This repository hosts LoRA adapters for the checker component used in the MedRAGChecker project. The checker is trained as an NLI-style verifier to classify a (evidence, claim) pair into:

Entail
Neutral
Contradict

These adapters are intended for research and evaluation (e.g., ensembling multiple checkers trained with different base models and/or training recipes such as SFT vs. GRPO).

This repo contains adapters only. You must load each adapter on top of its corresponding base model.

Each adapter subfolder typically includes:

adapter_config.json
adapter_model.safetensors (or .bin)

Available adapters

All adapters live under the Checker/ directory:

Adapter subfolder	Base model (HF id)	Training recipe
`Checker/med42-llama3-8b-sft`	`<PUT_BASE_MODEL_ID_HERE>`	SFT
`Checker/med42-llama3-8b-grpo`	`<PUT_BASE_MODEL_ID_HERE>`	GRPO
`Checker/meditron-sft`	`<PUT_BASE_MODEL_ID_HERE>`	SFT
`Checker/meditron-grpo`	`<PUT_BASE_MODEL_ID_HERE>`	GRPO
`Checker/PMC_LLaMA_13B-sft`	`<PUT_BASE_MODEL_ID_HERE>`	SFT
`Checker/qwen2-med-7b-sft`	`<PUT_BASE_MODEL_ID_HERE>`	SFT
`Checker/qwen2-med-7b-grpo`	`<PUT_BASE_MODEL_ID_HERE>`	GRPO

How to fill the “Base model (HF id)” column

Use a valid Hugging Face Hub model id (format: org/name). Examples:

meta-llama/Meta-Llama-3-8B-Instruct
Qwen/Qwen2-7B-Instruct

If your base model is not available on the Hub (only stored locally), you can either:

upload the base model to a private Hub repo and reference that id here, or
keep this field as N/A (local) and document your local loading instructions.

Quickstart: load an adapter with PEFT

python

from transformers import AutoModelForSequenceClassification, AutoTokenizer
from peft import PeftModel

# 1) Choose the base model that matches the adapter you want to use
base_model_id = "<HF_BASE_MODEL_ID>"

# 2) Choose the adapter subfolder inside this repo
repo_id = "JoyDaJun/Medragchecker-Student-Checker"
subfolder = "Checker/qwen2-med-7b-sft"  # example

tokenizer = AutoTokenizer.from_pretrained(base_model_id, use_fast=True)
model = AutoModelForSequenceClassification.from_pretrained(base_model_id)
model = PeftModel.from_pretrained(model, repo_id, subfolder=subfolder)

If your checker was trained using a Causal LM head instead of a sequence classification head, replace AutoModelForSequenceClassification with AutoModelForCausalLM and use the same prompt/template as in training.

Ensemble usage (optional)

If you trained multiple student checkers, you can ensemble them (e.g., by weighting each checker’s class probabilities using dev-set reliability such as per-class F1). This often helps stabilize performance across Entail / Neutral / Contradict, especially under class imbalance.

Limitations & responsible use

Not medical advice. Do not use for clinical decision-making.
Outputs may reflect biases or errors from training data and teacher supervision.
Please evaluate on your target dataset and report limitations clearly.

Citation

If you use these adapters, please cite your MedRAGChecker paper/project:

bibtex

@article{medragchecker,
  title={MedRAGChecker: A Claim-Level Verification Framework for Biomedical RAG},
  author={...},
  year={2025}
}

JoyDaJun/Medragchecker-Student-Checker

MedRAGChecker Student Checker — LoRA Adapters

Contents