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https://huggingface.co/ | The Home of Machine Learning
Create, discover and collaborate on ML better.
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https://huggingface.co/amazon | Hugging Face is working with Amazon Web Services to make it easier than ever for startups and enterprises to train and deploy Hugging Face models in Amazon SageMaker.
To train Hugging Face models in Amazon SageMaker, you can use the Hugging Face Deep Learning Containers (DLCs) and the Hugging Face support in the SageMaker Python SDK.
The DLCs are fully integrated with the SageMaker distributed training libraries to train models more quickly using the latest generation of accelerated computing instances available on Amazon EC2. With the SageMaker Python SDK, you can start training with just a single line of code, enabling your teams to move from idea to production more quickly.
To deploy Hugging Face models in Amazon SageMaker, you can use the Hugging Face Deep Learning Containers with the new Hugging Face Inference Toolkit.
With the new Hugging Face Inference DLCs, deploy your trained models for inference with just one more line of code, or select any of the 10,000+ models publicly available on the 🤗 Hub, and deploy them with Amazon SageMaker, to easily create production-ready endpoints that scale seamlessly, with built-in monitoring and enterprise-level security.
More information: AWS blog post, Community Forum |
https://huggingface.co/google | ALBERT release
The ALBERT release was done in two steps, over 4 checkpoints of different sizes each time. The first version is noted as "v1", the second as "v2". |
https://huggingface.co/Intel | Intel and Hugging Face are building powerful optimization tools to accelerate training and inference with Transformers. |
https://huggingface.co/microsoft | Research interests
None defined yet.
Collections 1
SpeechT5
The SpeechT5 framework consists of a shared seq2seq and six modal-specific (speech/text) pre/post-nets that can address a few audio-related tasks.
SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing
Paper • 2110.07205 • Published Oct 14, 2021 • 1
microsoft/speecht5_tts
Text-to-Speech • Updated Aug 25 • 53.2k • 273
Running ont4
182
👩🎤
SpeechT5 Speech Synthesis Demo
microsoft/speecht5_vc
Audio-to-Audio • Updated Mar 22 • 16.3k • 36
spaces 9
Building ona10g
2.03k
😻
HuggingGPT
Runtime error
719
🎨
Visual Chatgpt
Build error
55
🤖
ChatGPT Robotics
232
🌍
Promptist
Build error
57
🐠
GODEL Demo
Runtime error
17
🏢
Unicl Image Recognition Demo
models 254
microsoft/phi-1
Text Generation • Updated 6 days ago • 7.07k • 91
microsoft/phi-1_5
Text Generation • Updated 6 days ago • 168k • 869
microsoft/cvt-13
Image Classification • Updated 16 days ago • 6.99k • 5
microsoft/prophetnet-large-uncased-squad-qg
Text2Text Generation • Updated 16 days ago • 462 • 6
microsoft/swin-tiny-patch4-window7-224
Image Classification • Updated 18 days ago • 14k • 17
microsoft/tapex-large-sql-execution
Table Question Answering • Updated 18 days ago • 4.45k • 11
microsoft/git-base-vatex
Text Generation • Updated 18 days ago • 1.05k • 1
microsoft/mpnet-base
Fill-Mask • Updated 18 days ago • 140k • 20
microsoft/xclip-base-patch16-zero-shot
Feature Extraction • Updated 21 days ago • 21.2k • 15
microsoft/swin-base-patch4-window7-224
Image Classification • Updated 23 days ago • 8.47k • 3
datasets 5
microsoft/LCC_python
Viewer • Updated Jun 21 • 10 • 1
microsoft/LCC_java
Viewer • Updated Jun 21 • 3 • 1
microsoft/LCC_csharp
Viewer • Updated Jun 21 • 9 • 2
microsoft/CLUES
Viewer • Updated Mar 25, 2022 • 2 • 3
microsoft/codexglue_method_generation
Preview • Updated Oct 28, 2021 • 6 |
https://huggingface.co/grammarly | Research interests
None defined yet.
models 6
grammarly/coedit-large
Text2Text Generation • Updated 14 days ago • 21.1k • 26
grammarly/pseudonymization-seq2seq
Text2Text Generation • Updated Aug 31 • 4
grammarly/coedit-xxl
Text2Text Generation • Updated Aug 19 • 157 • 10
grammarly/coedit-xl-composite
Text2Text Generation • Updated Aug 19 • 19 • 8
grammarly/coedit-xl
Text2Text Generation • Updated Aug 19 • 456 • 4
grammarly/detexd-roberta-base
Text Classification • Updated Jul 10 • 131 • 4
datasets 3
grammarly/pseudonymization-data
Preview • Updated Aug 23 • 9 • 1
grammarly/coedit
Viewer • Updated Aug 19 • 286 • 9
grammarly/detexd-benchmark
Viewer • Updated Jul 10 • 11 • 1 |
https://huggingface.co/Writer | Writer is a generative AI platform focused on advancing AI technology by solving the problems faced by businesses. We are making LLMs accessible to everyone with the availability of our Palmyra LLMs on Hugging Face and our API. You can run these models in your own, secure environment and fine-tune them for your needs while protecting your data.
spaces 3 |
https://huggingface.co/docs/transformers | 🤗 Transformers
State-of-the-art Machine Learning for PyTorch, TensorFlow, and JAX.
🤗 Transformers provides APIs and tools to easily download and train state-of-the-art pretrained models. Using pretrained models can reduce your compute costs, carbon footprint, and save you the time and resources required to train a model from scratch. These models support common tasks in different modalities, such as:
📝 Natural Language Processing: text classification, named entity recognition, question answering, language modeling, summarization, translation, multiple choice, and text generation.
🖼️ Computer Vision: image classification, object detection, and segmentation.
🗣️ Audio: automatic speech recognition and audio classification.
🐙 Multimodal: table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering.
🤗 Transformers support framework interoperability between PyTorch, TensorFlow, and JAX. This provides the flexibility to use a different framework at each stage of a model’s life; train a model in three lines of code in one framework, and load it for inference in another. Models can also be exported to a format like ONNX and TorchScript for deployment in production environments.
Join the growing community on the Hub, forum, or Discord today!
If you are looking for custom support from the Hugging Face team
Contents
The documentation is organized into five sections:
GET STARTED provides a quick tour of the library and installation instructions to get up and running.
