Llama 2 cpu inference tutorial. Convert the fine-tuned model to GGML.
In comparison with Llama 2, the Meta group has made the next notable enhancements: Adoption of grouped question consideration (GQA), which improves inference effectivity. The integration comes with native RoCm support for AMD GPUs. Note: Download takes a while due to the size, which is 6. conda create -n llama-cpp python=3. cpp and ollama on Intel GPU. To do so, you need : LlamaForCausalLM which is like the brain of "Llama 2", LlamaTokenizer which helps "Llama 2" understand and break down words. It is built on the Google transformer architecture and has been fine-tuned for These steps will let you run quick inference locally. cpp, and find your inference speed Jun 18, 2023 · With the building process complete, the running of llama. 1. Then, go back to the thread window. Running LLMs on a computer’s CPU is getting much attention lately, with many tools trying to make it easier and faster. The DeepSpeed-Chat training framework now provides system support for the Llama and Llama-2 models across all three stages of training. Navigate to the main llama. run_generation. DeepSparse now supports accelerated inference of sparse-quantized Llama 2 models, with inference speeds 6-8x faster over the baseline at 60-80% sparsity. We're unlocking the power of these large language models. As a concrete example, we’ll look at running Llama 2 on an A10 GPU throughout the guide. co LangChain is a powerful, open-source framework designed to help you develop applications powered by a language model, particularly a large Sep 6, 2023 · Sep 6, 2023. Definitions. Feel free to change the dataset: there are many options on the Hugging Face Hub. We have to make sure that the adapter that we want to add has been fine-tuned for our base LLM, i. e. Sep 5, 2023 · tokenizer. Key Takeaways We expanded our Sparse Fine-Tuning research results to include Llama 2. cpp with 4-bit / 5-bit quantization support! [10/11] The training data and scripts of LLaVA-1. Even when only using the CPU, you still need at least 32 GB of RAM. Getting started with Meta Llama. The 'llama-recipes' repository is a companion to the Llama 2 model. We release all our models to the research community. We’ve achieved a latency of 29 milliseconds per token for The dynamic generator supports all inference, sampling and speculative decoding features of the previous two generators, consolidated into one API (with the exception of FP8 cache, though the Q4 cache mode is supported and performs better anyway, see here. For more detailed examples leveraging HuggingFace, see llama-recipes. # Set gpu_layers to the number of layers to offload to GPU. It supports model parallelism (MP) to fit large models that would otherwise not fit in GPU memory. 🌎; A notebook on how to run the Llama 2 Chat Model with 4-bit quantization on a local computer or Google Colab. This was followed by recommended practices for Benchmark. ) The library works the same with a CPU, but the inference can take about three times longer compared to using it on a GPU. ggmlv3. App overview. GPTQ drastically reduces the memory requirements to run LLMs, while the inference latency is on par with FP16 inference. Create a prompt baseline. This will launch the respective model within a Docker container, allowing you to interact with it through a command-line interface. Install langchain library which DeepSpeed Inference uses 4th generation Intel Xeon Scalable processors to speed up the inferences of GPT-J-6B and Llama-2-13B. So for example given In addition, we also provide a number of demo apps, to showcase the Llama 2 usage along with other ecosystem solutions to run Llama 2 locally, in the cloud, and on-prem. py. Run Examples . Llama 2, developed by Meta, is a family of large language models ranging from 7 billion to 70 billion parameters. In preliminary evaluations, the Alpaca model performed similarly to OpenAI's text-davinci-003 model for single-turn instruction following, but is smaller in size and easier/cheaper to reproduce with a cost of less than $600. We assume you know the benefits of fine-tuning, have a basic understanding of Llama-2 and LoRA, and are excited about running models at the edge 😎. Set to 0 if no GPU acceleration is available on your system. To download models from Hugging Face, you must first have a Huggingface account. For more information about what those are and how they work, see Jul 23, 2023 · In this tutorial video, Ill show you how to build a sophisticated Medical Chatbot using powerful open-source technologies. Resources. On this page. Sadly there is a bit of friction here due to licensing (I can't directly upload the checkpoints, I think). However, these models do not come cheap! 