5 окт. 2023 г. · by using device_map = 'cuda' !pip install accelerate. then use from transformers import AutoModelForCausalLM model = AutoModelForCausalLM ... Choose available GPU devices with device_map - Stack Overflow Alternative to device_map = "auto" in Huggingface Pretrained Другие результаты с сайта stackoverflow.com

27 дек. 2022 г. · The above result is not expected behavior. Without device_map='auto' at line 5, it works correctly. Line 5 becomes model = AutoModelForCausalLM.

18 мар. 2024 г. · Using device_map="auto" will split the large model into smaller chunks, store them in the CPU, and then put them sequentially into the GPU for each input.

Setting device_map="auto" automatically fills all available space on the GPU(s) first, then the CPU, and finally, the hard drive (the absolute slowest option) ...

12 июл. 2024 г. · I am curious about how to dispatch a large language model (LLM) into smaller pieces across GPUs using the vllm library.

I'm trying to load hugging face transformers LM model setting 4 bit quantization configuration but i'm getting below error, RuntimeError: No GPU found.

25 мая 2024 г. · 利用device_map实现多卡训练：model = AutoModelForCausalLM.from_pretrained(model_name, device_map='auto')【取代DP】. 【代码】利用device_map实现 ...

11 мар. 2024 г. · Is there a way to automatically infer the device of the model when using auto device map, and cast the input tensor to that? Here's what I have ...

... AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt ...

21 мая 2024 г. · model argument에 torch_dtype을 통해 명시한다. 1. model = AutoModelForCausalLM.from_pretrained(model_id, device_map = "auto" , torch_dtype = ...