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. 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. Hardware: 2x TITAN RTX 24GB each + NVlink with 2 NVLinks (NV2 in nvidia-smi topo -m) Software: pytorch-1. This will also be the name of the repository. Model Card: Nous-Yarn-Llama-2-13b-128k Preprint (arXiv) GitHub. py. Also 2x8x40GB A100s or 2x8x48GB A6000 can be used. co. ; Scalar ServerPCIe server with up to 8x customizable NVIDIA Tensor Core GPUs and dual Xeon or AMD EPYC. 1] 78244:78244 [0] NCCL INFO Using network Socket NCCL version 2. For commercial requests, please contact us at radrabha. New (beta)! Try our experimental Model Card Creator App. Stability AI release Stable Doodle, a groundbreaking sketch-to-image tool based on T2I-Adapter and SDXL. upload_file directly uploads files to a repository on the Hub. Includes 3rd generation NVLink for fast multi-GPU training. g. • Full NVLINK interconnectivity Support for up to 16 Drives • Up to 8 x SAS/SATA/NVMe Gen4 or 16x E3. Framework. 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. See this simple code example - how would you change it to take advantage of NVLink? DistributedDataParallel via NCCL would use NVLink, if available. The most common and practical way to control which GPU to use is to set the CUDA_VISIBLE_DEVICES environment variable. I am using the pytorch back-end. Jul. nvidia-smi nvlink -h. Based on the latest NVIDIA Ampere architecture. PyTorch transformer (HuggingFace,2019). 8-to-be + cuda-11. 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. To use Microsoft JARVIS, open this link and paste the OpenAI API key in the first field. 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 library contains tokenizers for all the models. Native support for models from HuggingFace — Easily run your own model or use any of the HuggingFace Model Hub. 847. For a quick performance test, I would recommend to run the nccl-tests and also verify the connections between the GPUs via nvidia-smi topo -m. The TL;DR. From the website. HuggingFaceH4 about 8 hours ago. This model can be easily used and deployed using HuggingFace's ecosystem. 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. On OpenLLM Leaderboard in HuggingFace, Falcon is the top 1, suppressing META’s LLaMA-65B. It acts as a hub for AI experts and enthusiasts—like a GitHub for AI. Hi, You can just add as many files as you’d like. . You can also create and share your own models. Note that. This repository contains code for training, finetuning, evaluating, and deploying LLMs for inference with Composer and the MosaicML platform. It can be used in combination with Stable Diffusion, such as runwayml/stable-diffusion-v1-5. I have not found any information with regards to the 3090 NVLink memory pooling. map () function from 🤗 Huggingface, but in this case it would be slow and time consuming. 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. These models can be used to generate and modify images based on text prompts. Cache management. Additionally you want the high-end PSU that has stable. . Combined with Transformer Engine and fourth-generation NVLink, Hopper Tensor Cores enable an order-of-magnitude speedup for HPC and AI workloads. To keep up. 🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch. 🤗 Transformers can be installed using conda as follows: conda install-c huggingface transformers. It will soon be available to developers through the early access program on the NVIDIA NeMo LLM service. This tutorial is based on a forked version of Dreambooth implementation by HuggingFace. txt> is a text file with one class name per line. Tokenizer. You might also want to provide a method for creating model repositories and uploading files to the Hub directly from your library. a metric identifier on the HuggingFace datasets repo (list all available metrics with datasets. 3. . Different from BERT and encoder-decoder structure, GPT receive some input ids as context, and generates the respective output ids as response. NVLink. english-gpt2 = your downloaded model name. 352. The chart below shows the growth of model size in recent years, a trend. CPU: AMD. CPU: AMD. 🤗 Transformers Quick tour Installation. co Join the Hugging Face community and get access to the augmented documentation experience Collaborate on models, datasets and Spaces Faster examples with accelerated inference Switch between documentation themes to get started Performance and Scalability Training large transformer models and deploying them to production present various challenges. Programmatic access. huggingface import HuggingFaceModel import sagemaker role = sagemaker. Mistral-7B-v0. huggingface. . It is addressed via choosing SHARDED_STATE_DICT state dict type when creating FSDP config. @inproceedings{du2022glm, title={GLM: General Language Model Pretraining with Autoregressive Blank Infilling}, author={Du, Zhengxiao and Qian, Yujie and Liu, Xiao and Ding, Ming and Qiu, Jiezhong and Yang, Zhilin and Tang, Jie}, booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational. Image Synthesis: Transforming Words into Visuals. 