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_mod
from peft import PeftModel from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig model_name = "decapoda-research/llama-7b-hf" tokenizer = LlamaTokenizer.from_pretrained(model_name) model = LlamaForCausalLM.from_pretrained( model_name, load_in_8bit=True, device_map="auto", ...
# 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...
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 ...
load_in_8bit=True, device_map="auto", ) pipeline = transformers.pipeline( "text-generation", model=model, tokenizer=tokenizer, ) 需要注意的是,INT8 混合精度推理使用的浮点精度是torch.float16而不是torch.bfloat16,因此请务必详尽地对结果进行测试。
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() ...
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 ...
Bitsandbytes now allows the pushing of 4bit models to hub (this was already possible for 8bit models). Bitsandbytes supports 4bit, nf4 and 8bit formats. Any of these models should correctly load using model.AutoModelForCausalLM (see docs...
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", )