The issue persists, so it's independent from the inf/nan bug and 100% confirmed caused by a combination of using bothload_in_8bit=Trueand multi gpu. This code returns comprehensible language when: it fits on a single GPU's VRAM and useload_in_8bit=True, ...
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) pipe = pipeline("text-generation", model=model, toke...
如果安装bitsandbytes并添加参数 load_in_8bit=True ,也可以传递 8 位加载的模型 # pip install accelerate bitsandbytesimporttorchfromtransformersimportpipelinepipe=pipeline(model="facebook/opt-1.3b",device_map="auto",model_kwargs={"load_in_8bit":True})output=pipe("This is a cool example!",...
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...
下面以使用bitsandbytes将一个小模型转换为 int8 为例,并给出相应的步骤。 首先导入模块,如下。 importtorch importtorch.nnasnn importbitsandbytesasbnb frombnb.nnimportLinear8bitLt 然后就可以定义自己的模型了。请注意,我们支持将任何精度的 checkpoint 或模型转换为 8 位 (FP16、BF16 或 FP32),但目前,...
2.1 Load the model # 确定模型导入精度ifscript_args.load_in_8bitandscript_args.load_in_4bit:...
besides, I just find if I add some parameters in from_pretrained method, may cause error too, like this: AutoModelForCausalLM.from_pretrained(model_id,device_map="auto",load_in_8bit=True,torch_dtype=torch.float32# Add this may cause error) ...
load_in_8bit=True, torch_dtype=torch.float16, device_map="auto", ) tokenizer = AutoTokenizer.from_pretrained(base_model) model = PeftModel.from_pretrained(model, my_model)defgenerate_and_tokenize_prompt(data_point): eval_prompt =f"""You are a powerful text-to-C# model. Your jo...
load_in_8bit=True, device_map='auto', ) tokenizer = AutoTokenizer.from_pretrained('bigscience/bloom-1b1') from peft import LoraConfig, get_peft_model config = LoraConfig( r= 8, #attention heads lora_alpha=32, #alpha scaling target_modules=["query_key_value"], ...
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 ...