4 days ago · DeepSpeed provides a seamless inference mode for compatible transformer based models trained using DeepSpeed, Megatron, and HuggingFace, meaning that we don’t require any change on the modeling side such as exporting the model or creating a different checkpoint from your trained checkpoints. Optimize Llama 3 Inference with PyTorch* A previous article covers the importance of model compression and overall inference optimization in developing LLM-based applications. This tutorial focuses on applying WOQ to meta-llama/Meta-Llama-3–8B Stanford Alpaca 1 is fine-tuned version of LLaMA 2 7B model using 52,000 demonstrations of following instructions. AutoGPTQ supports Exllama kernels for a wide range of architectures. In particular, we will leverage the latest, highly-performant Llama 2 chat model in this project. With the higher-level APIs and RAG support, it's convenient to deploy LLMs (Large Language Models) in your application with LLamaSharp. To enable GPU support, set certain environment variables before compiling: set Jul 18, 2023 · In this easy-to-follow guide, we will discover how to run quantized versions of open-source LLMs on local CPU inference for retrieval-augmented generation (aka document Q&A) in Python. Test llama. txt file: 1. This demonstration provides a glimpse into the potential of these devices Llama-2-7B-Chat: Open-source fine-tuned Llama 2 model designed for chat dialogue. Next, install the necessary Python packages from the requirements. Intel® Data Center GPU Max Series is a new GPU designed for AI for which DeepSpeed will also be enabled. Convert the fine-tuned model to GGML. This tutorial covers the prerequisites, instructions, and troubleshooting tips. Once we have those checkpoints, we have to convert them into Nov 8, 2023 · This blog post explores methods for enhancing the inference speeds of the Llama 2 series of models with PyTorch’s built-in enhancements, including direct high-speed kernels, torch compile’s transformation capabilities, and tensor parallelization for distributed computation. [11/6] Support Intel dGPU and CPU platforms. These names follow the format of the HuggingFace model and dataset names on their hub. 15 . 1. ONNX Runtime applied Megatron-LM Tensor Parallelism on the 70B model to split the original model weight onto Jul 25, 2023 · In this article, I’ll show you how to run Llama 2 on local CPU inference for document Q&A, namely how to use Llama 2 to answer questions from your own docs on your own machine. cpp library and llama-cpp-python package provide robust solutions for running LLMs efficiently on CPUs. –bf16 True enables half-precision training at brain-float 16 –num_train_epochs 2 sets the number of epochs to 2. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. LLamaSharp is a cross-platform library to run 🦙LLaMA/LLaVA model (and others) on your local device. Sep 13, 2023 · We successfully fine-tuned 70B Llama model using PyTorch FSDP in a multi-node multi-gpu setting while addressing various challenges. For ease of use, the examples use Hugging Face converted versions of the models. About In this tutorial, we will explore Llama-2 and demonstrate how to fine-tune it on a new dataset using Google Colab. Nov 1, 2023 · The speed of inference is getting better, and the community regularly adds support for new models. The model’s scale and complexity place many demands on AI accelerators, making it an ideal benchmark for LLM training and inference performance of PyTorch/XLA on Cloud TPUs. Discover the latest trends, research, and advancements in artificial Nov 14, 2023 · ONNX Runtime supports multi-GPU inference to enable serving large models. Originally, this was the main difference with GPTQ models, which are loaded and run on a GPU. Jan 27, 2024 · Inference Script. from llama_cpp import Llama. Our latest version of Llama – Llama 2 – is now accessible to individuals, creators, researchers, and businesses so they can experiment, innovate, and scale their ideas responsibly. In a conda env with PyTorch / CUDA available clone and download this repository. run_generation_with_deepspeed. Inference with Llama 3 70B consumes at least 140 GB of GPU RAM. Running Llama 2 on CPU Inference Locally for Document Q&A _ by Kenneth Leung _ Jul, 2023 _ Towards Data Science - Free download as PDF File (. Llama 2 model Distributed