0625 GB/sec bandwidth in each direction between two GPUs. 45. Hardware. Some environment variables are not specific to huggingface_hub but are still taken into account when they are set. 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. With a single-pane view that offers an intuitive user interface and integrated reporting, Base Command Platform manages the end-to-end lifecycle of AI development, including workload management. inception_resnet_v2. DeepSpeed features can be enabled, disabled, or configured using a config JSON file that should be specified as args. pretrained_model_name (str or os. It is open source, available for commercial use, and matches the quality of LLaMA-7B. nvidia-smi nvlink. + from accelerate import Accelerator + accelerator = Accelerator () + model, optimizer, training_dataloader. PyTorch transformer (HuggingFace,2019). Echelon ClustersLarge scale GPU clusters designed for AI. This is the most common setup for researchers and small-scale industry workflows. 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links. 2. Liu. 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. Addressing Challenge 2 . ConnectionError: HTTPSConnectionPool (host='cdn-lfs. Each new generation provides a faster bandwidth, e. model. It is useful if you have a GPU cluster with. Lightning, DeepSpeed. If it supports memory pooling, I might be interested to buy another 3090 with an NVLink adapter as it would allow me to fit larger models in memory. 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. For the base model, this is controlled by the denoising_end parameter and for the refiner model, it is controlled by the denoising_start parameter. However, one can also add multiple embedding vectors for the placeholder token to increase the number of fine-tuneable parameters. . Technically, yes: there is a single NVLink connector on both the RTX 2080 and 2080 Ti cards (compared to two on the Quadro GP100 and GV100). The. Get information from all datasets in the Hub. 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. py. Since Transformers version v4. Thus in essence. ; a. 2GB on GPU1 and 24GB on GPU2 (GPU1 needs room for context also hence it needs to load less of the model). vocab_size (int, optional, defaults to 50257) — Vocabulary size of the GPT-2 model. Hugging Face is more than an emoji: it's an open source data science and machine learning platform. Hyperplane ServerNVIDIA Tensor Core GPU server with up to 8x A100 or H100 GPUs, NVLink, NVSwitch, and InfiniBand. 5B tokens high-quality programming-related data, achieving 73. In this blog post, we'll explain how Accelerate leverages PyTorch features to load and run inference with very large models, even if they don't fit in RAM or one GPU. This is a good setup for large-scale industry workflows, e. Why, using Huggingface Trainer, single GPU training is faster than 2 GPUs? Ask Question Asked 1 year, 8 months ago Modified 1 year, 8 months ago Viewed 2k. A day after Salesforce CEO Marc Benioff jumped the gun with a post on X saying the company’s venture arm was “thrilled to lead” a new round of financing, Hugging Face has. Hardware: 2x TITAN RTX 24GB each + NVlink with 2 NVLinks (NV2 in nvidia-smi topo -m) Software: pytorch-1. 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. . Step 3: Load and Use Hugging Face Models. The easiest way to scan your HF cache-system is to use the scan-cache command from huggingface-cli tool. The original codebase can be found here:LightningModule. In order to share data between the different devices of a NCCL group, NCCL might fall back to using the host memory if peer-to-peer using NVLink or PCI is not possible. 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). like 6. The online Huggingface Gadio has been updated . json as part of the TrainerArguments class passed into the Trainer. You can use this argument to build a split from only a portion of a split in absolute number of examples or in proportion (e. Progress doesn't advance and counter stuck like this 18678/18684 [1:49:48<00:02, 2. 左半分：LLMのパラメータ数と、必要な GPU メモリ (fp16換算) 右半分：その基盤モデルの推論をするなら、どんなGPU. I simply want to login to Huggingface HUB using an access token. And all of this to just move the model on one (or several) GPU (s) at step 4. We believe in the power of collaboration and community, and we invite you to join us in making this directory a valuable resource for all. Automatically send and retrieve data from Hugging Face. With just one line of code, it provides a simple API that gives up to 6x performance speedup on NVIDIA GPUs. 