1 and 4. Access and share datasets for computer vision, audio, and NLP tasks. To log in, you must first create a Hugging Face account and acquire a User Access Token from the Settings page. A short string representing the path type should be used to specify the topographical cutoff for using. here is a quote from. nvidia/HelpSteer. json. To use Microsoft JARVIS, open this link and paste the OpenAI API key in the first field. The following is a list of the common parameters that should be modified based on your use cases: pretrained_model_name_or_path — Path to pretrained model or model identifier from. + from accelerate import Accelerator + accelerator = Accelerator () + model, optimizer, training_dataloader. If you are unfamiliar with Python virtual environments, take a look at this guide. Instruction formatHashes for nvidia-ml-py3-7. Training. Alternatively, you can insert this code. 847. Step 3: Load and Use Hugging Face Models. This is equivalent to huggingface_hub. If you previously logged in with huggingface-cli login on your system the. HuggingFace. Create a new model. huggingface. 1. To extract image features with this model, follow the timm feature extraction examples, just change the name of the model you want to use. BLOOM as a Large Language Model (LLM), is trained to continue and complete text from a prompt. 2GB on GPU1 and 24GB on GPU2 (GPU1 needs room for context also hence it needs to load less of the model). Mathematically this is calculated using entropy. Org profile for NVIDIA on Hugging Face, the AI community building the future. 5 billion in a $235-million funding round backed by technology heavyweights, including Salesforce , Alphabet's Google and Nvidia . g. Open-source version control system for Data Science and Machine Learning projects. Fine-tune Llama-2 series models with Deepspeed, Accelerate, and Ray Train TorchTrainer. CPU: AMD. 27,720. json as part of the TrainerArguments class passed into the Trainer. I have 2 machine - one is regular pcie 3090 - 2 x cards in nvlink - works good and nvlink shows activity via : nvidia-smi nvlink -gt r. We’re on a journey to advance and democratize artificial intelligence through open source and open science. In a nutshell, it changes the process above like this: Create an. training/evaluation) built upon the Huggingface PyTorch transformer (HuggingFace,2019). 1 - openpose Version. As the model needs 352GB in bf16 (bfloat16) weights ( 176*2 ), the most efficient set-up is 8x80GB A100 GPUs. ai Hugging Face Keras LightGBM MMCV Optuna PyTorch PyTorch Lightning Scikit-learn TensorFlow XGBoost Ultralytics YOLO v8. The abstract from the paper is the following: Transfer learning, where a model is first pre-trained on a data. Example code for Bert. Hugging Face is a community and data science platform that provides: Tools that enable users to build, train and deploy ML models based on open source (OS) code and technologies. Download the models and . With 2xP40 on R720, i can infer WizardCoder 15B with HuggingFace accelerate floatpoint in 3-6 t/s. Running on cpu upgrade2️⃣ Followed by a few practical examples illustrating how to introduce context into the conversation via a few-shot learning approach, using Langchain and HuggingFace. list_metrics()) e. py. Chapters 1 to 4 provide an introduction to the main concepts of the 🤗 Transformers library. 0 / transformers==4. In a nutshell, it changes the process above like this: Create an. Boolean value. The Hugging Face Hub is a platform (centralized web service) for hosting: [14] Git -based code repositories, including discussions and pull requests for projects. Text Classification • Updated May 6, 2022 • 1. I simply want to login to Huggingface HUB using an access token. py. Phind-CodeLlama-34B-v2. The TL;DR. But you need to choose the ExLlama loader, not Transformers. To simplify things, we will use a one-click installer for Text-Generation-WebUI (the program used to load Llama 2 with GUI). 8% pass@1 on HumanEval. Reload to refresh your session. This needs transformers and accelerate installed. 0. Designed for efficient scalability—whether in the cloud or in your data center. Along the way, you'll learn how to use the Hugging Face ecosystem — 🤗 Transformers, 🤗 Datasets, 🤗 Tokenizers, and 🤗 Accelerate — as well as. You switched accounts on another tab or window. 6 GB/s bandwidth. It works by downloading the weights (PT), converting them locally, and uploading. If Git support is enabled, then entry_point and source_dir should be relative paths in the Git repo if provided. Get started. 