Inference with DeepSpeed with AutoTP feature with May 6, 2024 · According to public leaderboards such as Chatbot Arena, Llama 3 70B is better than GPT-3. , Llama 2 7B. Dec 6, 2023 · Download the specific Llama-2 model ( Llama-2-7B-Chat-GGML) you want to use and place it inside the “models” folder. For Llama 3 70B: ollama run llama3-70b. Additionally, we will cover new methodologies and fine-tuning techniques that can help reduce memory usage and speed up the training process. This guide provides information and resources to help you set up Llama including how to access the model, hosting, how-to and integration guides. Sep 4, 2023 · GGML was designed to be used in conjunction with the llama. Loading the model requires multiple GPUs for inference, even with a powerful NVIDIA A100 80GB GPU. Nov 22, 2023 · Yes No. Before combining adapters, we need to add them to the base LLM. In case you have already your Llama 2 models on the disk, you should load them first. So Step 1, get the Llama 2 checkpoints by following the Meta instructions. Llama 2: open source, free for research and commercial use. If you want to learn how to fine-tune other models, check out this Mistral 7B Tutorial: A Step-by-Step Guide to Using and Fine-Tuning Mistral 7B. Meta Llama 3 is the latest in Meta’s line of language models, with versions containing 8 billion and 70 billion parameters. In this tutorial, we’ll focus on efficiently packaging and deploying Large Language Models (LLM), such as Llama2 🦙, using NVIDIA Triton Inference Server 🧜♂️, making them production-ready in no time. cpp library, also created by Georgi Gerganov. Fine-tuning. , 26. Leverages publicly available instruction datasets and over 1 million human annotations. The llama. Nov 27, 2023 · Add Multiple Adapters to Llama 2. 67 words per second There is also an extra message shown during text generation that reports the number and speed at which tokens are being generated. Running Llama 2 and other Open-Source LLMs on CPU Inference Locally for Document Q&A Preface This is a fork of Kenneth Leung's original repository, that adjusts the original code in several ways: In this video, @DataProfessor shows you how to build a Llama 2 chatbot in Python using the Streamlit framework for the frontend, while the LLM backend is han Jul 25, 2023 · Let’s talk a bit about the parameters we can tune here. . GPT-J model with INT4 Weight Only Quantization. Merge the LoRA Weights. Calculating the operations-to-byte (ops:byte) ratio of your GPU. 10. This release includes model weights and starting code for pretrained and fine-tuned Llama language models — ranging from 7B to 70B parameters. To deploy a Llama 2 model, go to the model page and click on the Deploy -> Inference Endpoints widget. Aug 25, 2023 · Introduction. We’ll cover: Reading key GPU specs to discover your hardware’s capabilities. Now we have seen a basic quick-start run, let's move to a Paperspace Machine and do a full fine-tuning run. You can view models linked from the ‘Introducing Llama 2’ tile or filter on the ‘Meta’ collection, to get started with the Llama 2 models. Get up and running with Llama 3, Mistral, Gemma 2, and other large language models. Code Llama is a family of state-of-the-art, open-access versions of Llama 2 specialized on code tasks, and we’re excited to release integration in the Hugging Face ecosystem! Code Llama has been released with the same permissive community license as Llama 2 and is available for commercial use. However, to run the larger 65B model, a dual GPU setup is necessary. - ollama/ollama Aug 16, 2023 · A fascinating demonstration has been conducted, showcasing the running of Llama 2 13B on an Intel ARC GPU, iGPU, and CPU. Quantize the model. [2024/04] You can now run Llama 3 on Intel GPU using llama. llama. Note: All of these library are being updated and changing daily, so this formula worked for me in October 2023. The response generation is so fast that I can't even keep up with it. However, with its 70 billion parameters, this is a very large model. 7% of its original size. cpp and ollama with ipex-llm; see the quickstart here. Nov 28, 2023 · 2. Hugging Face account and token. 