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. Inference is the process of using a trained model to make predictions on new data. 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. and operational efficiency for training and running state-of-the-art models, from the largest language and multi-modal models to more basic computer vision and NLP models. You want the face controlnet to be applied after the initial image has formed. cache or the content of. When training a style I use "artwork style" as the prompt. In this example, we will be using the HuggingFace inference API to use a model called all-MiniLM-L6-v2. As seen below, I created an. 0. When you create an HuggingFace Estimator, you can specify a training script that is stored in a GitHub repository as the entry point for the estimator, so that you don’t have to download the scripts locally. 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. . Then in the "gpu-split" box enter "17. I have several m/P 40 cards. text2vec-huggingface Overview . If you are. Dataset. training high-resolution image classification models on tens of millions of images using 20-100. Step 3. Transformers by HuggingFace is an all-encompassing library with state-of-the-art pre-trained models and easy-to-use tools. - GitHub - NickLucche/stable-diffusion-nvidia-docker: GPU-ready Dockerfile to run Stability. It is. Unfortunately I discovered that with larger models the GPU-GPU communication overhead can be prohibitive (most of the cluster nodes only support P2P GPU communication over PCIe, which is a lot slower than NVLink), and Huggingface's implementation actually performed worse on multiple GPUs than on two 3090s with NVLink (I opened an issue. Therefore, it is important to not modify the file to avoid having a. 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 AI. Model Details. It is highly recommended to install huggingface_hub in a virtual environment. g. This means the model cannot see future tokens. ; library_version (str, optional) — The version of the library. Framework. Sigmoid() ). Discover pre-trained models and datasets for your projects or play with the thousands of machine learning apps hosted on the Hub. <unlabeled_data. 5. . Then you can simply wrap your model with DDP and train. from that path you can manually delete. -2. The learning rate is selected based on validation loss. We’re on a journey to advance and democratize artificial intelligence through open source and open science. g. features["ner_tags"]. 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. . no_grad(): predictions=[] labels=[] for minibatch. 18M • 30. llmfoundry/ - source code for models, datasets. Please use the forums for questions like this as we keep issues for bugs and feature requests only. Unfortunately I discovered that with larger models the GPU-GPU communication overhead can be prohibitive (most of the cluster nodes only support P2P GPU communication over PCIe, which is a lot slower than NVLink), and Huggingface's implementation actually performed worse on multiple GPUs than on two 3090s with NVLink (I opened an issue track it. ac. 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. Download and save a repo with: htool save-repo <repo_id> <save_dir> -r <model/dataset>. Documentations. • 4 mo. 0. 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). Finetuned from model: LLaMA. AI stable-diffusion model v2 with a simple web interface. Usage. Designed for efficient scalability—whether in the cloud or in your data center. from huggingface_hub import logging. The fine-tuning script is based on this Colab notebook from Huggingface's blog: The Falcon has landed in the Hugging Face ecosystem. Fine-tune GPT-J-6B with Ray Train and DeepSpeed. Then we load the dataset like this: from datasets import load_dataset dataset = load_dataset("wikiann", "bn") And finally inspect the label names: label_names = dataset["train"]. If you are running text-generation-inference. 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. First, by keeping just one (or a few) model layers in GPU memory at any time, ZeRO-Inference significantly reduces the amount of GPU memory required to inference massive models. nn as nn from transformers. ) If you look at this, you'll see that their collator uses the return_tensors="tf" argument. ChatGLM2-6B 开源模型旨在与开源社区一起推动大模型技术发展，恳请开发者和大家遵守开源协议. huggingface_hub is tested on Python 3. 🐸. Load the dataset from the Hub. There are eight problem types that support incremental training and fine-tuning. It is unclear if NVIDIA will be able to keep its spot as the main deep learning hardware vendor in 2018 and both AMD and Intel Nervana will have a shot at overtaking NVIDIA. 