如果模型对于单个 GPU 来说太大,则可以设置 device_map=“auto” 以允许Accelerate自动确定如何加载和存储模型权重。 #!pip install accelerategenerator=pipeline(model="openai/whisper-large",device_map="auto") 请注意,如果传递了 device_map=“auto”,则在实例化管道时无需添加参数 device=device,否则您可能会...
首先,尝试运行这段代码 from transformers import AutoModelForCausalLM, AutoTokenizer import torch model_name = "llama-2-7b-hf" model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto") tokenizer = AutoTokenizer.from_pretrained(model_name) text = "say" inputs = tokenizer(text...
Actions Projects1 Security Insights Additional navigation options New issue device_map="auto"doesn't use all available GPUs whenload_in_8bit=True#22595 Closed 2 of 4 tasks yukw777opened this issueApr 5, 2023· 12 comments yukw777commentedApr 5, 2023 ...
Hi@Nidhogg-lyz, thanks for reporting. This happens becausedevice_map= "auto" is not fully supported in diffusers. Hence, some modules are split when it should not be the caseno_split_module_classes. For the meantime, I advise you to not use deivce_map="auto". Otherwise, even if you tr...
from transformersimportAutoTokenizer,AutoModelForCausalLM,GenerationConfig model_name="deepseek-ai/DeepSeek-V2"tokenizer=AutoTokenizer.from_pretrained(model_name,trust_remote_code=True)#`max_memory`should besetbased on your devices max_memory={i:"75GB"foriinrange(8)}#`device_map`cannot besetto`...
fromtransformersimportAutoModelimporttorchdefsetup_optimization():"""优化模型加载配置"""model=AutoModel.from_pretrained("bert-base-chinese",device_map="auto",# 自动设备分配torch_dtype=torch.float16,# 使用半精度浮点数减少内存占用low_cpu_mem_usage=True# 分批加载模型参数)model.eval()# 切换到推理模...
如上的auto_map配置项。configuration_chatglm文件是该config文件的类表现形式。 modeling_chatglm.py文件是源码文件,ChatGLM对话模型的所有源码细节都在该文件中。我之前一直没找到ChatGLM的源码,就是神经网络的相关代码,经过一波的分析,终于是定位到了。所以在config文件中会配置AutoModel API直接取调用modeling_chatglm...
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", quantization_config=quantization_config) 量化一个模型可能花费较长的时间。对于一个 175B 参数量的模型,如果使用一个大型校准数据集 (如“c4”),至少需要 4 个 GPU 时。正如上面提到的那样,许多 GPTQ 模型已经可以在 Hugging Fac...
fromtransformersimportAutoTokenizer importnumpyasnp # Load dataset from the hub dataset = load_dataset(dataset_id,name=dataset_config) # Load tokenizer of FLAN-t5-base tokenizer = AutoTokenizer.from_pretrained(model_id) print(f"Train dataset size:{len(dataset['train'])}") ...
# when doing batched tokenization, and additional methods to map between the # original string (character and words) and the token space. tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True) tokenizer(["Hello, this one sentence!"]) ...