tokenizer_path: The path to the tokenizer used to preprocess the text.ckpt_dir: The directory containing the model checkpoints.world_size: The total number of GPUs being used for model parallelism.local_rank: The rank of the current GPU (process) in the group of GPUs used for model parallelism. Most of these are preset within the model and do not need to be adjusted however, if you are getting into using LLMs and inferencing, here is some handy information that breaks down what the flags and switches do. Note: It is recommended to have a dedicated Anaconda environment for LLaMA, we have a few on the test bench so we can mess with packages and parameters and not hose the base installation.īe careful playing with the thermostat! Break Down Of The example.py File and Variables You Need To Know Disk Space the entire set of LLaMa checkpoints is pushing over 200TB.PyTorch installed (at least version 1.7.1).CUDA Toolkit installed (at least version 10.1).NVIDIA drivers installed (at least version 440.33).NVIDIA GPU(s) with a minimum of 16GB of VRAM.Meta LLaMa Requirementsīefore we start, we need to make sure we have the following requirements installed: In this article, we will provide a step-by-step guide on how we set up and ran LLaMA inference on NVIDIA GPUs, this is not guaranteed to work for everyone. It is publicly available and provides state-of-the-art results in various natural language processing tasks. Meta LLaMA is a large-scale language model trained on a diverse set of internet text. The RTX 8000 is a high-end graphics card capable of being used in AI and deep learning applications, and we specifically chose these out of the stack thanks to the 48GB of GDDR6 memory and 4608 CUDA cores on each card, and also Kevin is hoarding all the A6000‘s. We used the Lenovo P920 as a host for the cards, minimizing bottlenecks. In the Storage Review lab, we put two NVIDIA RTX 8000 GPUs on the job of running Meta’s LLaMa model to see how they performed when running inference on large language models. Running inference on these models can be computationally challenging, requiring powerful hardware to deliver real-time results. In recent months, large language models have been the subject of extensive research and development, with state-of-the-art models like GPT-4, Meta LLaMa, and Alpaca pushing the boundaries of natural language processing and the hardware required to run them.
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