Package: unitorch
Documentation•Installation Instructions•Reporting IssuesIntroduction🔥 unitorch is a library that simplifies and accelerates the development of unified models for natural language understanding, natural language generation, computer vision, click-through rate prediction, multimodal learning and reinforcement learning. It is built on top of PyTorch and integrates seamlessly with popular frameworks such as transformers, peft, diffusers, and fastseq. With unitorch, you can use a single command line tool or a one-line codeimport unitorchimport to leverage the state-of-the-art models and datasets without sacrificing performance or accuracy.What's New ModelSDXLreleased with the paperSDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesisby Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, Robin Rombach.MiniGPT-4released with the paperMiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Modelsby Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, Mohamed Elhoseiny.LLaMAreleased with the paperLLaMA: Open and Efficient Foundation Language Modelsby Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, Guillaume Lample.ControlNetreleased with the paperAdding Conditional Control to Text-to-Image Diffusion Modelsby Lvmin Zhang, Anyi Rao, Maneesh Agrawala.BLOOMreleased with the paperBLOOM: A 176B-Parameter Open-Access Multilingual Language Modelby BigScience Workshop: Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilić, Daniel Hesslow...DreamBoothreleased with the paperDreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationby Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, Kfir Aberman.PEGASUS-Xreleased with the paperInvestigating Efficiently Extending Transformers for Long Input Summarizationby Jason Phang, Yao Zhao, Peter J. Liu.BLIPreleased with the paperBLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationby Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi.BEiTreleased with the paperBEiT: BERT Pre-Training of Image Transformersby Hangbo Bao, Li Dong, Songhao Piao, Furu Wei.Swin Transformerreleased with the paperSwin Transformer: Hierarchical Vision Transformer using Shifted Windowsby Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.CLIPreleased with the paperLearning Transferable Visual Models From Natural Language Supervisionby Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever.mT5released with the papermT5: A massively multilingual pre-trained text-to-text transformerby Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.Vision Transformer (ViT)released with the paperAn Image is Worth 16x16 Words: Transformers for Image Recognition at Scaleby Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.DeBERTa-V2released with the paperDeBERTa: Decoding-enhanced BERT with Disentangled Attentionby Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.DeBERTareleased with the paperDeBERTa: Decoding-enhanced BERT with Disentangled Attentionby Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.MBartreleased with the paperMultilingual Denoising Pre-training for Neural Machine Translationby Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.PEGASUSreleased with the paperPEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationby Jingqing Zhang, Yao Zhao, Mohammad Saleh, Peter J. Liu.BARTreleased with the paperBART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensionby Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.T5released with the paperExploring the Limits of Transfer Learning with a Unified Text-to-Text Transformerby Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu.VisualBERTreleased with the paperVisualBERT: A Simple and Performant Baseline for Vision and Languageby Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.RoBERTareleased together with the paperRoBERTa: A Robustly Optimized BERT Pretraining Approachby Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.BERTreleased with the paperBERT: Pre-training of Deep Bidirectional Transformers for Language Understandingby Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.FeaturesUser-Friendly Python PackageFaster & Streamlined Train/InferenceDeepspeed Integration for Large-Scale ModelsCUDA OptimizationExtensive STOA Model & Task SupportsInstallationpip3installunitorchQuick ExamplesSource Codeimportunitorch# import bart modelfromunitorch.models.bartimportBartForGenerationmodel=BartForGeneration("path/to/bart/config.json")# use the configuration classfromunitorch.cliimportCoreConfigureParserconfig=CoreConfigureParser("path/to/config.ini")Multi-GPU Trainingtorchrun--no_python--nproc_per_node4\unitorch-trainexamples/configs/generation/bart.ini\--train_filepath/to/train.tsv--dev_filepath/to/dev.tsvSingle-GPU Inferenceunitorch-inferexamples/configs/generation/bart.ini--test_filepath/to/test.tsvFind more details in the Tutorials section of thedocumentation.LicenseCode released under MIT license.