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使用官方huggingface代码推理报错 #108
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没有用的,我的代码: ` tokenizer = LlamaTokenizer.from_pretrained(model_path, add_eos_token=False, add_bos_token=False, eos_token='', trust_remote_code=True) ` |
看到你应该是离线使用的hf模型,离线使用可以参考在模型目录执行下面的脚本 import torch, transformers
import sys, os
sys.path.append(
os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)))
from transformers import AutoModelForCausalLM,AutoTokenizer,LlamaTokenizer
from yuan_hf_model import YuanForCausalLM
torch.set_printoptions(precision=12)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
print("Creat tokenizer...")
tokenizer = LlamaTokenizer.from_pretrained('./', add_eos_token=False, add_bos_token=False)
tokenizer.add_tokens(['<sep>', '<pad>', '<mask>', '<predict>', '<FIM_SUFFIX>', '<FIM_PREFIX>', '<FIM_MIDDLE>','<commit_before>','<commit_msg>','<commit_after>','<jupyter_start>','<jupyter_text>','<jupyter_code>','<jupyter_output>','<empty_output>'], special_tokens=True)
print("Creat model...")
input_text = ['请问目前最先进的机器学习算法有哪些?<sep>']
inputs = tokenizer(input_text, return_tensors="pt")["input_ids"].to("cuda:0")
model = YuanForCausalLM.from_pretrained('./',device_map='auto',torch_dtype=torch.half,trust_remote_code=True)
outputs = model.generate(inputs,do_sample=False,max_length=100)
print(tokenizer.decode(outputs[0]))
# 请问目前最先进的机器学习算法有哪些?<sep> 目前最先进的机器学习算法包括以下几种:\n1. 卷积神经网络(Convolutional Neural Networks,CNN):CNN是近年来在图像处理领域中广泛应用的一种神经网络,包含卷积层、池化层和全连接层,训练时需要大量标注数据以及自我调整参数。\n2. 循环神经网络(Recurrent Neural Networks,RNN):RNN指的是信息流模型,能够处理序列数据, |
其实不必这么麻烦的,主要是你们文件夹命名的问题,我将模型文件夹名称由Yuan2.0-51B-hf改成Yuan2-51B-hf就解决了问题 |
报错
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