tokenizer.pad_token = tokenizer.eos_token #为了防止生成的文本出现[PAD],这里将[PAD]重置为[EOS] input_ids = tokenizer(['Human: 介绍一下中国\nAssistant: '], return_tensors="pt", add_special_tokens=False).input_ids.to('cuda') #将输入的文本转换为token generate_input = { "input_ids": ...
tokenizer = AutoTokenizer.from_pretrained(base_model_name_or_path,use_fast=False) tokenizer.pad_token = tokenizer.eos_token model = AutoModelForCausalLM.from_pretrained(base_model_name_or_path,device_map='auto',torch_dtype=torch.float16,load_in_8bit=True) model = PeftModel.from_pretrained(m...
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) tokenizer.pad_token = tokenizer.eos_token tokenizer.padding_side = "right" 下面是参数定义, # Activate 4-bit precision base model loading use_4bit = True # Compute dtype for 4-bit base models bnb_4bit_compute_dtype...
在第36行,调用add_special_tokens({' pad_token ': ' [PAD] '})这是另一个重要代码,因为我们数据集中的文本长度可以变化,批处理中的序列可能具有不同的长度。为了确保批处理中的所有序列具有相同的长度,需要将填充令牌添加到较短的序列中。这些填充标记通常是没有任何含义的标记,例如。 在第37行,我们设置toke...
tokenizer.pad_token = tokenizer.eos_token tokenizer.padding_side ="right" 下面是参数定义, # Activate 4-bit precision base model loadinguse_4bit=True# Compute dtype for 4-bit base modelsbnb_4bit_compute_dtype="float16"# Quantization type (fp4 or nf4)bnb_4bit_quant_type="nf4"# Activate ...
注意Llama 2 和 Mistral 7B 没有默认的pad_token_id,我们将其设为eos_token_id。 Mistral 7B: # 加载 Mistral 7B 分词器 fromtransformersimportAutoTokenizer, DataCollatorWithPadding mistral_tokenizer = AutoTokenizer.from_pretrained(mistral_checkpoint, add_prefix_space=True) ...
tokenizer.pad_token = tokenizer.eos_token tokenizer.padding_side = "right" 下面是参数定义, # Activate 4-bit precision base model loading use_4bit = True # Compute dtype for 4-bit base models bnb_4bit_compute_dtype = "float16" # Quantization type (fp4 or nf4) ...
tokenizer.pad_token = tokenizer.eos_token tokenizer.padding_side = "right" 下面是参数定义, # Activate 4-bit precision base model loading use_4bit = True # Compute dtype for 4-bit base models bnb_4bit_compute_dtype = "float16" # Quantization type (fp4 or nf4) ...
tokenizer=LlamaTokenizer.from_pretrained(args.checkpoint)tokenizer.add_special_tokens({'pad_token':'<PAD>'})model=LlamaForCausalLM.from_pretrained(args.checkpoint)model.to(torch.bfloat16)model.train()# Prepare dataset train_dataset=AlpacaDataset(tokenizer=tokenizer,data_path=args.data_root)train_data...
model.config.pretraining_tp = 1 peft_config = LoraConfig( lora_alpha=16, lora_dropout=0.1, r=64, bias="none", task_type="CAUSAL_LM",)tokenizer = AutoTokenizer.from_pretrained(base_model_name, trust_remote_code=True)tokenizer.pad_token = tokenizer.eos_tokenoutput_dir =...