以4 比特加载模型的基本方法是通过在调用from_pretrained方法时传递参数load_in_4bit=True,并将设备映射设置成“auto”。 fromtransformersimportAutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m", load_in_4bit=True, device_map="auto") ... 这样就行了! 一般地,我们...
然后,我们看下 4 比特量化的 GPU 显存消耗峰值是多少。可以用与之前相同的 API 将模型量化为 4 比特 - 这次参数设置为load_in_4bit=True而不是load_in_8bit=True。 model = AutoModelForCausalLM.from_pretrained("bigcode/octocoder", load_in_4bit=True, low_cpu_mem_usage=True, pad_token_id=0) ...
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...
# Load tokenizer and model with QLoRA configuration bnb_config = BitsAndBytesConfig( 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 =...
load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_quant_type='nf4', bnb_4bit_use_double_quant=False, max_memory=24000 ) model = AutoModelForCausalLM.from_pretrained( model_id, quantization_config=nf4_config,
2.1 Load the model # 确定模型导入精度ifscript_args.load_in_8bitandscript_args.load_in_4bit:...
Checklist 1. I have searched related issues but cannot get the expected help. 2. The bug has not been fixed in the latest version. Describe the bug 请问如何从本地加载这个数据集 就算我复制了一份缓存进去,仍然会有另一个站点连不上: Loading calibrate datase
The `load_in_4bit` and `load_in_8bit` arguments are deprecated and will be removed in the future versions. Please, pass a `BitsAndBytesConfig` object in `quantization_config` argument instead. Loading checkpoint shards: 100%|█████████████████████████...
Using load_in_4bit makes the model extremely slow (with accelerate 0.21.0.dev0 and bitsandbytes 0.39.1, should be latest version and I installed from source) Using the following code from transformers import LlamaTokenizer, AutoModelForCausalLM, AutoTokenizer import torch from time import time...
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 ...