load_in_8bit=True, device_map='auto') 注:代码中我已经将模型cache到指定目录中,每次运行不用再从云端下载模型,但是为啥会调用hugggingface.co服务呢,我从源码看下,本地cache目录文件,这是transformers库,自动存放的,/root/.cache/huggingface/modules/transformers_modules/THUDM/chatglm-6b,这里面会存在模型相...
, 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Auto...
Linear8bitLt(64,64, has_fp16_weights=False) ) 此处标志变量has_fp16_weights非常重要。默认情况下,它设置为True,用于在训练时使能 Int8/FP16 混合精度。但是,因为在推理中我们对内存节省更感兴趣,因此我们需要设置has_fp16_weights=False。 现在加载 8 位模型! int8_model.load_state_dict(torch.load("...
# 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 ...
device_map="auto"doesn't use all available GPUs whenload_in_8bit=True#22595 New issue System Info transformersversion: 4.28.0.dev0 Platform: Linux-4.18.0-305.65.1.el8_4.x86_64-x86_64-with-glibc2.28 Python version: 3.10.4 Huggingface_hub version: 0.13.3 ...
BNB 4-bit Quantizationimport torch from transformers import AutoTokenizer, AutoModel path = "OpenGVLab/InternVL2-8B" model = AutoModel.from_pretrained( path, torch_dtype=torch.bfloat16, load_in_4bit=True, low_cpu_mem_usage=True, use_flash_attn=True, trust_remote_code=True).eval() ...
quantization_config = BitsAndBytesConfig(load_in_8bit=True, llm_int8_enable_fp32_cpu_offload=True) AutoModelForCausalLM.from_pretrained(path, device_map='auto', quantization_config=quantization_config) If the model does not fit into VRAM, it reports: ...
将load_in_8bit或load_in_4bit参数添加到from_pretrained()中,并设置device_map="auto"以有效地将模型分发到硬件: from transformers import AutoModelForCausalLM, AutoTokenizer peft_model_id = "ybelkada/opt-350m-lora" model = AutoModelForCausalLM.from_pretrained(peft_model_id, device_map="auto"...
8. 设置LoRA 现在,让我们加载 LoRA 配置。我们将利用 LoRA 来减少可训练参数的数量,从而减少微调模型所需的内存占用。 # Load LoRA lora_config = LoraConfig( task_type="CAUSAL_LM", r=16, lora_alpha=32, target_modules="all-linear", )