TUTORIALS are a great place to start if you’re a beginner. This section will help you gain the basic skills you need to start using the library.
HOW-TO GUIDES show you how to achieve a specific goal, like finetuning a pretrained model for language modeling or how to write and share a custom model.
CONCEPTUAL GUIDES offers more discussion and explanation of the underlying concepts and ideas behind models, tasks, and the design philosophy of 🤗 Transformers.
API describes all classes and functions:
MAIN CLASSES details the most important classes like configuration, model, tokenizer, and pipeline.
MODELS details the classes and functions related to each model implemented in the library.
INTERNAL HELPERS details utility classes and functions used internally.
Supported models
ALBERT (from Google Research and the Toyota Technological Institute at Chicago) released with the paper ALBERT: A Lite BERT for Self-supervised Learning of Language Representations, by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
ALIGN (from Google Research) released with the paper Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision by Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, Tom Duerig.
AltCLIP (from BAAI) released with the paper AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities by Chen, Zhongzhi and Liu, Guang and Zhang, Bo-Wen and Ye, Fulong and Yang, Qinghong and Wu, Ledell.
Audio Spectrogram Transformer (from MIT) released with the paper AST: Audio Spectrogram Transformer by Yuan Gong, Yu-An Chung, James Glass.
Autoformer (from Tsinghua University) released with the paper Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting by Haixu Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long.
Bark (from Suno) released in the repository suno-ai/bark by Suno AI team.
BART (from Facebook) released with the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
BARThez (from École polytechnique) released with the paper BARThez: a Skilled Pretrained French Sequence-to-Sequence Model by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis.
BARTpho (from VinAI Research) released with the paper BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese by Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen.
BEiT (from Microsoft) released with the paper BEiT: BERT Pre-Training of Image Transformers by Hangbo Bao, Li Dong, Furu Wei.
BERT (from Google) released with the paper BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
BERT For Sequence Generation (from Google) released with the paper Leveraging Pre-trained Checkpoints for Sequence Generation Tasks by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
BERTweet (from VinAI Research) released with the paper BERTweet: A pre-trained language model for English Tweets by Dat Quoc Nguyen, Thanh Vu and Anh Tuan Nguyen.
BigBird-Pegasus (from Google Research) released with the paper Big Bird: Transformers for Longer Sequences by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
BigBird-RoBERTa (from Google Research) released with the paper Big Bird: Transformers for Longer Sequences by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
BioGpt (from Microsoft Research AI4Science) released with the paper BioGPT: generative pre-trained transformer for biomedical text generation and mining by Renqian Luo, Liai Sun, Yingce Xia, Tao Qin, Sheng Zhang, Hoifung Poon and Tie-Yan Liu.
BiT (from Google AI) released with the paper Big Transfer (BiT): General Visual Representation Learning by Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, Neil Houlsby.
Blenderbot (from Facebook) released with the paper Recipes for building an open-domain chatbot by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
BlenderbotSmall (from Facebook) released with the paper Recipes for building an open-domain chatbot by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
BLIP (from Salesforce) released with the paper BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation by Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi.
BLIP-2 (from Salesforce) released with the paper BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models by Junnan Li, Dongxu Li, Silvio Savarese, Steven Hoi.
BLOOM (from BigScience workshop) released by the BigScience Workshop.
BORT (from Alexa) released with the paper Optimal Subarchitecture Extraction For BERT by Adrian de Wynter and Daniel J. Perry.
BridgeTower (from Harbin Institute of Technology/Microsoft Research Asia/Intel Labs) released with the paper BridgeTower: Building Bridges Between Encoders in Vision-Language Representation Learning by Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan.
BROS (from NAVER CLOVA) released with the paper BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documents by Teakgyu Hong, Donghyun Kim, Mingi Ji, Wonseok Hwang, Daehyun Nam, Sungrae Park.
ByT5 (from Google Research) released with the paper ByT5: Towards a token-free future with pre-trained byte-to-byte models by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel.
CamemBERT (from Inria/Facebook/Sorbonne) released with the paper CamemBERT: a Tasty French Language Model by Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
CANINE (from Google Research) released with the paper CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.
Chinese-CLIP (from OFA-Sys) released with the paper Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese by An Yang, Junshu Pan, Junyang Lin, Rui Men, Yichang Zhang, Jingren Zhou, Chang Zhou.
CLAP (from LAION-AI) released with the paper Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation by Yusong Wu, Ke Chen, Tianyu Zhang, Yuchen Hui, Taylor Berg-Kirkpatrick, Shlomo Dubnov.
CLIP (from OpenAI) released with the paper Learning Transferable Visual Models From Natural Language Supervision by 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.
CLIPSeg (from University of Göttingen) released with the paper Image Segmentation Using Text and Image Prompts by Timo Lüddecke and Alexander Ecker.
CodeGen (from Salesforce) released with the paper A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong.
CodeLlama (from MetaAI) released with the paper Code Llama: Open Foundation Models for Code by Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, Artyom Kozhevnikov, Ivan Evtimov, Joanna Bitton, Manish Bhatt, Cristian Canton Ferrer, Aaron Grattafiori, Wenhan Xiong, Alexandre Défossez, Jade Copet, Faisal Azhar, Hugo Touvron, Louis Martin, Nicolas Usunier, Thomas Scialom, Gabriel Synnaeve.
Conditional DETR (from Microsoft Research Asia) released with the paper Conditional DETR for Fast Training Convergence by Depu Meng, Xiaokang Chen, Zejia Fan, Gang Zeng, Houqiang Li, Yuhui Yuan, Lei Sun, Jingdong Wang.
ConvBERT (from YituTech) released with the paper ConvBERT: Improving BERT with Span-based Dynamic Convolution by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
ConvNeXT (from Facebook AI) released with the paper A ConvNet for the 2020s by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie.
ConvNeXTV2 (from Facebook AI) released with the paper ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders by Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, Saining Xie.
CPM (from Tsinghua University) released with the paper CPM: A Large-scale Generative Chinese Pre-trained Language Model by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun.
CPM-Ant (from OpenBMB) released by the OpenBMB.
CTRL (from Salesforce) released with the paper CTRL: A Conditional Transformer Language Model for Controllable Generation by Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, Caiming Xiong and Richard Socher.
CvT (from Microsoft) released with the paper CvT: Introducing Convolutions to Vision Transformers by Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan, Lei Zhang.
