如上所述,你还可以通过更改BitsAndBytesConfig中的bnb_4bit_compute_dtype参数来更改量化模型的计算数据类型。 importtorch fromtransformersimportBitsAndBytesConfig quantization_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16 ) 嵌套量化 要启用嵌套量化,你可以使用BitsAn...
您还可以自动量化模型,以 8 位甚至 4 位模式加载,使用 bitsandbytes。4 位加载大 70B 版本大约需要 34 GB 的内存运行。这是如何以 4 位模式加载生成管道: pipeline = pipeline( "text-generation", model=model_id, model_kwargs={ "torch_dtype": torch.bfloat16, "quantization_config": {"load_in_4...
我们将使用 BitsAndBytesConfig 以 4 位格式加载模型。这将大大减少内存消耗,但会牺牲一些准确性。 compute_dtype = getattr(torch, "float16") bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type='nf4', bnb_4bit_compute_dtype=compute_dtype, bnb_4bit_use_double_quant=False...
quantization_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, ) model_id = "mistralai/Mistral-7B-Instruct-v0.1" from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_4...
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...
args.load_in_4bit:quantization_config=BitsAndBytesConfig(load_in_8bit=script_args.load_in_8bit...
@BaileyWei 2-3x slower is to be expected with load_in_4bit (vs 16-bit weights), on any model -- that's the current price of performing dynamic quantization :) Member gante commented Jun 28, 2023 • edited @cnut1648 @younesbelkada If we take the code example from @cnut1648 and...
Unused kwargs: ['_load_in_4bit', '_load_in_8bit', 'quant_method']. These kwargs are not used in <class 'transformers.utils.quantization_config.BitsAndBytesConfig'>. `low_cpu_mem_usage` was None, now set to True since model is quantized. ...
load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16 ) tokenizer = GemmaTokenizer.from_pretrained(base_model_path) #using low_cpu_mem_usage since model is quantized model = AutoModelForCausalLM.from_pretrained(base_model_path,quantization_config=bnb_config,low...
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 ...