每个节点 8 张 GPU，4 条 NVLink 卡间互联，4 条 OmniPath 链路 ; CPU: AMD EPYC 7543 32 核处理器 ; CPU 内存: 每个节点 512GB ; GPU 显存: 每个节点 640GB ; 节点间连接: 使用 Omni-Path Architecture (OPA) 网卡，网络拓扑为无阻塞胖树 ; NCCL - 通信网络: 一个完全专用的子网 2017-12-21 by Tim Dettmers 91 Comments. 🤗 Transformers pipelines support a wide range of NLP tasks that you can easily use on. Accelerate is just a wrapper around PyTorch distributed, it's not doing anything different behind the scenes. Model Details. With its 860M UNet and 123M text encoder, the. here is a quote from Nvidia Ampere GA102 GPU Architecture: to get started Model Parallelism Parallelism overview In the modern machine learning the various approaches to parallelism are used to: fit very large models onto limited hardware - e. 🤗 PEFT is tested on Python 3. Model checkpoints will soon be available through HuggingFace and NGC, or for use through the service, including: T5: 3BHardware: 2x TITAN RTX 24GB each + NVlink with 2 NVLinks (NV2 in nvidia-smi topo -m) Software: pytorch-1. Now that your environment is set up, you can load and utilize Hugging Face models within your code. 6 GB/s bandwidth. After that, click on “Submit”. For current SOTA models which have about a hundred layers (e. "<cat-toy>". This improves communication efficiency and can lead to substantial training speed up especially when a computer lacks a faster interconnect such as NVLink. And all of this to just move the model on one (or several) GPU (s) at step 4. nvidia-smi topo - m / nvidia-smi nvlink -s. This repo contains the content that's used to create the Hugging Face course. Echelon ClustersLarge scale GPU clusters designed for AI. it's usable. We fine-tuned StarCoderBase. DataParallel (model, device_ids= [0,1]) The Huggingface docs on training with multiple GPUs are not really clear to me and don't have an example of using the Trainer. 2. The. url (str) — The path to the file to be downloaded. For local datasets: if path is a local directory (containing data files only) -> load a generic dataset builder (csv, json,. Here DP is ~10% slower than DDP w/ NVlink, but ~15% faster than DDP w/o NVlink. Example of model without license: huggingface_hub package exposes a logging utility to control the logging level of the package itself. env. That is TP size <= gpus per node. The course teaches you about applying Transformers to various tasks in natural language processing and beyond. intra-node: NVLink; inter-node: Infiniband / Intel OPA; Software: Data Parallel / Distributed Data Parallel; fp16 (autocast caching) Bigger Models Hardware: bigger GPUs; more GPUs; more CPU and NVMe (offloaded. No problem. To create a new repository, visit huggingface. eval() with torch. It's more technical than that, so if you want details on how it works, I suggest reading up on NVlink. txt> is a text file with one class name per line. These updates–which include two trailblazing techniques and a hyperparameter tool to optimize and scale training of LLMs on any number of GPUs–offer new capabilities to. At a high level, you can spawn 2 CPU processes, 1 for each GPU, and create a NCCL Process Group to have fast data transfer between the 2 GPUs. How would I send data to GPU with and without pipeline? Any advise is highly appreciated. english-gpt2 = your downloaded model name. Fine-tune vicuna-13b with PyTorch Lightning and DeepSpeed. If nvlink connections are utilized, usage should go up during training. Maybe look into the Upstage 30b Llama model which ranks higher than Llama 2 70b on the leaderboard and you should be able to run it on one 3090, I can run it on my M1 Max 64GB very fast. MPT-7B was trained on the MosaicML platform in 9. While the bulk of the semantic composition is done by the latent diffusion model, we can improve local, high-frequency details in generated images by improving the quality of the autoencoder. Our youtube channel features tuto. Hyperplane ServerNVIDIA Tensor Core GPU server with up to 8x A100 or H100 GPUs, NVLink, NVSwitch, and InfiniBand. "NVLink Usage Counters" section in this tutorial shows how to see if data is being transferred. GPT-2 is an example of a causal language model. Accelerate. Stable Diffusion XL. NVlink. It's 4. gz; Algorithm Hash digest; SHA256: 390f02919ee9d73fe63a98c73101061a6b37fa694a793abf56673320f1f51277: Copy : MD5Specifically, Microsoft announced new NC H100 v5 virtual machines for Azure, the industry’s first cloud instances featuring a pair of PCIe-based H100 GPUs connected via Nvidia NVLink, with. Module object from nn. Text Generation Inference (TGI) is a toolkit for deploying and serving Large Language Models (LLMs). upload_file directly uploads files to a repository on the Hub. 