Data2Vec (from Facebook) released with the paper Data2Vec: A General Framework for Self-supervised Learning in Speech, Vision and Language by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli.
DeBERTa (from Microsoft) released with the paper DeBERTa: Decoding-enhanced BERT with Disentangled Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
DeBERTa-v2 (from Microsoft) released with the paper DeBERTa: Decoding-enhanced BERT with Disentangled Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
Decision Transformer (from Berkeley/Facebook/Google) released with the paper Decision Transformer: Reinforcement Learning via Sequence Modeling by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch.
Deformable DETR (from SenseTime Research) released with the paper Deformable DETR: Deformable Transformers for End-to-End Object Detection by Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, Jifeng Dai.
DeiT (from Facebook) released with the paper Training data-efficient image transformers & distillation through attention by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
DePlot (from Google AI) released with the paper DePlot: One-shot visual language reasoning by plot-to-table translation by Fangyu Liu, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Wenhu Chen, Nigel Collier, Yasemin Altun.
DETA (from The University of Texas at Austin) released with the paper NMS Strikes Back by Jeffrey Ouyang-Zhang, Jang Hyun Cho, Xingyi Zhou, Philipp Krähenbühl.
DETR (from Facebook) released with the paper End-to-End Object Detection with Transformers by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
DialoGPT (from Microsoft Research) released with the paper DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
DiNAT (from SHI Labs) released with the paper Dilated Neighborhood Attention Transformer by Ali Hassani and Humphrey Shi.
DINOv2 (from Meta AI) released with the paper DINOv2: Learning Robust Visual Features without Supervision by Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mahmoud Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, Piotr Bojanowski.
DistilBERT (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. The same method has been applied to compress GPT2 into DistilGPT2, RoBERTa into DistilRoBERTa, Multilingual BERT into DistilmBERT and a German version of DistilBERT.
DiT (from Microsoft Research) released with the paper DiT: Self-supervised Pre-training for Document Image Transformer by Junlong Li, Yiheng Xu, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei.
Donut (from NAVER), released together with the paper OCR-free Document Understanding Transformer by Geewook Kim, Teakgyu Hong, Moonbin Yim, Jeongyeon Nam, Jinyoung Park, Jinyeong Yim, Wonseok Hwang, Sangdoo Yun, Dongyoon Han, Seunghyun Park.
DPR (from Facebook) released with the paper Dense Passage Retrieval for Open-Domain Question Answering by Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
DPT (from Intel Labs) released with the paper Vision Transformers for Dense Prediction by René Ranftl, Alexey Bochkovskiy, Vladlen Koltun.
EfficientFormer (from Snap Research) released with the paper EfficientFormer: Vision Transformers at MobileNetSpeed by Yanyu Li, Geng Yuan, Yang Wen, Ju Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, Jian Ren.
EfficientNet (from Google Brain) released with the paper EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks by Mingxing Tan, Quoc V. Le.
ELECTRA (from Google Research/Stanford University) released with the paper ELECTRA: Pre-training text encoders as discriminators rather than generators by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
EnCodec (from Meta AI) released with the paper High Fidelity Neural Audio Compression by Alexandre Défossez, Jade Copet, Gabriel Synnaeve, Yossi Adi.
EncoderDecoder (from Google Research) released with the paper Leveraging Pre-trained Checkpoints for Sequence Generation Tasks by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
ERNIE (from Baidu) released with the paper ERNIE: Enhanced Representation through Knowledge Integration by Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Xuyi Chen, Han Zhang, Xin Tian, Danxiang Zhu, Hao Tian, Hua Wu.
ErnieM (from Baidu) released with the paper ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora by Xuan Ouyang, Shuohuan Wang, Chao Pang, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang.
ESM (from Meta AI) are transformer protein language models. ESM-1b was released with the paper Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences by Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, and Rob Fergus. ESM-1v was released with the paper Language models enable zero-shot prediction of the effects of mutations on protein function by Joshua Meier, Roshan Rao, Robert Verkuil, Jason Liu, Tom Sercu and Alexander Rives. ESM-2 and ESMFold were released with the paper Language models of protein sequences at the scale of evolution enable accurate structure prediction by Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido, Alexander Rives.
Falcon (from Technology Innovation Institute) by Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme.
FLAN-T5 (from Google AI) released in the repository google-research/t5x by Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei
FLAN-UL2 (from Google AI) released in the repository google-research/t5x by Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei
FlauBERT (from CNRS) released with the paper FlauBERT: Unsupervised Language Model Pre-training for French by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
FLAVA (from Facebook AI) released with the paper FLAVA: A Foundational Language And Vision Alignment Model by Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela.
FNet (from Google Research) released with the paper FNet: Mixing Tokens with Fourier Transforms by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon.
FocalNet (from Microsoft Research) released with the paper Focal Modulation Networks by Jianwei Yang, Chunyuan Li, Xiyang Dai, Lu Yuan, Jianfeng Gao.
Funnel Transformer (from CMU/Google Brain) released with the paper Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
GIT (from Microsoft Research) released with the paper GIT: A Generative Image-to-text Transformer for Vision and Language by Jianfeng Wang, Zhengyuan Yang, Xiaowei Hu, Linjie Li, Kevin Lin, Zhe Gan, Zicheng Liu, Ce Liu, Lijuan Wang.
GLPN (from KAIST) released with the paper Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth by Doyeon Kim, Woonghyun Ga, Pyungwhan Ahn, Donggyu Joo, Sehwan Chun, Junmo Kim.
GPT (from OpenAI) released with the paper Improving Language Understanding by Generative Pre-Training by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
GPT Neo (from EleutherAI) released in the repository EleutherAI/gpt-neo by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
GPT NeoX (from EleutherAI) released with the paper GPT-NeoX-20B: An Open-Source Autoregressive Language Model by Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, Samuel Weinbach
GPT NeoX Japanese (from ABEJA) released by Shinya Otani, Takayoshi Makabe, Anuj Arora, and Kyo Hattori.
GPT-2 (from OpenAI) released with the paper Language Models are Unsupervised Multitask Learners by Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodeiand Ilya Sutskever.
GPT-J (from EleutherAI) released in the repository kingoflolz/mesh-transformer-jax by Ben Wang and Aran Komatsuzaki.
GPT-Sw3 (from AI-Sweden) released with the paper Lessons Learned from GPT-SW3: Building the First Large-Scale Generative Language Model for Swedish by Ariel Ekgren, Amaru Cuba Gyllensten, Evangelia Gogoulou, Alice Heiman, Severine Verlinden, Joey Öhman, Fredrik Carlsson, Magnus Sahlgren.
