20 июн. 2023 г. · When I try to move the model back to CPU to free up GPU memory for other processing, I get an error: model = model.to('cpu') torch.cuda.empty_cache()

5 окт. 2023 г. · huggingface accelerate could be helpful in moving the model to GPU before it's fully loaded in CPU, so it worked when GPU memory > model size > CPU memory. Run pre-trained LLM model on CPU - ValueError: Expected a ... In Pytorch and Huggingface transformers, why does loading ... Другие результаты с сайта stackoverflow.com

Therefore, an automatically computed device map might be too intense on the CPU. Move a few modules to the disk device if you get crashes due to a lack of RAM.

27 дек. 2022 г. · My machine has two A100 (80 GB) GPUs, and I confirmed that the model is loaded on two GPUs when I use device_map='auto'.

21 мар. 2024 г. · Transformers models can be easily loaded across multiple devices using device_map="auto". This will automatically allocate weights across available devices.

Run Llama 2 locally on CPU or GPU. Download the Llama 2 Meta AI models using the link below: https://ai.meta.com/resources/models-and-libraries/llama-downloads/

25 мая 2024 г. · 设计设备映射时，可以让Accelerate库来处理设备映射的计算; 通过设置 device_map 为支持的选项之一（"auto"、 "balanced"、 "balanced_low_0"、 ...

11 мар. 2024 г. · This is a question on the Huggingface transformers library. Is there a way to automatically infer the device of the model when using auto device map, and cast ...

20 авг. 2023 г. · This feature is beneficial for users who need to fit large models and distribute them between the GPU and CPU. Adjusting Outlier Threshold.

4 окт. 2023 г. · によると、transformersのpipeline実行時に device_map="auto" を渡すと、大規模なモデルでも効率よく実行してくれるとのことです。 内部的にどういう動作 ...