pipe=pipeline(model="facebook/opt-1.3b",device_map="auto",model_kwargs={"load_in_8bit":True})output=,do_sample=True,top_p=0.95)
, do_sample=True, top_p=0.95) import torch from transformers import pipeline pipe = pipeline(model="facebook/opt-1.3b", device_map="auto", model_kwargs={"load_in_8bit": True}) output = pipe("This is a cool example!", do_sample=True, top_p=0.95) AutoClass Transformers提供的...
( model_name, load_in_8bit=True, device_map="auto", use_auth_token=True ) model = PeftModel.from_pretrained(model, "tloen/alpaca-lora-7b", adapter_name="eng_alpaca") model.load_adapter("22h/cabrita-lora-v0-1", adapter_name="portuguese_alpaca") model.set_adapter("eng_alpaca") ...
The issue persists, so it's independent from the inf/nan bug and 100% confirmed caused by a combination of using bothload_in_8bit=Trueand multi gpu. This code returns comprehensible language when: it fits on a single GPU's VRAM and useload_in_8bit=True, ...
Before this change, the following code would attempt to load the whole model on the first GPU in a two gpu setup, potentially causing OOM errors. After the change, it loads it evenly across GPUs, as intended. model = AutoModelForCausalLM.from_pretrained( checkpoint, load_in_8bit=True, ...
所有的操作都集成在Linear8bitLt模块中,你可以轻松地从bitsandbytes库中导入它。它是torch.nn.modules的子类,你可以仿照下述代码轻松地将其应用到自己的模型中。 下面以使用bitsandbytes将一个小模型转换为 int8 为例,并给出相应的步骤。 首先导入模块,如下。
只要您的模型支持使用🤗 Accelerate加载并包含torch.nn.Linear层,您就可以在调用[~PreTrainedModel.from_pretrained]方法时使用load_in_8bit或load_in_4bit参数来量化模型。这应适用于任何模式。 fromtransformersimportAutoModelForCausalLM model_8bit=AutoModelForCausalLM.from_pretrained("facebook/opt-350m",load...
2.1 Load the model # 确定模型导入精度ifscript_args.load_in_8bitandscript_args.load_in_4bit:...
# load model from the hub model = AutoModelForSeq2SeqLM.from_pretrained(model_id, load_in_8bit=True, device_map="auto") 现在,我们可以使用 peft 为LoRA int-8 训练作准备了。 from peft import LoraConfig, get_peft_model, prepare_model_for_int8_training, TaskType # Define LoRA Config lor...
pipeline = pipeline("text-generation", model=model, model_kwargs={"torch_dtype": torch.bfloat16,"quantization_config": {"load_in_4bit": True} },)有关使用 Transformers 模型的更多详细信息，请查看模型卡。模型卡https://hf.co/gg-hf/gemma-2-9b 与 Google Cloud 和推理端点的集成 ...