6. This article will break down how it works and what it means for the future of graphics. 0. Example. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. I suppose the problem is related to the data not being sent to GPU. Software Megatron-DeepSpeed (Github link. If you look closely, though, you will see that the connectors. Example code for Bert. To retrieve the new Hugging Face LLM DLC in Amazon SageMaker, we can use the. Inter-node connect: Omni-Path Architecture (OPA). Follow the installation pages of TensorFlow, PyTorch or Flax to see how to install them with conda. Use it for distributed training on large models and datasets. Its usage may incur costs. 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links ; CPU: AMD EPYC 7543 32-Core. Reply replyDistilBERT (from HuggingFace), released together with the paper DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter by Victor Sanh, Lysandre Debut and Thomas Wolf. 0. The NVlink was designed specifically to let multiple GPUs pool their resources. 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. The T5 model was presented in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Control over model inference: The framework offers a wide range of options to manage model inference, including precision adjustment, quantization, tensor parallelism, repetition penalty, and more. NVLink and NVSwitch for NVIDIA Ampere architecture provide extra 600GB/s GPU-to-GPU. here is a quote from Nvidia Ampere GA102 GPU Architecture: Third-Generation NVLink® GA102 GPUs utilize NVIDIA’s third. See full list on huggingface. I’ve decided to use the Huggingface Pipeline since I had experience with it. 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. 3. it's usable. In this article. This repo holds the files that go into that build. The goal is to convert the Pytorch nn. Text-to-Image. 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. com is the world's best emoji reference site, providing up-to-date and well-researched information you can trust. 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. 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links. You will need to create a free account at HuggingFace, then head to settings under your profile. I retrained an instance of sentence-transformers using contrastive loss on an unsupervised data dump and now want to finetune the above model on a labeled, binary dataset. BLOOM as a Large Language Model (LLM), is trained to continue and complete text from a prompt. Lightning provides advanced and optimized model-parallel training strategies to support massive models of billions of parameters. Figure 1. from_pretrained ('. Reload to refresh your session. 0 78244:78465 [0] NCCL INFO Call to connect returned Connection timed. g. This is equivalent to huggingface_hub. But you need to choose the ExLlama loader, not Transformers. Running on t4. ac. We’re on a journey to advance and democratize artificial intelligence through open source and open science. The segments_info contains more information about the individual segments of the map (such as their class / category ID). Code 2. Used only when HF_HOME is not set!. 9 for deep learning. g. Open-source version control system for Data Science and Machine Learning projects. 1 (note the difference in ETA is just because 3. 3. State-of-the-art computer vision models, layers, optimizers, training/evaluation, and utilities. 25 GB/sec bandwidth in each direction, and 112. Task Guides. 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. When you download a dataset, the processing scripts and data are stored locally on your computer. nvidia-smi nvlink. Let me present you a demo which will describe the entire process. 7. Join Hugging Face. def accuracy_accelerate_nlp(network, loader, weights, accelerator): correct = 0 total = 0 network. 0. We’re on a journey to advance and democratize artificial intelligence through open source and open science. Each modelBy Miguel Rebelo · May 23, 2023. In this blog post, we'll walk through the steps to install and use the Hugging Face Unity API. 0 / transformers==4. We’re on a journey to advance and democratize artificial intelligence through. 27,720. You signed out in another tab or window. iiit. So for consumers, I cannot recommend buying. GTO. tail-recursion. 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, with each link providing 14. Other optional arguments include: --teacher_name_or_path (default: roberta-large-mnli): The name or path of the NLI teacher model. Stable Diffusion is a text-to-image latent diffusion model created by the researchers and engineers from CompVis, Stability AI and LAION. Fine-tune GPT-J-6B with Ray Train and DeepSpeed. g. 