tokenizer.pad_token = tokenizer.unk_token input = tokenizer(prompts, padding='max_length', max_length=20, return_tensors="pt"); print(input) 在这个例子中,我要求tokenizer填充到max_length。我将max_length设置为20。如果你的示例包含10个标记,tokenizer将添加10个填充标记。 {'input_ids': tensor([...
max_gen_len (Optional[int], optional): Maximum length of the generated response 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. Returns: List[Chat...
# 定义数据集 class TwoSumDataset(torch.utils.data.Dataset): def __init__(self,size = 100000, min_length=10,max_length=20): super(Dataset, self).__init__() self.size = size self.min_length=min_length self.max_length=max_length def __len__(self): return self.size def __getitem...
其他参数,如max_length和批处理大小被设置为使用尽可能多的VRAM(我们目前的配置中约为占用20 GB,如果你是4090,建议使用我们的配置)。 orpo_args = ORPOConfig( learning_rate=8e-6, beta=0.1, lr_scheduler_type="linear", max_length=1024, max_prompt_length=512, per_device_train_batch_size=2, per...
max_length=self.sequence_len, padding=False, return_tensors=None ) input_ids, attention_mask, labels=[], [], []if( result["input_ids"][-1] !=self.eos_token_idandlen(result["input_ids"]) <self.sequence_lenandadd_eos_token
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, ...
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...
Python# self.inv_freq.dtype == torch.bfloat16 when bfloat16 is enabled during trainingt = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)在实际训练时如果开了 bfloat16, self.inv_freq 的 dtype 会被转为 bfloat16, 可以通过简单的代码来看一下位置碰撞的问题...
max_steps=max_steps, warmup_ratio=warmup_ratio, group_by_length=group_by_length, lr_scheduler_type=lr_scheduler_type, report_to="tensorboard" ) 在完成微调之后,我们将使用pipeline进行推理。可以选择各种管道任务的列表,像“图像分类”,“文本摘要”等。还可以为任务选择要使用的模型。为了定制也可以添加...
disable_tqdm=disable_tqdm, report_to="tensorboard", seed=42)# Create the trainertrainer = SFTTrainer( model=model, train_dataset=dataset, peft_config=peft_config, max_seq_length=max_seq_length, tokenizer=tokenizer, packing=packing, formatting_func=format_instruction, ...