# Line 368ifcache_dirisNone:cache_dir=TRANSFORMERS_CACHEifisinstance(cache_dir,Path):cache_dir=str(cache_dir) 这个变量在hub.py开头被导入: # line 93# Determine default cache directory. Lots of legacy environment variables to ensure backward compatibility.# The best way to set the cache path is...
from_pretrained("internlm/internlm2-chat-7b", trust_remote_code=True, cache_dir='/home/{username}/huggingface') # Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and cause OOM Error. model = AutoModelForCausalLM.from_pretrained("intern...
steps=2, # set to 1000 for full training save_steps=64, # set to 500 for full training eval_steps=64, # set to 8000 for full training warmup_steps=1, # set to 2000 for full training max_steps=128, # delete for full training overwrite_output_dir=True, sav...
predict_with_generate=True, logging_steps=2, # set to 1000 for full training save_steps=64, # set to 500 for full training eval_steps=64, # set to 8000 for full training warmup_steps=1, # set to 2000 for full training max_steps=128, # delete for full training overwrite_output_di...
logging_steps=2,# set to 1000 for full trainingsave_steps=64,# set to 500 for full trainingeval_steps=64,# set to 8000 for full trainingwarmup_steps=1,# set to 2000 for full trainingmax_steps=128,# delete for full trainingoverwrite_output_dir=True, ...
设置变量名为 HF_HOME 或 TRANSFORMERS_CACHE,变量值为你希望存储模型的路径(例如,D:\huggingface_cache)。 或者在命令行中设置 set HF_HOME=D:\huggingface_cache Linux/MacOS 编辑shell 配置文件(如 .bashrc, .zshrc) export HF_HOME=/path/to/your/desired/cache/dir ...
set_seed(42) model = MBartForConditionalGeneration.from_pretrained(model_checkpoint, cache_dir="/data0/xp/gec/model") model.resize_token_embeddings(len(tokenizer)) configuration = model.config # Data collator data_collator = DataCollatorForSeq2Seq( ...
importdatasetsdataset=datasets.load_dataset("stas/wmt16-en-ro-pre-processed",cache_dir="./wmt16-en_ro") 1. 2. 3. 在上图1中可以看到数据集内容。我们需要将其“压平”,这样可以更好的访问数据,让后将其保存到硬盘中。 复制 defflatten(batch):batch['en']=batch['translation']['en']batch['...
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch.") # Set seed before initializing model. set_seed(training_args.seed) # 加载任务相关处理的processor if data_args.task_name in processor_map: processor = processor_map[data_args.task_name](data_args, training_arg...
predict_with_generate=True, logging_steps=2, # set to 1000 for full training save_steps=64, # set to 500 for full training eval_steps=64, # set to 8000 for full training warmup_steps=1, # set to 2000 for full training max_steps=128, # delete for full training overwrite_output_di...