首先尝试从 kwargs 中获取load_in_8bit和load_in_4bit,默认为 False ifquantization_configisNoneand(load_in_8bitorload_in_4bit):quantization_method_from_args=QuantizationMethod.BITS_AND_BYTESquantization_config,kwargs=BitsAndBytesConfig.from_dict(config_dict={"load_in_8bit":load_in_8bit,"load_...
( load_in_4bit=args.bits == 4, load_in_8bit=args.bits == 8, llm_int8_threshold=6.0, llm_int8_has_fp16_weight=False, bnb_4bit_compute_dtype=compute_dtype, bnb_4bit_use_double_quant=args.double_quant, bnb_4bit_quant_type=args.quant_type ), torch_dtype=(torch.float32 if ...
用户可以轻松地启用与 Transformers 类似的 API 来进行量化和推理。只需将 ‘load_in_4bit’ 设为 true,然后从 HuggingFace URL 或本地路径输入模型即可。下方提供了启用仅限权重的 (weight-only) INT4 量化的示例代码: 默认设置为:将权重存储为 4 位,以 8 位进行计算。但也支持不同计算数据类型 (dtype) ...
只需将 ‘load_in_4bit’ 设为 true,然后从 HuggingFace URL 或本地路径输入模型即可。下方提供了启用仅限权重的 (weight-only) INT4 量化的示例代码: from transformers import AutoTokenizer, TextStreamer fromintel_extension_for_transformers.transformers import AutoModelForCausalLM model_name = "Intel/neura...
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...
前文中提到,这里量化的程序和原版程序没有使用上的区别,所以多数程序都可以保持原样。为了能够让模型正确的通过 4BIT 方式加载和运行,我们需要调整两处内容: 我们需要调整前两篇文章中相关项目使用的model.py中的model_id变量,以及在AutoModelForCausalLM.from_pretrained调用中加上load_in_4bit=True: ...
As a quickstart, load a model in 4bit by (at the time of this writing) installing accelerate and transformers from source, and make sure you have installed the latest version of bitsandbytes library (0.39.0). pip install -q -U bitsandbytes pip install -q -U git+https://github.com/...
Several months ago, we received a list of several new characters that are to appear in Transformers Animated season 3. Among the one listed on that list was one described as "A***, a bit different than previous show, more details". Many assumed that this character would be either Acree...
from_pretrained("google/flan-ul2", load_in_8bit=True, device_map="auto") >>> tokenizer = AutoTokenizer.from_pretrained("google/flan-ul2") >>> inputs = tokenizer("A step by step recipe to make bolognese pasta:", return_tensors="pt") >>> outputs = model.generate(**inputs) >>...
transformers库是一个用于自然语言处理(NLP)的机器学习库,提供了近几年在NLP领域取得巨大成功的预训练模型,例如BERT、GPT、RoBERTa、T5等。 该库由Hugging Face公司开发,是目前最流行的NLP预训练模型库之一。在实际应用中,使用已经训练好的模型可以显著提高模型的效果和速度。