2. Merging Llama 3 Aug 5, 2023 · Step 3: Configure the Python Wrapper of llama. The updates to the model includes a 40% larger dataset, chat variants fine-tuned on human preferences using Reinforcement Learning with Human Feedback (RHLF), and scaling further up all the way to 70 billion parameter models. For 7B models, we advise you to select "GPU [medium] - 1x Nvidia A10G". We saw how 🤗 Transformers and 🤗 Accelerates now supports efficient way of initializing large models when using FSDP to overcome CPU RAM getting out of memory. Based on llama. You will need to re-start your notebook from the beginning. The results include 60% sparsity with INT8 quantization and no drop in accuracy. cpp has a “convert. Optimized tokenizer with a vocabulary of 128K tokens designed to encode language extra Jul 23, 2023 · Download Llama2 model to your local environment. json” which is in the adapter directory. Get Token CPU: ~0. 0) LLaMA (includes Alpaca, Vicuna, Koala, GPT4All, and Wizard) MPT Clone this example. We will continue to improve it for new devices and new LLMs. Open the Windows Command Prompt by pressing the Windows Key + R, typing “cmd,” and pressing “Enter. Llama 2 model with INT8 Weight Only Quantization. pdf), Text File (. cpp for LLM inference Description:Dive into the world of advanced coding techniques with our tutorial on Codellama. In this DeepSpeed-Inference introduces several features to efficiently serve transformer-based PyTorch models. Watch the accompanying video walk-through (but for Mistral) here! If you'd like to see that notebook instead, click here. com/rohanpaul_ai🔥🐍 Checkout the MASSIVELY UPGRADED 2nd Edition of my Book (with 1300+ pages of Dense Python Knowledge) Covering A notebook on how to fine-tune the Llama 2 model on a personal computer using QLoRa and TRL. First, we want to load a llama-2-7b-chat-hf model and train it on the mlabonne/guanaco-llama2-1k (1,000 samples), which will produce our fine-tuned model llama-2-7b-miniguanaco. The library is written in C/C++ for efficient inference of Llama models. cpp, llama-cpp-python. A notebook on how to quantize the Llama 2 model using GPTQ from the AutoGPTQ library. txt) or read online for free. 🌎; ⚡️ Inference. Oct 30, 2023 · –world_size 8 indicates the number of workers in the distributed system. cpp. Jul 31, 2023 · In this video, you'll learn how to use the Llama 2 in Python. Full parameter fine-tuning is a method that fine-tunes all the parameters of all the layers of the pre-trained model. model llama 2 tokenizer; Step 5: Load the Llama 2 model from the disk. Loading an LLM with 7B parameters isn’t possible on consumer hardware without quantization. In the top-level directory run: pip install -e . [10/12] LLaVA is now supported in llama. Fortunately, many of the setup steps are similar to above, and either don't need to be redone (Paperspace account, LLaMA 2 model request, Hugging Face account), or just redone in the same way. Llama 2 includes both a base pre-trained model and a fine-tuned model for chats available in three sizes ( 7B, 13B & 70B parameter Nov 6, 2023 · Quantized models are serializable and can be shared on the Hub. Some key benefits of using LLama. the path of the models Once the model download is complete, you can start running the Llama 3 models locally using ollama. Explore cutting-edge AI insights on the Habana blog . To support this, we encountered a spectrum of issues, spanning from minor runtime errors to intricate performance-related challenges. Additionally, you will find supplemental materials to further assist you while building with Llama. cpp begins. 7 times faster training speed with a better Rouge score on the advertising text generation task. co/spaces and select “Create new Space”. We have fine-tuned our model using the GPU. 6 GB, i. In the model section, select the Groq Llama 3 70B in the "Remote" section and start prompting. py” that will do that for you. Here is a high-level overview of the Llama2 chatbot app: The user provides two inputs: (1) a Replicate API token (if requested) and (2) a prompt input (i. 5 are released here, and evaluation scripts are released here! [10/10] Roboflow Deep Dive: First Impressions with LLaVA-1. 🌎; 🚀 Deploy Jul 29, 2023 · Learn how to run Llama 2 on CPU inference locally for document Q&A using Python on Linux or macOS. At present, inference is only on the CPU, but we hope to support GPU inference in the future through alternate backends. Llama-2-7B-Chat: Open-source fine-tuned Llama 2 model designed for chat dialogue. In May 9, 2024 · Launch the Jan AI application, go to the settings, select the “Groq Inference Engine” option in the extension section, and add the API key. Jul 24, 2023 · Llama 1 vs Llama 2 Benchmarks — Source: huggingface. Sign up at this URL, and then obtain your token at this location. 