metadata
license: apache-2.0
ProLLaMA: A Protein Large Language Model for Multi-Task Protein Language Processing
Paper on arxiv for more information
Github for more information
ProLLaMA_Stage_1 is based on Llama-2-7b, so please follow the license of Llama2.
Input format:
Seq=<
#You can also specify the first few amino acids of the protein sequence:
Seq=<MAPGGMPRE
Quick usage:
# you can replace the model_path with your local path
CUDA_VISIBLE_DEVICES=0 python main.py --model "GreatCaptainNemo/ProLLaMA_Stage_1" --interactive
# main.py is as follows 👇:
import argparse
import json, os
import torch
from transformers import LlamaForCausalLM, LlamaTokenizer
from transformers import GenerationConfig
from tqdm import tqdm
generation_config = GenerationConfig(
temperature=0.2,
top_k=40,
top_p=0.9,
do_sample=True,
num_beams=1,
repetition_penalty=1.2,
max_new_tokens=400
)
parser = argparse.ArgumentParser()
parser.add_argument('--model', default=None, type=str,help="The local path of the model. If None, the model will be downloaded from HuggingFace")
parser.add_argument('--interactive', action='store_true',help="If True, you can input instructions interactively. If False, the input instructions should be in the input_file.")
parser.add_argument('--input_file', default=None, help="You can put all your input instructions in this file (one instruction per line).")
parser.add_argument('--output_file', default=None, help="All the outputs will be saved in this file.")
args = parser.parse_args()
if __name__ == '__main__':
if args.interactive and args.input_file:
raise ValueError("interactive is True, but input_file is not None.")
if (not args.interactive) and (args.input_file is None):
raise ValueError("interactive is False, but input_file is None.")
if args.input_file and (args.output_file is None):
raise ValueError("input_file is not None, but output_file is None.")
load_type = torch.bfloat16
if torch.cuda.is_available():
device = torch.device(0)
else:
raise ValueError("No GPU available.")
model = LlamaForCausalLM.from_pretrained(
args.model,
torch_dtype=load_type,
low_cpu_mem_usage=True,
device_map='auto',
quantization_config=None
)
tokenizer = LlamaTokenizer.from_pretrained(args.model)
model.eval()
with torch.no_grad():
if args.interactive:
while True:
raw_input_text = input("Input:")
if len(raw_input_text.strip())==0:
break
input_text = raw_input_text
input_text = tokenizer(input_text,return_tensors="pt")
generation_output = model.generate(
input_ids = input_text["input_ids"].to(device),
attention_mask = input_text['attention_mask'].to(device),
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
generation_config = generation_config,
output_attentions=False
)
s = generation_output[0]
output = tokenizer.decode(s,skip_special_tokens=True)
print("Output:",output)
print("\n")
else:
outputs=[]
with open(args.input_file, 'r') as f:
examples =f.read().splitlines()
print("Start generating...")
for index, example in tqdm(enumerate(examples),total=len(examples)):
input_text = tokenizer(example,return_tensors="pt") #add_special_tokens=False ?
generation_output = model.generate(
input_ids = input_text["input_ids"].to(device),
attention_mask = input_text['attention_mask'].to(device),
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
generation_config = generation_config
)
s = generation_output[0]
output = tokenizer.decode(s,skip_special_tokens=True)
outputs.append(output)
with open(args.output_file,'w') as f:
f.write("\n".join(outputs))
print("All the outputs have been saved in",args.output_file)
Citation:
@article{lv2024prollama,
title={ProLLaMA: A Protein Large Language Model for Multi-Task Protein Language Processing},
author={Lv, Liuzhenghao and Lin, Zongying and Li, Hao and Liu, Yuyang and Cui, Jiaxi and Chen, Calvin Yu-Chian and Yuan, Li and Tian, Yonghong},
journal={arXiv preprint arXiv:2402.16445},
year={2024}
}