def write_tokenizer(tokenizer_path, input_tokenizer_path): # Initialize the tokenizer based on the `spm` model tokenizer_class = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast print(f"Saving a {tokenizer_class.__name__} to {tokenizer_path}.") tokenizer = tokenizer_class(...
ChatGLM-6B 是平衡中英文分词效果最好的 tokenizer。由于词表比较大,中文处理时间也有增加。 BLOOM 虽然是词表最大的,但由于是多语种的,在中英文上分词效率与 ChatGLM-6B 基本相当。需要注意的是,在实验统计时,BLOOM 的 tokenizer 用了 transformers 的 BloomTokenizerFast 实现,分词速度更快。 从两个例子上,来...
3 old_tokenizer = AutoTokenizer.from_pretrained(PATH_TO_LLAMA_DIR,) ---> 5 old_tokenizer.train_new_from_iterator(["I love huggingface!"], 50) File ~/transformers/src/transformers/tokenization_utils_fast.py:709, in PreTrainedTokenizerFast.train_new_from_iterator(self, text_iterator, vocab_si...
# Please copy-and-paste your command here. When I'm going to Marge tokenizer, I get this error 'AttributeError: 'LlamaTokenizerFast' object has no attribute 'sp_model'' llama_spm = sp_pb2_model.ModelProto() llama_spm.ParseFromString(llama_tokenizer.sp_model.serialized_model_proto()) De...
LlamaTokenizerFast(name_or_path='NousResearch/Llama-2-7b-hf', vocab_size=32000, model_max_length=1000000000000000019884624838656, is_fast=True, padding_side='left', truncation_side='right', special_tokens={'bos_token': '', 'eos_token': '', 'unk_token': '<unk>', 'pad_token': '<...
所以可以先直接使用LlamaTokenizer class来实现，或者用AutoTokenizer class，将use_fast赋为False。二是用于EasyLM框架的EasyLM格式。在此请注意，与原始LLaMA不同，该OpenLLaMA的分词器和权重是完全从头开始训练的，因此不再需要获取原始 LLaMA的这俩信息。接下来，在训练量已达成一致的情况下，看OpenLLaMA各规模模型的...
transformers import AutoTokenizerargs.output_dir = "llama-7-int4-dolly"# 加载基础LLM模型与分词器model = AutoPeftModelForCausalLM.from_pretrained( args.output_dir, low_cpu_mem_usage=True, torch_dtype=torch.float16, load_in_4bit=True,) tokenizer = AutoTokenizer.from_pretrained...
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=False) model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", torch_dtype=torch.bfloat16).cuda() # 启动FastAPI应用 #用6006端口可以将autodl的端口映射到本地,从而在本地使用api ...
BloomForCausalLM, BloomTokenizerFast, AutoModelForCausalLM, LlamaTokenizer, LlamaFo...
{}"""FastLanguageModel.for_inference(model) inputs = tokenizer( [ alpaca_prompt.format("请用中文回答",# instruction"太阳还有五十亿年就没了,那到时候向日葵看哪呢?",# input"",# output) ], return_tensors ="pt").to("cuda")fromtransformersimportTextStreamer ...