LLaMA-3-8B-TP
This model is fine-tuned from Meta-Llama-3-8B-Instruct by using TrialPanorama dataset for clinical trials.
Model Details
- Base Model: Meta-Llama-3-8B-Instruct
- Fine-tuning Method: Two-stage training
- Stage 1: Supervised Fine-Tuning (SFT) for knowledge injection
- Stage 2: RLVR (Reinforcement Learning with Verifiable Reward)
Usage
Basic Usage with Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load model and tokenizer
model_name = "TrialPanorama/LLaMA-3-8B-TP"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
device_map="auto"
)
# Prepare input (a toy example)
prompt = """Given the following clinical trial information, estimate the required sample size:
[Input Information]
Please provide the estimated sample size and reasoning."""
# Generate response
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=512,
temperature=0.6,
top_p=0.95,
do_sample=True
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
Usage with vLLM (Recommended for Production)
from vllm import LLM, SamplingParams
# Initialize vLLM
llm = LLM(
model="TrialPanorama/LLaMA-3-8B-TP",
tensor_parallel_size=1,
dtype="bfloat16"
)
# Set sampling parameters
sampling_params = SamplingParams(
temperature=0.6,
top_p=0.95,
max_tokens=512
)
# Generate
prompts = ["Your sample size estimation prompt here"]
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
print(output.outputs[0].text)
Citation
If you use this model in your research, please cite:
@article{wang2025trialpanorama,
title = {Developing Large Language Models for Clinical Research Using One Million Clinical Trials},
author = {Wang, Zifeng and Lin, Jiacheng and Jin, Qiao and Gao, Junyi and Pradeepkumar, Jathurshan and Jiang, Pengcheng and Lu, Zhiyong and Sun, Jimeng},
journal = {arXiv preprint arXiv:2505.16097},
year = {2025},
url = {https://arxiv.org/abs/2505.16097}
}