from_pretrained("Qwen/Qwen2-0.5B", dtype="float16") >>> input_features = tokenizer("你好!请自我介绍一下。", return_tensors="pd") >>> outputs = model.generate(**input_features, max_length=128) >>> print(tokenizer.batch_decode(outputs[0], skip_special_tokens=True)) ['我是一个AI...
例如,当计算一个小批量的梯度时,某些参数的梯度将比其他参数的梯度更早可用。因此,在GPU仍在运行时就开始使用PCI-Express总线带宽来移动数据是有利的。删除这两个部分之间的`waitall`以模拟这个场景。 :end_tab: :begin_tab:`pytorch` 这种方式效率不高。注意到当列表中的其余部分还在计算时,我们可能就已经开始...
>>> from paddlenlp.transformers import AutoTokenizer, AutoModelForCausalLM >>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B") >>> model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-0.5B", dtype="float16") >>> input_features = tokenizer("你好!请自我介绍一下。", retu...
* batch_size:每次输入模型的图像数量。该参数值越大,要求GPU的内存容量越大。 ``` # Importing a pretrained PyTorch model from TIAToolbox predictor = PatchPredictor(pretrained_model='resnet18-kather100k', batch_size=32) # Users can load any PyTorch model architecture instead using the following s...
from_pretrained("Qwen/Qwen2-0.5B", dtype="float16") >>> input_features = tokenizer("你好!请自我介绍一下。", return_tensors="pd") >>> outputs = model.generate(**input_features, max_length=128) >>> print(tokenizer.batch_decode(outputs[0], skip_special_tokens=True)) ['我是一个AI...
from_pretrained("Qwen/Qwen2-0.5B", dtype="float16") >>> input_features = tokenizer("你好!请自我介绍一下。", return_tensors="pd") >>> outputs = model.generate(**input_features, max_length=128) >>> print(tokenizer.batch_decode(outputs[0], skip_special_tokens=True)) ['我是一个AI...
from_pretrained("Qwen/Qwen2-0.5B", dtype="float16") >>> input_features = tokenizer("你好!请自我介绍一下。", return_tensors="pd") >>> outputs = model.generate(**input_features, max_length=128) >>> print(tokenizer.batch_decode(outputs[0], skip_special_tokens=True)) ['我是一个AI...
>>>frompaddlenlp.transformersimportAutoTokenizer,AutoModelForCausalLM>>>tokenizer=AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B")>>>model=AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-0.5B",dtype="float16")>>>input_features=tokenizer("你好!请自我介绍一下。",return_tensors="pd")>>>outputs...