🤗 Accelerate was created for PyTorch users who like to write the training loop of PyTorch models but are reluctant to write and maintain the boilerplate code needed to use multi-GPUs/TPU/fp16. The current NLP models are humungous, OpenAI's GPT-3 needs approximately 200-300 gigs of gpu ram to be trained on GPUs. Step 3. g. We have been noticing some odd behavior when trying to configure one of our servers (running CentOS 7) for NV-Link using two GV100 GPUs. Reload to refresh your session. py. GPUs: 288 A100 80GB GPUs with 8 GPUs per node (36 nodes) using NVLink 4 inter-gpu connects, 4 OmniPath links; Communication: NCCL-communications network with a fully dedicated subnet; Software Orchestration: Megatron-DeepSpeed; Optimizer & parallelism: DeepSpeed; Neural networks: PyTorch (pytorch-1. Open-source version control system for Data Science and Machine Learning projects. deepspeed_config. The main advantage of doing this for big models is that during step 2 of the workflow shown above, each shard of the checkpoint is loaded after the previous one, capping the memory usage in RAM to the model size plus the size of the biggest shard. PathLike) — This can be either:. Figure 1. HuggingFaceH4 about 8 hours ago. If you prefer, you can also install it with conda. Inference. Hardware. Revving Up Transformer Engine. Yes you can split it over the two GPUs. We’re on a journey to advance and democratize artificial intelligence through open source and open science. This guide will show you how to: Finetune DistilGPT2 on the r/askscience subset of the ELI5 dataset. Third-Generation NVLink® GA102 GPUs utilize NVIDIA’s third-generation NVLink interface, which includes four x4 links, with each link providing 14. Please check the inference pricing page, especially before vectorizing large amounts of data. Parameters . As far as I have experienced, if you save it (huggingface-gpt-2 model, it is not on cache but on disk. 27,720. Follow these steps: Load a Pre-trained Model: Visit. pkl 3. Hugging Face is especially important because of the " we have no moat " vibe of AI. You might also want to provide a method for creating model repositories and uploading files to the Hub directly from your library. 3 GB/s. Transformers by HuggingFace is an all-encompassing library with state-of-the-art pre-trained models and easy-to-use tools. Fine-tune Llama-2 series models with Deepspeed, Accelerate, and Ray Train TorchTrainer. Firstly, you need to login with huggingface-cli login (you can create or find your token at settings). I think it was puegot systems that did a test and found that the NVlink allows a scaling factor of . Here are some key points to consider: Use vLLM when maximum speed is required for batched prompt delivery. As seen below, I created an. Designed for efficient scalability—whether in the cloud or in your data center. to get started Model Parallelism Parallelism overview In the modern machine learning the various approaches to parallelism are used to: fit very large models onto limited. TP is almost always used within a single node. 1 (note the difference in ETA is just because 3. The huggingface_hub library offers two ways to. An extensive package providing APIs and user. Understand the license of the models you plan to use and verify that license allows your use case. Valid model ids can be located at the root-level, like bert-base-uncased, or namespaced under a user or organization name, like dbmdz/bert-base-german-cased. exceptions. Shows available performance counters on present cards. It makes drawing easier. : Users who want to train massive models of billions of parameters efficiently across multiple GPUs and machines. the GLUE metric has a configuration for each subset) process_id (int, optional) — for distributed evaluation: id of the processInstall the huggingface-cli and run huggingface-cli login - this will prompt you to enter your token and set it at the right path. From the website. Specify the license. StableDiffusionUpscalePipeline can be used to enhance the resolution of input images by a factor of 4. Usage (HuggingFace Transformers) Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. Model Description Nous-Yarn-Llama-2-13b-128k is a state-of-the-art language model for long context, further pretrained on long context data for 600 steps. The split argument can actually be used to control extensively the generated dataset split. Good to hear there's still hope. Model checkpoints will soon be available through HuggingFace and NGC, or for use through the service, including: T5: 3B Hardware: 2x TITAN RTX 24GB each + NVlink with 2 NVLinks (NV2 in nvidia-smi topo -m) Software: pytorch-1. bin] and install fasttext package. The segments_info contains more information about the individual segments of the map (such as their class / category ID). 