GPTBigCode (from BigCode) released with the paper SantaCoder: don’t reach for the stars! by Loubna Ben Allal, Raymond Li, Denis Kocetkov, Chenghao Mou, Christopher Akiki, Carlos Munoz Ferrandis, Niklas Muennighoff, Mayank Mishra, Alex Gu, Manan Dey, Logesh Kumar Umapathi, Carolyn Jane Anderson, Yangtian Zi, Joel Lamy Poirier, Hailey Schoelkopf, Sergey Troshin, Dmitry Abulkhanov, Manuel Romero, Michael Lappert, Francesco De Toni, Bernardo García del Río, Qian Liu, Shamik Bose, Urvashi Bhattacharyya, Terry Yue Zhuo, Ian Yu, Paulo Villegas, Marco Zocca, Sourab Mangrulkar, David Lansky, Huu Nguyen, Danish Contractor, Luis Villa, Jia Li, Dzmitry Bahdanau, Yacine Jernite, Sean Hughes, Daniel Fried, Arjun Guha, Harm de Vries, Leandro von Werra.
GPTSAN-japanese released in the repository tanreinama/GPTSAN by Toshiyuki Sakamoto(tanreinama).
Graphormer (from Microsoft) released with the paper Do Transformers Really Perform Bad for Graph Representation? by Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, Tie-Yan Liu.
GroupViT (from UCSD, NVIDIA) released with the paper GroupViT: Semantic Segmentation Emerges from Text Supervision by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang.
HerBERT (from Allegro.pl, AGH University of Science and Technology) released with the paper KLEJ: Comprehensive Benchmark for Polish Language Understanding by Piotr Rybak, Robert Mroczkowski, Janusz Tracz, Ireneusz Gawlik.
Hubert (from Facebook) released with the paper HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
I-BERT (from Berkeley) released with the paper I-BERT: Integer-only BERT Quantization by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.
IDEFICS (from HuggingFace) released with the paper OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents by Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela, Matthieu Cord, Victor Sanh.
ImageGPT (from OpenAI) released with the paper Generative Pretraining from Pixels by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever.
Informer (from Beihang University, UC Berkeley, Rutgers University, SEDD Company) released with the paper Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting by Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang.
InstructBLIP (from Salesforce) released with the paper InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning by Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi.
Jukebox (from OpenAI) released with the paper Jukebox: A Generative Model for Music by Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, Ilya Sutskever.
LayoutLM (from Microsoft Research Asia) released with the paper LayoutLM: Pre-training of Text and Layout for Document Image Understanding by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
LayoutLMv2 (from Microsoft Research Asia) released with the paper LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou.
LayoutLMv3 (from Microsoft Research Asia) released with the paper LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking by Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, Furu Wei.
LayoutXLM (from Microsoft Research Asia) released with the paper LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
LED (from AllenAI) released with the paper Longformer: The Long-Document Transformer by Iz Beltagy, Matthew E. Peters, Arman Cohan.
LeViT (from Meta AI) released with the paper LeViT: A Vision Transformer in ConvNet’s Clothing for Faster Inference by Ben Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, Matthijs Douze.
LiLT (from South China University of Technology) released with the paper LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding by Jiapeng Wang, Lianwen Jin, Kai Ding.
LLaMA (from The FAIR team of Meta AI) released with the paper LLaMA: Open and Efficient Foundation Language Models by 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.
Llama2 (from The FAIR team of Meta AI) released with the paper Llama2: Open Foundation and Fine-Tuned Chat Models by Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushka rMishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing EllenTan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom.
Longformer (from AllenAI) released with the paper Longformer: The Long-Document Transformer by Iz Beltagy, Matthew E. Peters, Arman Cohan.
LongT5 (from Google AI) released with the paper LongT5: Efficient Text-To-Text Transformer for Long Sequences by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang.
LUKE (from Studio Ousia) released with the paper LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.
LXMERT (from UNC Chapel Hill) released with the paper LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering by Hao Tan and Mohit Bansal.
M-CTC-T (from Facebook) released with the paper Pseudo-Labeling For Massively Multilingual Speech Recognition by Loren Lugosch, Tatiana Likhomanenko, Gabriel Synnaeve, and Ronan Collobert.
M2M100 (from Facebook) released with the paper Beyond English-Centric Multilingual Machine Translation by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
MarianMT Machine translation models trained using OPUS data by Jörg Tiedemann. The Marian Framework is being developed by the Microsoft Translator Team.
MarkupLM (from Microsoft Research Asia) released with the paper MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding by Junlong Li, Yiheng Xu, Lei Cui, Furu Wei.
Mask2Former (from FAIR and UIUC) released with the paper Masked-attention Mask Transformer for Universal Image Segmentation by Bowen Cheng, Ishan Misra, Alexander G. Schwing, Alexander Kirillov, Rohit Girdhar.
MaskFormer (from Meta and UIUC) released with the paper Per-Pixel Classification is Not All You Need for Semantic Segmentation by Bowen Cheng, Alexander G. Schwing, Alexander Kirillov.
MatCha (from Google AI) released with the paper MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering by Fangyu Liu, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Yasemin Altun, Nigel Collier, Julian Martin Eisenschlos.
mBART (from Facebook) released with the paper Multilingual Denoising Pre-training for Neural Machine Translation by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
mBART-50 (from Facebook) released with the paper Multilingual Translation with Extensible Multilingual Pretraining and Finetuning by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
MEGA (from Meta/USC/CMU/SJTU) released with the paper Mega: Moving Average Equipped Gated Attention by Xuezhe Ma, Chunting Zhou, Xiang Kong, Junxian He, Liangke Gui, Graham Neubig, Jonathan May, and Luke Zettlemoyer.
Megatron-BERT (from NVIDIA) released with the paper Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
Megatron-GPT2 (from NVIDIA) released with the paper Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
MGP-STR (from Alibaba Research) released with the paper Multi-Granularity Prediction for Scene Text Recognition by Peng Wang, Cheng Da, and Cong Yao.
Mistral (from Mistral AI) by The Mistral AI team: Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
mLUKE (from Studio Ousia) released with the paper mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models by Ryokan Ri, Ikuya Yamada, and Yoshimasa Tsuruoka.