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. A full training run takes ~1 hour on one V100 GPU. Here is the full benchmark code and outputs: Develop. The abstract from the paper is the following: Transfer learning, where a model is first pre-trained on a data. Get started. Images generated with text prompt = “Portrait of happy dog, close up,” using the HuggingFace Diffusers text-to-image model with batch size = 1, number of iterations = 25, float16 precision, DPM Solver Multistep Scheduler,In order to share data between the different devices of a NCCL group, NCCL might fall back to using the host memory ifpeer-to-peer using NVLink or PCI is not possible. Retrieve the new Hugging Face LLM DLC . 🤗 Accelerate is a library that enables the same PyTorch code to be run across any distributed configuration by adding just four lines of code! In short, training and inference at scale made simple, efficient and adaptable. Accelerate, DeepSpeed. The model can be. run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test. DGX Cloud is powered by Base Command Platform, including workflow management software for AI developers that spans cloud and on-premises resources. Table 2. No problem. Installation Open your Unity project; Go to Window-> Package. huggingface_tool. Hugging Face is especially important because of the " we have no moat " vibe of AI. This checkpoint is a conversion of the original checkpoint into diffusers format. The old ones: RTX 3090: 936. What is NVLink, and is it useful? Generally, NVLink is not useful. Download the Llama 2 Model. Disc IO network: shared network with other types of nodes. {"payload":{"allShortcutsEnabled":false,"fileTree":{"inference/huggingface/zero_inference":{"items":[{"name":"images","path":"inference/huggingface/zero_inference. I want to add certain whitespaces to the tokenizer like line ending ( ) and tab ( ). A virtual. Zero-shot image-to-text generation with BLIP-2 . By Yesha Shastri, AI Developer and Writer on February 16, 2023 in Machine Learning. It's 4. Unlike gradient accumulation (where improving communication efficiency requires increasing the effective batch size), Local SGD does not require changing a batch size or a learning rate. Parameters . 0. Sequential( nn. For a quick performance test, I would recommend to run the nccl-tests and also verify the connections between the GPUs via nvidia-smi topo -m. 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links. 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. 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. g. py. 1 is the successor model of Controlnet v1. (It's set up to not use Tensorflow by default. RTX 4080 16GB: 720 GB/s. Alternatively, you can insert this code. I suppose the problem is related to the data not being sent to GPU. That is not what the OP is looking for as it will remove all libraries and does not clear the default cache. This should be quite easy on Windows 10 using relative path. Along the way, you'll learn how to use the Hugging Face ecosystem — 🤗 Transformers, 🤗 Datasets, 🤗 Tokenizers, and 🤗 Accelerate — as well as. Final thoughts :78244:78244 [0] NCCL INFO NET/Plugin : No plugin found (libnccl-net. Hugging Face Inc. Use the Hub’s Python client libraryOur Intel ® Gaudi ® 2 AI acceleratoris driving improved deep learning price-performance. co', port=443): Read timed out. With 2xP40 on R720, i can infer WizardCoder 15B with HuggingFace accelerate floatpoint in 3-6 t/s. ; This module is available on. , NVLINK or NVSwitch) consider using one of these options: ZeRO - as it requires close to no modifications to the model; A combination of PipelineParallel(PP) with TensorParallel(TP) and DataParallel(DP) - this approach will result in fewer communications, but requires significant changes to the model NVlink. NVlink. The WebUI extension for ControlNet and other injection-based SD controls. Looking directly at the data from NVIDIA, we can find that for CNNs, a system with 8x A100 has a 5% lower overhead than a system of 8x V100. filter (DatasetFilter or str or Iterable, optional) — A string or DatasetFilter which can be used to identify datasets on the hub. 8% pass@1 on HumanEval. LIDA is a library for generating data visualizations and data-faithful infographics. Transformers, DeepSpeed. In Amazon SageMaker Studio, open the JumpStart landing page either through the Home page or the Home menu on the left-side panel. m@research. coI use the stable-diffusion-v1-5 model to render the images using the DDIM Sampler, 30 Steps and 512x512 resolution. split='train[:10%]' will load only the first 10% of the train split) or to mix splits (e. Best to experiment to find the winner on your particular setup. 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. Git-like experience to organize your data, models, and experiments.