As an example, I trained a model to predict imbd ratings with an example from the HuggingFace resources, shown below. I’ve tried a number of ways (save_model, save_pretrained) and either am struggling to save it at all or when loaded, can’t figure out what to call to get prediction...
我们可以看到,上面这种方式下载的模型权重路径会比较长,我们可以通过调用save_pretrained方法将模型保存到另一个路径。 import torch from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("internlm/internlm2-chat-7b", trust_remote_code=True) # Set `torch_...
from datasets import load_dataset , Dataset,list_datasets # 设置一下下载的临时路径,要不然每次下载在C盘,空间受不了 datasets = load_dataset("code_search_net", "python", cache_dir='D:\\temp\\huggingface\\chen\\datasets') # 下面是读取数据的过程 # huggingface推荐建立一个迭代器函数,迭代器的好处...
I have defined my model via huggingface, but I don't know how to save and load the model, hopefully someone can help me out, thanks! CeyaoZhang and mertyyanik reacted with thumbs up emojiCeyaoZhang reacted with eyes emoji 👍
I've tried save model via: ppo_trainer.save_pretrained("./model_after_rl") and load the model via: model = AutoModelForCausalLMWithValueHead.from_pretrained("./model_after_rl") ref_model = AutoModelForCausalLMWithValueHead.from_pretraine...
I tried to save the model withpipe.save_pretrained("./local_model_directory")and then load the model in the second run with `pipe("object-detection", model="./local_model_directory"). This throws an error and doesn't work at all. ...
2.6 Save the Model trainer.save_model(script_args.output_dir)三、详解SFTTrainer trl/trainer/...
model_checkpoint = "distilbert-base-uncased" # use_fast: Whether or not to try to load the fast version of the tokenizer. # Most of the tokenizers are available in two flavors: a full python # implementation and a “Fast” implementation based on the Rust library Tokenizers. ...
模型导出时将生成`config.json`和`pytorch_model.bin`参数文件。前者就是1中的配置文件,这和我们的直觉相同,即config和model应该是紧密联系在一起的两个类。后者其实和torch.save()存储得到的文件是相同的,这是因为Model都直接或者间接继承了Pytorch的Module类。从这里可以看出,HuggingFace在实现时很好地尊重了Pytorch...
我们在Kaggle的kernel上运行# 选择一个大模型,当然需要足够多的硬盘空间,并且要耐心等待checkpoint="facebook/opt-6.7b"generator=pipeline("text-generation",model=checkpoint,device_map="auto",torch_dtype=torch.float16)# 使用pipeline进行推理generator("More and more large language models are opensourced ...