9 conda activate llama-cpp. Setup python and virtual environment. After 4-bit quantization with GPTQ, its size drops to 3. This tutorial will use QLoRA, a fine-tuning method that combines quantization and LoRA. Even in FP16 precision, the LLaMA-2 70B model requires 140GB. Version 2 has a more permissive license than version 1, allowing for commercial use. The LLM model used in this May 22, 2024 · Explore how we can optimize inference on CPUs for scalable, low-latency deployments of Llama 3. Llama 2 model Distributed Inference with DeepSpeed with AutoTP feature on BF16. LLaMa. cpp main binary. This model was contributed by zphang with contributions from BlackSamorez. Discover the power of QLoRA for finetuning on Google Colab's fr Nov 6, 2023 · Llama 2 is a state-of-the-art LLM that outperforms many other open source language models on many benchmarks, including reasoning, coding, proficiency, and knowledge tests. cpp , inference with LLamaSharp is efficient on both CPU and GPU. To run inference on multi-GPU for compatible models Large language model. Visit the Meta website and register to download the model/s. By leveraging 4-bit quantization technique, LLaMA Factory's QLoRA further improves the efficiency regarding the GPU memory. Aug 30, 2023 · In mid-July, Meta released its new family of pre-trained and finetuned models called Llama-2 ( L arge La nguage Model- M eta A I), with an open source and commercial character to facilitate its use and expansion. 5 GB. Discover Llama 2 models in AzureML’s model catalog. The goal of this repository is to provide examples to quickly get started with fine-tuning for domain adaptation and how to run inference for the fine-tuned models. Even for smaller models, MP can be used to reduce latency for inference. cpp in running open Apr 20, 2024 · The Llama 3 is an auto-regressive LLM based mostly on a decoder-only transformer. Llama 2 model with INT8 Quantization with SmoothQuant technique. If you want to use only the CPU, you can replace the content of the cell below with the following lines. This works even when you don't even meet the ram requirements (32GB), the inference will be ≥10x slower than DDR4, but you can still get an adequate summary while on a coffee break. We will be following these steps: Run Llama-2 on CPU Apr 29, 2024 · Building a chatbot using Llama 3; Method 2: Using Ollama; What is Llama 3. 1 Go to huggingface. 8G. This tutorial shows how I use Llama. The code of the implementation in Hugging Face is based on GPT-NeoX Nov 17, 2023 · This guide will help you understand the math behind profiling transformer inference. llm = Llama(. 5. 78 [ ] The abstract from the paper is the following: In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Nov 14, 2023 · ONNX Runtime supports multi-GPU inference to enable serving large models. model_path Aug 1, 2023 · #llama2 #llama #largelanguagemodels #generativeai #llama #deeplearning #openai #QAwithdocuments #ChatwithPDF ⭐ Learn LangChain: Jan 16, 2024 · Step 1. ai/mbermanIn this video, I show you how to fine-tune LLaMA 2 (and other LLMs) for your s Sep 28, 2023 · Step 1: Create a new AutoTrain Space. More details here. Show Inference Code. Fine-tune with LoRA. The SDSC Voyager supercomputer is an innovative AI system designed specifically for science and engineering research at scale. Jul 18, 2023 · You can try out Text Generation Inference on your own infrastructure, or you can use Hugging Face's Inference Endpoints. For more examples, see the Llama 2 recipes repository. Llama 2 is an open source large language model created by Meta AI . Then find the process ID PID under Processes and run the command kill [PID]. Since each Intel® Gaudi®2 AI accelerators node contains 8 Intel Gaudi AI accelerators cards, we will set this to 8 to leverage all the cards on the node. cpp folder using the cd command. Oct 6, 2023 · To re-try after you tweak your parameters, open a Terminal ('Launcher' or '+' in the nav bar above -> Other -> Terminal) and run the command nvidia-smi. Oct 23, 2023 · In this tutorial, we are going to walk step by step how to fine tune Llama-2 with LoRA, export it to ggml, and run it on the edge on a CPU. Today, we’re excited to release: As the neural net architecture is identical, we can also inference the Llama 2 models released by Meta. In this notebook and tutorial, we will fine-tune Meta's Llama 2 7B. Getting started with Llama 2 on Azure: Visit the model catalog to start using Llama 2. 