate trials, this adapter scores each (patient, trial criterion) pair so the most relevant trials are reranked to the top and shortlisted for the downstream eligibility-reasoning stage.
Usage
TrialMatchAI loads this adapter automatically. In config.json it sits on top of the instruct base:
"reranker_model_path": "google/gemma-2-2b-it",
"reranker_adapter_path": "majdabd33/trialmatchai-gemma2-reranker-lora"
It downloads on first use, so no separate model download step is needed. See the TrialMatchAI repository for the full pipeline and configuration.
Base model and license
This is an adapter of google/gemma-2-2b, governed by the
Gemma license; accept it on the base model page before use. For
research and informational use only. It is not medical advice, not a medical device, and must not
replace review by qualified healthcare professionals.
Citation
Abdallah, M. et al. TrialMatchAI: an end-to-end AI-powered clinical trial recommendation system to streamline patient-to-trial matching. Nature Communications 17, 4472 (2026). https://doi.org/10.1038/s41467-026-70509-w
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Base model
google/gemma-2-2b