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...
首先安装trl包并下载脚本: pip install trl gitclonehttps://github.com/lvwerra/trl 然后,你就可以运行脚本了: python trl/examples/scripts/sft_trainer.py \ --model_name meta-llama/Llama-2-7b-hf \ --dataset_name timdettmers/openassistant-guanaco \ --load_in_4bit \ --use_peft \ --batch_...
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...
File "/root/miniconda3/lib/python3.10/site-packages/lmdeploy/lite/utils/calib_dataloader.py", line 58, in get_ptb traindata = load_dataset('ptb_text_only', 'penn_treebank', split='train') File "/root/miniconda3/lib/python3.10/site-packages/datasets/load.py", line 2549, in load_datas...
peft_model_id = "username/my-awesome-model" model2 = LlamaForCausalLM.from_pretrained(peft_model_id, device_map="auto", load_in_4bit=True, use_auth_token= hf_auth) which should also work according to the docs, but gave me does not appear to have a file named config.json ...
# 确定模型导入精度ifscript_args.load_in_8bitandscript_args.load_in_4bit:raiseValueError("You can't load the model in 8 bits and 4 bits at the same time")elifscript_args.load_in_8bitorscript_args.load_in_4bit:quantization_config=BitsAndBytesConfig(load_in_8bit=script_args.load_in_8...
load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16, bnb_4bit_use_double_quant=False, model = AutoModelForCausalLM.from_pretrained( model_name, quantization_config=bnb_config, # use the gpu device_map= "auto" ...
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,
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 ...
load_tests Adding small benchmark script. (#881) 1年前 proto feat: add mistral model (#1071) 11个月前 router Modify the default formax_new_tokens. (#1097) 11个月前 server 不支持初始化npu torch.Generator, 规则判断强制转换成cpu 9个月前 ...