2 Give your Space a name and select a preferred usage license if you plan to make your model or Space public. Sep 18, 2023 · First, in lines 2, 5, and 8 we define the model_name, the dataset_name and the new_model. Testing conducted to date has not — and could not — cover all scenarios. In my latest Towards Data Science post, I share how to perform CPU inference of open-source large language models (LLMs) like Llama 2 for document Q&A (aka retrieval-augmented generation). Beam provides a repo of examples, and you can clone this example app by running this command: beam create-app llama2. This is a guide on how to use the --prompt-cache option with the llama. q4_0. bin. Sep 12, 2023 · Sign up for Gradient and get $10 in free credits today: https://grdt. The abstract from the paper is the following: In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. ONNX Runtime applied Megatron-LM Tensor Parallelism on the 70B model to split the original model weight onto Mar 26, 2024 · Introduction. To install Python, visit the Python website, where you can choose your OS and download the version of Python you like. With the new weight compression feature from OpenVINO, now you can run llama2–7b with less than 16GB of RAM on CPUs! One of the most exciting topics of 2023 in AI should be the emergence of open-source LLMs like Llama2, Red Pajama, and MPT. For fast inference on GPUs, we would need 2x80 GB GPUs. Download the model. Nov 15, 2023 · Let’s dive in! Getting started with Llama 2. For instance, one can use an RTX 3090, an ExLlamaV2 model loader, and a 4-bit quantized LLaMA or Llama-2 30B model, achieving approximately 30 to 40 tokens per second, which is huge. You can find these models readily available in a Hugging Face Jul 27, 2023 · The 7 billion parameter version of Llama 2 weighs 13. Learn how to use Sentence Transfor LLaMA 2 represents a new step forward for the same LLaMA models that have become so popular the past few months. You can find this information in the file “adapter_config. cpp was developed by Georgi Gerganov. [2024/04] ipex-llm now supports Llama 3 on both Intel GPU and CPU. ask a question). # CPU llama-cpp-python!pip install llama-cpp-python==0. Compared to ChatGLM's P-Tuning, LLaMA Factory's LoRA tuning offers up to 3. We will use Python to write our script to set up and run the pipeline. . cd into the new llama2 directory. Models in the catalog are organized by collections. You can also convert your own Pytorch language models into the GGUF format. Currently, the following models are supported: BLOOM; GPT-2; GPT-J; GPT-NeoX (includes StableLM, RedPajama, and Dolly 2. Full run. PEFT, or Parameter Efficient Fine Tuning, allows Oct 23, 2023 · Run Llama-2 on CPU. The following 5 python scripts are provided in Github repo example directory to launch inference workloads with supported models. Llama 2 model with BF16. First things first, we need to download a Llama2 model to our local machine. [2024/04] ipex-llm now provides C++ interface, which can be used as an accelerated backend for running llama. 🐦 TWITTER: https://twitter. ”. Feb 2, 2024 · This GPU, with its 24 GB of memory, suffices for running a Llama model. It can load GGML models and run them on a CPU. We’ll use the Python wrapper of llama. 3 In order to deploy the AutoTrain app from the Docker Template in your deployed space select Docker > AutoTrain. For Llama 3 8B: ollama run llama3-8b. py In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla-70B and PaLM-540B. This repository is intended as a minimal example to load Llama 2 models and run inference. Jul 21, 2023 · Llama 2 supports longer context lengths, up to 4096 tokens. It implements the Meta’s LLaMa architecture in efficient C/C++, and it is one of the most dynamic open-source communities around the LLM inference with more than 390 contributors, 43000+ stars on the official GitHub repository, and 930+ releases. Llama 2 is a new technology that carries potential risks with use. 5 and some versions of GPT-4. Aug 16, 2023 · Download 3B ggml model here llama-2–13b-chat. Step 1: Prerequisites and dependencies. Aug 31, 2023 · Training Causal Language Models on SDSC’s Gaudi-based Voyager Supercomputing Cluster. Jul 24, 2023 · Fig 1. Oct 16, 2023 · It also helps developers deliver high-performance inference across cloud, on-premise, and edge devices. Start by creating a new Conda environment and activating it: 1 2. You can also learn to fine-tune LLMs using the TPUs by following the tutorial Fine-Tune and Run Inference on Google's Gemma Model Using TPUs. In general, it can achieve the best performance but it is also the most resource-intensive and time consuming: it requires most GPU resources and takes the longest. xg oc ed ua yx ug qb do ns uk