MMS (from Facebook) released with the paper Scaling Speech Technology to 1,000+ Languages by Vineel Pratap, Andros Tjandra, Bowen Shi, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Zhaoheng Ni, Apoorv Vyas, Maryam Fazel-Zarandi, Alexei Baevski, Yossi Adi, Xiaohui Zhang, Wei-Ning Hsu, Alexis Conneau, Michael Auli.
MobileBERT (from CMU/Google Brain) released with the paper MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices by Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny Zhou.
MobileNetV1 (from Google Inc.) released with the paper MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications by Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam.
MobileNetV2 (from Google Inc.) released with the paper MobileNetV2: Inverted Residuals and Linear Bottlenecks by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen.
MobileViT (from Apple) released with the paper MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer by Sachin Mehta and Mohammad Rastegari.
MobileViTV2 (from Apple) released with the paper Separable Self-attention for Mobile Vision Transformers by Sachin Mehta and Mohammad Rastegari.
MPNet (from Microsoft Research) released with the paper MPNet: Masked and Permuted Pre-training for Language Understanding by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
MPT (from MosaiML) released with the repository llm-foundry by the MosaicML NLP Team.
MRA (from the University of Wisconsin - Madison) released with the paper Multi Resolution Analysis (MRA) for Approximate Self-Attention by Zhanpeng Zeng, Sourav Pal, Jeffery Kline, Glenn M Fung, Vikas Singh.
MT5 (from Google AI) released with the paper mT5: A massively multilingual pre-trained text-to-text transformer by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
MusicGen (from Meta) released with the paper Simple and Controllable Music Generation by Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi and Alexandre Défossez.
MVP (from RUC AI Box) released with the paper MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
NAT (from SHI Labs) released with the paper Neighborhood Attention Transformer by Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi.
Nezha (from Huawei Noah’s Ark Lab) released with the paper NEZHA: Neural Contextualized Representation for Chinese Language Understanding by Junqiu Wei, Xiaozhe Ren, Xiaoguang Li, Wenyong Huang, Yi Liao, Yasheng Wang, Jiashu Lin, Xin Jiang, Xiao Chen and Qun Liu.
NLLB (from Meta) released with the paper No Language Left Behind: Scaling Human-Centered Machine Translation by the NLLB team.
NLLB-MOE (from Meta) released with the paper No Language Left Behind: Scaling Human-Centered Machine Translation by the NLLB team.
Nougat (from Meta AI) released with the paper Nougat: Neural Optical Understanding for Academic Documents by Lukas Blecher, Guillem Cucurull, Thomas Scialom, Robert Stojnic.
Nyströmformer (from the University of Wisconsin - Madison) released with the paper Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention by Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh.
OneFormer (from SHI Labs) released with the paper OneFormer: One Transformer to Rule Universal Image Segmentation by Jitesh Jain, Jiachen Li, MangTik Chiu, Ali Hassani, Nikita Orlov, Humphrey Shi.
OpenLlama (from s-JoL) released in Open-Llama.
OPT (from Meta AI) released with the paper OPT: Open Pre-trained Transformer Language Models by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al.
OWL-ViT (from Google AI) released with the paper Simple Open-Vocabulary Object Detection with Vision Transformers by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby.
Pegasus (from Google) released with the paper PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
PEGASUS-X (from Google) released with the paper Investigating Efficiently Extending Transformers for Long Input Summarization by Jason Phang, Yao Zhao, and Peter J. Liu.
Perceiver IO (from Deepmind) released with the paper Perceiver IO: A General Architecture for Structured Inputs & Outputs by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.
Persimmon (from ADEPT) released in a blog post by Erich Elsen, Augustus Odena, Maxwell Nye, Sağnak Taşırlar, Tri Dao, Curtis Hawthorne, Deepak Moparthi, Arushi Somani.
PhoBERT (from VinAI Research) released with the paper PhoBERT: Pre-trained language models for Vietnamese by Dat Quoc Nguyen and Anh Tuan Nguyen.
Pix2Struct (from Google) released with the paper Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding by Kenton Lee, Mandar Joshi, Iulia Turc, Hexiang Hu, Fangyu Liu, Julian Eisenschlos, Urvashi Khandelwal, Peter Shaw, Ming-Wei Chang, Kristina Toutanova.
PLBart (from UCLA NLP) released with the paper Unified Pre-training for Program Understanding and Generation by Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang.
PoolFormer (from Sea AI Labs) released with the paper MetaFormer is Actually What You Need for Vision by Yu, Weihao and Luo, Mi and Zhou, Pan and Si, Chenyang and Zhou, Yichen and Wang, Xinchao and Feng, Jiashi and Yan, Shuicheng.
Pop2Piano released with the paper Pop2Piano : Pop Audio-based Piano Cover Generation by Jongho Choi and Kyogu Lee.
ProphetNet (from Microsoft Research) released with the paper ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
PVT (from Nanjing University, The University of Hong Kong etc.) released with the paper Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions by Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao.
QDQBert (from NVIDIA) released with the paper Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation by Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius.
RAG (from Facebook) released with the paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks by Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela.
REALM (from Google Research) released with the paper REALM: Retrieval-Augmented Language Model Pre-Training by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang.
Reformer (from Google Research) released with the paper Reformer: The Efficient Transformer by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
RegNet (from META Platforms) released with the paper Designing Network Design Space by Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár.
RemBERT (from Google Research) released with the paper Rethinking embedding coupling in pre-trained language models by Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder.
ResNet (from Microsoft Research) released with the paper Deep Residual Learning for Image Recognition by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
RoBERTa (from Facebook), released together with the paper RoBERTa: A Robustly Optimized BERT Pretraining Approach by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
RoBERTa-PreLayerNorm (from Facebook) released with the paper fairseq: A Fast, Extensible Toolkit for Sequence Modeling by Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, Michael Auli.
RoCBert (from WeChatAI) released with the paper RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining by HuiSu, WeiweiShi, XiaoyuShen, XiaoZhou, TuoJi, JiaruiFang, JieZhou.
RoFormer (from ZhuiyiTechnology), released together with the paper RoFormer: Enhanced Transformer with Rotary Position Embedding by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
RWKV (from Bo Peng), released on this repo by Bo Peng.
SegFormer (from NVIDIA) released with the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
Segment Anything (from Meta AI) released with the paper Segment Anything by Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick.
