55 lines
1.6 KiB
Python
55 lines
1.6 KiB
Python
from transformers import LlamaTokenizer, AutoModelForCausalLM, AutoConfig, GenerationConfig
|
|
import torch
|
|
|
|
|
|
TEMPLATE = (
|
|
"<s>Human: "
|
|
"{instruction} \n</s><s>Assistant: "
|
|
)
|
|
|
|
def generate_prompt(instruction):
|
|
return TEMPLATE.format_map({'instruction': instruction})
|
|
|
|
ckpt_path = './LLM/TechGPT2-Atom-hf/'
|
|
load_type = torch.float16
|
|
device = torch.device(1)
|
|
tokenizer = LlamaTokenizer.from_pretrained(ckpt_path)
|
|
tokenizer.pad_token_id = 2
|
|
tokenizer.bos_token_id = 1
|
|
tokenizer.eos_token_id = 2
|
|
tokenizer.padding_side = "left"
|
|
model_config = AutoConfig.from_pretrained(ckpt_path)
|
|
model = AutoModelForCausalLM.from_pretrained(ckpt_path, torch_dtype=load_type, config=model_config)
|
|
model.to(device)
|
|
model.eval()
|
|
|
|
generation_config = GenerationConfig(
|
|
temperature=0.3,
|
|
top_p=0.95,
|
|
top_k=50,
|
|
num_beams=1,
|
|
bos_token_id=1,
|
|
eos_token_id=2,
|
|
pad_token_id=2,
|
|
max_new_tokens=1024,
|
|
min_new_tokens=10,
|
|
do_sample=True,
|
|
)
|
|
|
|
example = '出血因凝血因子大量被消耗、血小板减少及继发纤溶亢进，发生出血。\n请列示这段文本中的所有特定实体。'
|
|
|
|
instruction = generate_prompt(instruction=example)
|
|
|
|
instruction = tokenizer(instruction, return_tensors="pt")
|
|
input_ids = instruction["input_ids"].to(device)
|
|
with torch.no_grad():
|
|
generation_output = model.generate(
|
|
input_ids=input_ids,
|
|
generation_config=generation_config,
|
|
return_dict_in_generate=True,
|
|
output_scores=True,
|
|
repetition_penalty=1.2,
|
|
)
|
|
output = generation_output.sequences[0]
|
|
output = tokenizer.decode(output, skip_special_tokens=True)
|
|
print(output) |