max_gen_len (Optional[int], optional): Maximum length of the generated completion sequence. If not provided, it's set to the model's maximum sequence length minus 1. logprobs (bool, optional): Flag indicating whether to compute token log probabilities. Defaults to False. echo (bool, ...
通过更改 max_length 可以指定希望生成响应的长度。将 num_return_sequences 参数设置为大于 1,可以生成多个输出。在脚本中添加以下内容,以提供输入以及如何运行 pipeline 任务的信息: sequences = pipeline ('I have tomatoes, basil and cheese at home. What can I cook for dinner?\n',do_sample=True,top_...
Max Length 512 512 512 Trainable Parameters (%) 2.97% 6.06% 6.22% Training Device 8× A100 16 × A100 16 × A100 Distributed Training DeepSpeed Zero-2 DeepSpeed Zero-2 DeepSpeed Zero-2 其中,预训练部分又分为两个阶段: 第一阶段:冻结transformer参数,仅训练embedding,在尽量不干扰原模型的情况下适配...
通过更改 max_length 可以指定希望生成响应的长度。将 num_return_sequences 参数设置为大于 1,可以生成多个输出。在脚本中添加以下内容,以提供输入以及如何运行 pipeline 任务的信息: sequences = pipeline ( 'I have tomatoes, basil and cheese at home. What can I cook for dinner?\n', do_sample=True, ...
通过更改 max_length 可以指定希望生成响应的长度。将 num_return_sequences 参数设置为大于 1,可以生成多个输出。在脚本中添加以下内容,以提供输入以及如何运行 pipeline 任务的信息: sequences = pipeline ('I have tomatoes, basil and cheese at home. What can I cook for dinner?\n',do_sample=True,top...
(self,i): return get_data(self.min_length,self.max_length) def show_example(self,example): input_ids,labels = example['input_ids'],example['labels'] x = ''.join([vocab_r[a] for a,b in zip(input_ids,labels) if b==-100]) y = ''.join([vocab_r[a] for a,b in zip(...
max_length=1024, max_prompt_length=512, per_device_train_batch_size=2, per_device_eval_batch_size=2, gradient_accumulation_steps=4, optim="paged_adamw_8bit", num_train_epochs=1, evaluation_strategy="steps", eval_steps=0.2, logging_steps=1, ...
import torchfrom modelscope import snapshot_download, AutoModel, AutoTokenizerimport osmodel_dir = snapshot_download('LLM-Research/Meta-Llama-3-8B-Instruct', cache_dir='/root/autodl-tmp', revision='master')def process_func(example): MAX_LENGTH = 384 # Llama分词器会将一个中文字切分为...
MAX_LENGTH = 128 model = AutoModel.from_pretrained("v2ray/Llama-3-70B") input_text = [ '美国的首都是哪里?' ] input_tokens = model.tokenizer(input_text, return_tensors="pt", return_attention_mask=False, truncation=True, max_length=MAX_LENGTH, ...
max_length 2048 \--check_dataset_strategy warning \--lora_rank 8 \--lora_alpha 32 \--lora_dropout_p 0.05 \--lora_target_modules ALL \--gradient_checkpointing true \--batch_size 1 \--weight_decay 0.1 \--learning_rate 1e-4 \--gradient_accumulation_steps $(expr 16 / $nproc_per_...