SEW (from ASAPP) released with the paper Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
SEW-D (from ASAPP) released with the paper Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
SpeechT5 (from Microsoft Research) released with the paper SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing by Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei.
SpeechToTextTransformer (from Facebook), released together with the paper fairseq S2T: Fast Speech-to-Text Modeling with fairseq by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
SpeechToTextTransformer2 (from Facebook), released together with the paper Large-Scale Self- and Semi-Supervised Learning for Speech Translation by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.
Splinter (from Tel Aviv University), released together with the paper Few-Shot Question Answering by Pretraining Span Selection by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy.
SqueezeBERT (from Berkeley) released with the paper SqueezeBERT: What can computer vision teach NLP about efficient neural networks? by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
SwiftFormer (from MBZUAI) released with the paper SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan.
Swin Transformer (from Microsoft) released with the paper Swin Transformer: Hierarchical Vision Transformer using Shifted Windows by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
Swin Transformer V2 (from Microsoft) released with the paper Swin Transformer V2: Scaling Up Capacity and Resolution by Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo.
Swin2SR (from University of Würzburg) released with the paper Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration by Marcos V. Conde, Ui-Jin Choi, Maxime Burchi, Radu Timofte.
SwitchTransformers (from Google) released with the paper Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity by William Fedus, Barret Zoph, Noam Shazeer.
T5 (from Google AI) released with the paper Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
T5v1.1 (from Google AI) released in the repository google-research/text-to-text-transfer-transformer by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
Table Transformer (from Microsoft Research) released with the paper PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents by Brandon Smock, Rohith Pesala, Robin Abraham.
TAPAS (from Google AI) released with the paper TAPAS: Weakly Supervised Table Parsing via Pre-training by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
TAPEX (from Microsoft Research) released with the paper TAPEX: Table Pre-training via Learning a Neural SQL Executor by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou.
Time Series Transformer (from HuggingFace).
TimeSformer (from Facebook) released with the paper Is Space-Time Attention All You Need for Video Understanding? by Gedas Bertasius, Heng Wang, Lorenzo Torresani.
Trajectory Transformer (from the University of California at Berkeley) released with the paper Offline Reinforcement Learning as One Big Sequence Modeling Problem by Michael Janner, Qiyang Li, Sergey Levine
Transformer-XL (from Google/CMU) released with the paper Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context by Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
TrOCR (from Microsoft), released together with the paper TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.
TVLT (from UNC Chapel Hill) released with the paper TVLT: Textless Vision-Language Transformer by Zineng Tang, Jaemin Cho, Yixin Nie, Mohit Bansal.
UL2 (from Google Research) released with the paper Unifying Language Learning Paradigms by Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Neil Houlsby, Donald Metzler
UMT5 (from Google Research) released with the paper UniMax: Fairer and More Effective Language Sampling for Large-Scale Multilingual Pretraining by Hyung Won Chung, Xavier Garcia, Adam Roberts, Yi Tay, Orhan Firat, Sharan Narang, Noah Constant.
UniSpeech (from Microsoft Research) released with the paper UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang.
UniSpeechSat (from Microsoft Research) released with the paper UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.
UPerNet (from Peking University) released with the paper Unified Perceptual Parsing for Scene Understanding by Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, Jian Sun.
VAN (from Tsinghua University and Nankai University) released with the paper Visual Attention Network by Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu.
VideoMAE (from Multimedia Computing Group, Nanjing University) released with the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Zhan Tong, Yibing Song, Jue Wang, Limin Wang.
ViLT (from NAVER AI Lab/Kakao Enterprise/Kakao Brain) released with the paper ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision by Wonjae Kim, Bokyung Son, Ildoo Kim.
Vision Transformer (ViT) (from Google AI) released with the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
VisualBERT (from UCLA NLP) released with the paper VisualBERT: A Simple and Performant Baseline for Vision and Language by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
ViT Hybrid (from Google AI) released with the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
VitDet (from Meta AI) released with the paper Exploring Plain Vision Transformer Backbones for Object Detection by Yanghao Li, Hanzi Mao, Ross Girshick, Kaiming He.
ViTMAE (from Meta AI) released with the paper Masked Autoencoders Are Scalable Vision Learners by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick.
ViTMatte (from HUST-VL) rreleased with the paper ViTMatte: Boosting Image Matting with Pretrained Plain Vision Transformers by Jingfeng Yao, Xinggang Wang, Shusheng Yang, Baoyuan Wang.
ViTMSN (from Meta AI) released with the paper Masked Siamese Networks for Label-Efficient Learning by Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Michael Rabbat, Nicolas Ballas.
VITS (from Kakao Enterprise) released with the paper Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech by Jaehyeon Kim, Jungil Kong, Juhee Son.
ViViT (from Google Research) released with the paper ViViT: A Video Vision Transformer by Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, Cordelia Schmid.
Wav2Vec2 (from Facebook AI) released with the paper wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
Wav2Vec2-Conformer (from Facebook AI) released with the paper FAIRSEQ S2T: Fast Speech-to-Text Modeling with FAIRSEQ by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino.
Wav2Vec2Phoneme (from Facebook AI) released with the paper Simple and Effective Zero-shot Cross-lingual Phoneme Recognition by Qiantong Xu, Alexei Baevski, Michael Auli.
WavLM (from Microsoft Research) released with the paper WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
Whisper (from OpenAI) released with the paper Robust Speech Recognition via Large-Scale Weak Supervision by Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever.
X-CLIP (from Microsoft Research) released with the paper Expanding Language-Image Pretrained Models for General Video Recognition by Bolin Ni, Houwen Peng, Minghao Chen, Songyang Zhang, Gaofeng Meng, Jianlong Fu, Shiming Xiang, Haibin Ling.
X-MOD (from Meta AI) released with the paper Lifting the Curse of Multilinguality by Pre-training Modular Transformers by Jonas Pfeiffer, Naman Goyal, Xi Lin, Xian Li, James Cross, Sebastian Riedel, Mikel Artetxe.
XGLM (From Facebook AI) released with the paper Few-shot Learning with Multilingual Language Models by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O’Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li.
XLM (from Facebook) released together with the paper Cross-lingual Language Model Pretraining by Guillaume Lample and Alexis Conneau.