1 The Mistral-7B-Instruct-v0. models, also with Git-based version control; datasets, mainly in text, images, and audio; web applications ("spaces" and "widgets"), intended for small-scale demos of machine learning. A place where a broad community of data scientists, researchers, and ML engineers can come together and share ideas, get support and. License: Non-commercial license. Four links provide 56. 1 kB Fix tokenizer for transformers 0. tail-recursion. We’re on a journey to advance and democratize artificial intelligence through open source and open science. Addressing Challenge 2 . Drag and drop an image into controlnet, select IP-Adapter, and use the "ip-adapter-plus-face_sd15" file that you downloaded as the model. AI startup has raised $235 million in a Series D funding round, as first reported by The Information, then seemingly verified by Salesforce CEO Marc Benioff on X (formerly known as Twitter). The addition is on-the-fly, the merging is not required. The real difference will depend on how much data each GPU needs to sync with the others - the more there is to sync, the more a slow link will slow down the total runtime. g. It provides information for anyone considering using the model or who is affected by the model. However, for this installer to work, you need to download the Visual Studio 2019 Build Tool and install the necessary resources. GPUs: 64 A100 80GB GPUs with 8 GPUs per node (8 nodes) using NVLink 4 inter-gpu connects, 4 OmniPath links. Text-to-Image. CPU: AMD. I am observing that when I train the exact same model (6 layers, ~82M parameters) with exactly the same data and TrainingArguments, training on a single GPU training. The degree of TP may also make a difference. 352. So if normally your python packages get installed into: ~ /anaconda3/ envs /main/ lib /python3. NVLink is a high speed interconnect between GPUs. Download a PDF of the paper titled HuggingFace's Transformers: State-of-the-art Natural Language Processing, by Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and R'emi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and. when comms are slow then the gpus idle a lot - slow results. GPUs: 416 A100 80GB GPUs (52 nodes) - using 384 gpus (48 nodes) and keeping 32 gpus (4 nodes) in reserve. load_dataset () command and give it the short name of the dataset you would like to load as listed above or on the Hub. 概要. The documentation is organized in five parts: GET STARTED contains a quick tour, the installation instructions and some useful information about our philosophy and a glossary. cpp, you can do the following, using Zephyr as an example model: Get the weights from the hub. get_execution. inception_resnet_v2. 8+. Llama 2 is a family of state-of-the-art open-access large language models released by Meta today, and we’re excited to fully support the launch with comprehensive integration in Hugging Face. 0) — this is another confounding factor. The Megatron 530B model is one of the world’s largest LLMs, with 530 billion parameters based on the GPT-3 architecture. Discover pre-trained models and datasets for your projects or play with the thousands of machine learning apps hosted on the Hub. The huggingface_hub library allows you to interact with the Hugging Face Hub, a platform democratizing open-source Machine Learning for creators and collaborators. Upload the new model to the Hub. co/settings/token) with this command: Cmd/Ctrl+Shift+P to open VSCode command palette. co', port=443): Read timed out. 4 kB Add index 5 months ago; quantization. All the datasets currently available on the Hub can be listed using datasets. , Aug. . Interested in fine-tuning on your own custom datasets but unsure how to get going? I just added a tutorial to the docs with several examples that each walk you through downloading a dataset, preprocessing & tokenizing, and training with either Trainer, native PyTorch, or native TensorFlow 2. We are collaborating with HuggingFace, and a more powerful adapter is in the works. Model Card: Nous-Yarn-Llama-2-13b-128k Preprint (arXiv) GitHub. For commercial requests, please contact us at radrabha. You can connect two cards at once and you will get 90-100% improvement in things like Blender but games (even older ones) will be 0% and you can't do VRAM pooling (so no more cheap 48GB VRAM through 2x 3090 if. I don't think the NVLink this is an option, and I'd love to hear your experience and plan on sharing mine as well. 