metadata
license: apache-2.0
Introduction
Aquila is a large language model trained by BAAI, and AquilaMed-RL is an industry model from Aquila language model. Based on the Aquila general pre-trained model, we continued pre-training , SFT and RL in the medical domain and obtained our AquilaMed-RL model.
Model Details
The pipeline of the training procedure is bellow, for more details you can read our technical report: https://github.com/FlagAI-Open/industry-application/blob/main/Aquila_med_tech-report.pdf
Evaluation
usage
when you have downloaded the model, you can use the bellow code to run the model
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
model_dir = "xxx"
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
config = AutoConfig.from_pretrained(model_dir, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_dir, config=config, trust_remote_code=True
)
model.cuda()
model.eval()
template = "<|im_start|>system\nYou are a helpful assistant in medical domain.<|im_end|>\n<|im_start|>user\n{question}<|im_end|>\n<|im_start|>assistant\n"
text = "我肚子疼怎么办？"
item_instruction = template.format(question=text)
inputs = tokenizer(item_instruction, return_tensors="pt").to("cuda")
input_ids = inputs["input_ids"]
prompt_length = len(input_ids[0])
generate_output = model.generate(
input_ids=input_ids, do_sample=False, max_length=1024, return_dict_in_generate=True
)
response_ids = generate_output.sequences[0][prompt_length:]
predicts = tokenizer.decode(
response_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
)
print("predict:", predicts)
"""
predict: 肚子疼可能是多种原因引起的，例如消化不良、胃炎、胃溃疡、胆囊炎、胰腺炎、肠道感染等。如果疼痛持续或加重，或者伴随有呕吐、腹泻、发热等症状，建议尽快就医。如果疼痛轻微，可以尝试以下方法缓解：
1. 饮食调整：避免油腻、辛辣、刺激性食物，多喝水，多吃易消化的食物，如米粥、面条、饼干等。
2. 休息：避免剧烈运动，保持充足的睡眠。
3. 热敷：用热水袋或毛巾敷在肚子上，可以缓解疼痛。
4. 药物：可以尝试一些非处方药，如布洛芬、阿司匹林等，但请务必在医生的指导下使用。
如果疼痛持续或加重，或者伴随有其他症状，建议尽快就医。
希望我的回答对您有所帮助。如果您还有其他问题，欢迎随时向我提问。
"""
Citation
If you find our work helpful, feel free to give us a cite.
@article{AquilaMed,
title={AquilaMed Technical Report},
year={2024}
}