XLM-ProphetNet (from Microsoft Research) released with the paper ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
XLM-RoBERTa (from Facebook AI), released together with the paper Unsupervised Cross-lingual Representation Learning at Scale by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
XLM-RoBERTa-XL (from Facebook AI), released together with the paper Larger-Scale Transformers for Multilingual Masked Language Modeling by Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau.
XLM-V (from Meta AI) released with the paper XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models by Davis Liang, Hila Gonen, Yuning Mao, Rui Hou, Naman Goyal, Marjan Ghazvininejad, Luke Zettlemoyer, Madian Khabsa.
XLNet (from Google/CMU) released with the paper XLNet: Generalized Autoregressive Pretraining for Language Understanding by Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
XLS-R (from Facebook AI) released with the paper XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
XLSR-Wav2Vec2 (from Facebook AI) released with the paper Unsupervised Cross-Lingual Representation Learning For Speech Recognition by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
YOLOS (from Huazhong University of Science & Technology) released with the paper You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection by Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu.
YOSO (from the University of Wisconsin - Madison) released with the paper You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh.
Supported frameworks
The table below represents the current support in the library for each of those models, whether they have a Python tokenizer (called “slow”). A “fast” tokenizer backed by the 🤗 Tokenizers library, whether they have support in Jax (via Flax), PyTorch, and/or TensorFlow.
Model PyTorch support TensorFlow support Flax Support
ALBERT ✅ ✅ ✅
ALIGN ✅ ❌ ❌
AltCLIP ✅ ❌ ❌
Audio Spectrogram Transformer ✅ ❌ ❌
Autoformer ✅ ❌ ❌
Bark ✅ ❌ ❌
BART ✅ ✅ ✅
BEiT ✅ ❌ ✅
BERT ✅ ✅ ✅
Bert Generation ✅ ❌ ❌
BigBird ✅ ❌ ✅
BigBird-Pegasus ✅ ❌ ❌
BioGpt ✅ ❌ ❌
BiT ✅ ❌ ❌
Blenderbot ✅ ✅ ✅
BlenderbotSmall ✅ ✅ ✅
BLIP ✅ ✅ ❌
BLIP-2 ✅ ❌ ❌
BLOOM ✅ ❌ ✅
BridgeTower ✅ ❌ ❌
BROS ✅ ❌ ❌
CamemBERT ✅ ✅ ❌
CANINE ✅ ❌ ❌
Chinese-CLIP ✅ ❌ ❌
CLAP ✅ ❌ ❌
CLIP ✅ ✅ ✅
CLIPSeg ✅ ❌ ❌
CodeGen ✅ ❌ ❌
CodeLlama ✅ ❌ ❌
Conditional DETR ✅ ❌ ❌
ConvBERT ✅ ✅ ❌
ConvNeXT ✅ ✅ ❌
ConvNeXTV2 ✅ ❌ ❌
CPM-Ant ✅ ❌ ❌
CTRL ✅ ✅ ❌
CvT ✅ ✅ ❌
Data2VecAudio ✅ ❌ ❌
Data2VecText ✅ ❌ ❌
Data2VecVision ✅ ✅ ❌
DeBERTa ✅ ✅ ❌
DeBERTa-v2 ✅ ✅ ❌
Decision Transformer ✅ ❌ ❌
Deformable DETR ✅ ❌ ❌
DeiT ✅ ✅ ❌
DETA ✅ ❌ ❌
DETR ✅ ❌ ❌
DiNAT ✅ ❌ ❌
DINOv2 ✅ ❌ ❌
DistilBERT ✅ ✅ ✅
DonutSwin ✅ ❌ ❌
DPR ✅ ✅ ❌
DPT ✅ ❌ ❌
EfficientFormer ✅ ✅ ❌
EfficientNet ✅ ❌ ❌
ELECTRA ✅ ✅ ✅
EnCodec ✅ ❌ ❌
Encoder decoder ✅ ✅ ✅
ERNIE ✅ ❌ ❌
ErnieM ✅ ❌ ❌
ESM ✅ ✅ ❌
FairSeq Machine-Translation ✅ ❌ ❌
Falcon ✅ ❌ ❌
FlauBERT ✅ ✅ ❌
FLAVA ✅ ❌ ❌
FNet ✅ ❌ ❌
FocalNet ✅ ❌ ❌
Funnel Transformer ✅ ✅ ❌
GIT ✅ ❌ ❌
GLPN ✅ ❌ ❌
GPT Neo ✅ ❌ ✅
GPT NeoX ✅ ❌ ❌
GPT NeoX Japanese ✅ ❌ ❌
GPT-J ✅ ✅ ✅
GPT-Sw3 ✅ ✅ ✅
GPTBigCode ✅ ❌ ❌
GPTSAN-japanese ✅ ❌ ❌
Graphormer ✅ ❌ ❌
GroupViT ✅ ✅ ❌
Hubert ✅ ✅ ❌
I-BERT ✅ ❌ ❌
IDEFICS ✅ ❌ ❌
ImageGPT ✅ ❌ ❌
Informer ✅ ❌ ❌
InstructBLIP ✅ ❌ ❌
Jukebox ✅ ❌ ❌
LayoutLM ✅ ✅ ❌
LayoutLMv2 ✅ ❌ ❌
LayoutLMv3 ✅ ✅ ❌
LED ✅ ✅ ❌
LeViT ✅ ❌ ❌
LiLT ✅ ❌ ❌
LLaMA ✅ ❌ ❌
Longformer ✅ ✅ ❌
LongT5 ✅ ❌ ✅
LUKE ✅ ❌ ❌
LXMERT ✅ ✅ ❌
M-CTC-T ✅ ❌ ❌
M2M100 ✅ ❌ ❌
Marian ✅ ✅ ✅
MarkupLM ✅ ❌ ❌
Mask2Former ✅ ❌ ❌
MaskFormer ✅ ❌ ❌
mBART ✅ ✅ ✅
MEGA ✅ ❌ ❌
Megatron-BERT ✅ ❌ ❌
MGP-STR ✅ ❌ ❌
Mistral ✅ ❌ ❌
MobileBERT ✅ ✅ ❌
MobileNetV1 ✅ ❌ ❌
MobileNetV2 ✅ ❌ ❌
MobileViT ✅ ✅ ❌
MobileViTV2 ✅ ❌ ❌
MPNet ✅ ✅ ❌
MPT ✅ ❌ ❌