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. . Controlnet v1. Hi, You can just add as many files as you’d like. feature. Here is the full benchmark code and outputs: Here DP is ~10% slower than DDP w/ NVlink, but ~15% faster than DDP w/o NVlink. 0 which would limit bandwidth to like 16GB/s on 2x x8 port. t5-11b is 45GB in just model params significantly speed up training - finish training that would take a year in hours Each new generation provides a faster bandwidth, e. The real difference will depend on how much data each GPU needs to sync with the others - the more there is to sync, the more a slow link will slow down the total runtime. Depending on your needs and settings, you can fine-tune the model with 10GB to 16GB GPU. Causal language modeling predicts the next token in a sequence of tokens, and the model can only attend to tokens on the left. Downloading models Integrated libraries. State-of-the-art computer vision models, layers, optimizers, training/evaluation, and utilities. The hub works as a central place where users can explore, experiment, collaborate, and. Advanced. Accelerate, DeepSpeed. High performance multi-GPU computing becomes an inevitable trend due to the ever-increasing demand on computation capability in emerging domains such as deep learning, big data and planet-scale simulations. Inter-node connect: Omni-Path Architecture (OPA) NCCL-communications network: a fully dedicated subnet. As of 2023-02-22, there are 8 different models and 3 optional experimental t2iadapter models:. HuggingFace includes a caching mechanism. This should be quite easy on Windows 10 using relative path. Originally launched as a chatbot app for teenagers in 2017, Hugging Face evolved over the years to be a place where you can host your own. Hyperplane ServerNVIDIA Tensor Core GPU server with up to 8x A100 or H100 GPUs, NVLink, NVSwitch, and InfiniBand. All the open source things related to the Hugging Face Hub. 3. training high-resolution image classification models on tens of millions of images using 20-100. one-line dataloaders for many public datasets: one-liners to download and pre-process any of the major public datasets (image datasets, audio. Q4_K_M. Most of the tokenizers are available in two flavors: a full python implementation and a “Fast” implementation based on the Rust library tokenizers. Based on the individual link speed (~25 GB/s) it appears we are. At least consider if the cost of the extra GPUs and the running cost of electricity is worth it compared to renting 48. models, also with Git-based version control; datasets, mainly in text, images, and audio; web applications ("spaces" and "widgets"), intended for small-scale demos of machine learning. Task Guides. 5 with huggingface token in 3rd cell, then your code download the original model from huggingface as well as the vae and combone them and make ckpt from it. Parameters . When you have fast intranode connectivity like NVLink as compared to PCIe usually the comms overhead is lower and then compute dominates and gpus excel at what they do - fast results. We’re on a journey to advance and democratize artificial intelligence through open source and open science. cc:63 NCCL WARN Failed to open libibverbs. ; library_version (str, optional) — The version of the library. Data- parallel fine-tuning using HuggingFace Trainer; MP: Model- parallel fine-tuning using Huggingface Trainer; MP+TP: Model- and data- parallel fine-tuning using open-source libraries; CentML: A mixture of parallelization and optimization strategies devised by. This extension is for AUTOMATIC1111's Stable Diffusion web UI, allows the Web UI to add ControlNet to the original Stable Diffusion model to generate images. The huggingface_hub library provides a Python interface to create, share, and update Model Cards. Sequential into the Huggingface PreTrainedModel object, then run something like: import torch. The Nvidia system provides 32 petaflops of FP8 performance. "NVLink Usage Counters" section in this tutorial shows how to see if data is being transferred across nvlink. If you want to run chat-ui with llama. Final thoughts :78244:78244 [0] NCCL INFO NET/Plugin : No plugin found (libnccl-net. If you add this to your collator,. This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model. Accelerate is a HuggingFace library that simplifies PyTorch code adaptation for. Used only when HF_HOME is not set!. I have to actually demo PyTorch, so I’ll see if I. GPUs, storage, and InfiniBand networking. Uses. ; sort (Literal["lastModified"] or str, optional) — The key with which to. This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of. Choose your model on the Hugging Face Hub, and, in order of precedence, you can either: Set the LLM_NVIM_MODEL environment variable. The ControlNet learns task-specific conditions in an end-to-end way, and the learning is robust even when the training dataset is small (< 50k). model',local_files_only=True) Please note the 'dot' in. here is a quote from Nvidia Ampere GA102 GPU Architecture: Third-Generation NVLink® GA102 GPUs utilize NVIDIA’s third-generation NVLink interface, which includes four x4 links,HuggingFace Diffusers library,12 were launched, queried, and benchmarked on a PowerEdge XE9680 server. Parameters . -2. Synopsis: This is to demonstrate and articulate how easy it is to deal with your NLP datasets using the Hugginfaces Datasets Library than the old traditional complex ways. Programmatic access. Includes 3rd generation NVLink for fast multi-GPU training. co/new: Specify the owner of the repository: this can be either you or any of the organizations you’re affiliated with. Framework. ; library_name (str, optional) — The name of the library to which the object corresponds. The old ones: RTX 3090: 936. dev0 DataLoader One of the important requirements to reach great training speed is the ability to feed the GPU at the maximum speed it can handle. Hugging Face datasets supports loading from Spark DataFrames using datasets. GPUs: 128 A100 80GB GPUs with 8 GPUs per node (16 nodes) using NVLink 4 inter-gpu connects, 4 OmniPath links; Communication: NCCL-communications network with a fully dedicated subnet; Software. It is, to the best of our knowledge, the largest dense autoregressive model that has publicly available weights at the time of. Dual 3090 with NVLink is the most bang per buck, $700 per card. I suppose the problem is related to the data not being sent to GPU. Echelon ClustersLarge scale GPU clusters designed for AI. This article shows how to get an incredibly fast per token throughput when generating with the 176B parameter BLOOM model. Much of the cutting-edge work in AI revolves around LLMs like Megatron 530B. . For example, if you want have a complete experience for Inference, run:Create a new model. This article will break down how it works and what it means for the future of graphics. Accelerate, DeepSpeed. Note that this filename is explicitly set to. A full training run takes ~1 hour on one V100 GPU. split='train[:100]+validation[:100]' will create a split from the first 100. It is open source, available for commercial use, and matches the quality of LLaMA-7B. Let’s load the SQuAD dataset for Question Answering. 8+. 学習済 LLM (大規模言語モデル)のパラメータ数と食うメモリ容量（予想含む）、ホストできるGPUを調べたメモ ※適宜修正、拡充していく。. Run inference with pipelines Write portable code with AutoClass Preprocess data Fine-tune a pretrained model Train with a script Set up distributed training with 🤗 Accelerate Load and train adapters with 🤗 PEFT Share your model Agents Generation with LLMs. You signed in with another tab or window. To allow the container to use 1G of Shared Memory and support SHM sharing, we add --shm-size 1g on the above command. with_transform () function which will do transformation. 0 49 549 124 (1 issue needs help) 2 Updated 2 days ago. 2 2 Dataset The dataset is extracted from comment chains scraped from Reddit spanning from 2005 till 2017. Vicuna is a chat assistant trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT. That is not what the OP is looking for as it will remove all libraries and does not clear the default cache. 1. 3. 24xlarge When to use it: When you need all the performance you can get. I use the "20,22" memory split so that the display card has some room for the framebuffer to handle display. I added the parameter resume_download=True (to begin downloading from where it stops) and increased the. from huggingface_hub import logging. from_pretrained ('. Progress doesn't advance and counter stuck like this 18678/18684 [1:49:48<00:02, 2. /server -m models/zephyr-7b-beta. Hub documentation. Table 2. Hardware: 2x TITAN RTX 24GB each + NVlink with 2 NVLinks (NV2 in nvidia-smi topo -m) Software: pytorch-1. As the model needs 352GB in bf16 (bfloat16) weights ( 176*2 ), the most efficient set-up is 8x80GB A100 GPUs. Finetuned from model: LLaMA. Accuracy results for zero-, one-, and few-shot evaluations using MT-NLG. The huggingface_hub library offers two ways to assist you with creating repositories and uploading files: create_repo creates a repository on the Hub. GPU-ready Dockerfile to run Stability.