MRA ✅ ❌ ❌
MT5 ✅ ✅ ✅
MusicGen ✅ ❌ ❌
MVP ✅ ❌ ❌
NAT ✅ ❌ ❌
Nezha ✅ ❌ ❌
NLLB-MOE ✅ ❌ ❌
Nougat ✅ ✅ ✅
Nyströmformer ✅ ❌ ❌
OneFormer ✅ ❌ ❌
OpenAI GPT ✅ ✅ ❌
OpenAI GPT-2 ✅ ✅ ✅
OpenLlama ✅ ❌ ❌
OPT ✅ ✅ ✅
OWL-ViT ✅ ❌ ❌
Pegasus ✅ ✅ ✅
PEGASUS-X ✅ ❌ ❌
Perceiver ✅ ❌ ❌
Persimmon ✅ ❌ ❌
Pix2Struct ✅ ❌ ❌
PLBart ✅ ❌ ❌
PoolFormer ✅ ❌ ❌
Pop2Piano ✅ ❌ ❌
ProphetNet ✅ ❌ ❌
PVT ✅ ❌ ❌
QDQBert ✅ ❌ ❌
RAG ✅ ✅ ❌
REALM ✅ ❌ ❌
Reformer ✅ ❌ ❌
RegNet ✅ ✅ ✅
RemBERT ✅ ✅ ❌
ResNet ✅ ✅ ✅
RetriBERT ✅ ❌ ❌
RoBERTa ✅ ✅ ✅
RoBERTa-PreLayerNorm ✅ ✅ ✅
RoCBert ✅ ❌ ❌
RoFormer ✅ ✅ ✅
RWKV ✅ ❌ ❌
SAM ✅ ✅ ❌
SegFormer ✅ ✅ ❌
SEW ✅ ❌ ❌
SEW-D ✅ ❌ ❌
Speech Encoder decoder ✅ ❌ ✅
Speech2Text ✅ ✅ ❌
Speech2Text2 ❌ ❌ ❌
SpeechT5 ✅ ❌ ❌
Splinter ✅ ❌ ❌
SqueezeBERT ✅ ❌ ❌
SwiftFormer ✅ ❌ ❌
Swin Transformer ✅ ✅ ❌
Swin Transformer V2 ✅ ❌ ❌
Swin2SR ✅ ❌ ❌
SwitchTransformers ✅ ❌ ❌
T5 ✅ ✅ ✅
Table Transformer ✅ ❌ ❌
TAPAS ✅ ✅ ❌
Time Series Transformer ✅ ❌ ❌
TimeSformer ✅ ❌ ❌
Trajectory Transformer ✅ ❌ ❌
Transformer-XL ✅ ✅ ❌
TrOCR ✅ ❌ ❌
TVLT ✅ ❌ ❌
UMT5 ✅ ❌ ❌
UniSpeech ✅ ❌ ❌
UniSpeechSat ✅ ❌ ❌
UPerNet ✅ ❌ ❌
VAN ✅ ❌ ❌
VideoMAE ✅ ❌ ❌
ViLT ✅ ❌ ❌
Vision Encoder decoder ✅ ✅ ✅
VisionTextDualEncoder ✅ ✅ ✅
VisualBERT ✅ ❌ ❌
ViT ✅ ✅ ✅
ViT Hybrid ✅ ❌ ❌
VitDet ✅ ❌ ❌
ViTMAE ✅ ✅ ❌
ViTMatte ✅ ❌ ❌
ViTMSN ✅ ❌ ❌
VITS ✅ ❌ ❌
ViViT ✅ ❌ ❌
Wav2Vec2 ✅ ✅ ✅
Wav2Vec2-Conformer ✅ ❌ ❌
WavLM ✅ ❌ ❌
Whisper ✅ ✅ ✅
X-CLIP ✅ ❌ ❌
X-MOD ✅ ❌ ❌
XGLM ✅ ✅ ✅
XLM ✅ ✅ ❌
XLM-ProphetNet ✅ ❌ ❌
XLM-RoBERTa ✅ ✅ ✅
XLM-RoBERTa-XL ✅ ❌ ❌
XLNet ✅ ✅ ❌
YOLOS ✅ ❌ ❌
YOSO ✅ ❌ ❌ |
https://huggingface.co/docs/safetensors | You are viewing main version, which requires
installation from source
. If you'd like regular pip install, checkout the latest stable version (v0.3.2).
Safetensors
Safetensors is a new simple format for storing tensors safely (as opposed to pickle) and that is still fast (zero-copy). Safetensors is really fast 🚀.
Installation
with pip:
with conda:
conda install -c huggingface safetensors
Usage
Load tensors
from safetensors import safe_open
tensors = {}
with safe_open("model.safetensors", framework="pt", device=0) as f:
for k in f.keys():
tensors[k] = f.get_tensor(k)
Loading only part of the tensors (interesting when running on multiple GPU)
from safetensors import safe_open
tensors = {}
with safe_open("model.safetensors", framework="pt", device=0) as f:
tensor_slice = f.get_slice("embedding")
vocab_size, hidden_dim = tensor_slice.get_shape()
tensor = tensor_slice[:, :hidden_dim]
Save tensors
import torch
from safetensors.torch import save_file
tensors = {
"embedding": torch.zeros((2, 2)),
"attention": torch.zeros((2, 3))
}
save_file(tensors, "model.safetensors")
Format
Let’s say you have safetensors file named model.safetensors, then model.safetensors will have the following internal format:
Featured Projects
Safetensors is being used widely at leading AI enterprises, such as Hugging Face, EleutherAI, and StabilityAI. Here is a non-exhaustive list of projects that are using safetensors:
huggingface/transformers
AUTOMATIC1111/stable-diffusion-webui
Llama-cpp
microsoft/TaskMatrix
hpcaitech/ColossalAI
huggingface/pytorch-image-models
CivitAI
huggingface/diffusers
coreylowman/dfdx
invoke-ai/InvokeAI
oobabooga/text-generation-webui
Sanster/lama-cleaner
PaddlePaddle/PaddleNLP
AIGC-Audio/AudioGPT
brycedrennan/imaginAIry
comfyanonymous/ComfyUI
LianjiaTech/BELLE
alvarobartt/safejax
MaartenGr/BERTopic
LaurentMazare/tch-rs
chainyo/tensorshare |
https://huggingface.co/docs/diffusers | Diffusers
🤗 Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. Whether you’re looking for a simple inference solution or want to train your own diffusion model, 🤗 Diffusers is a modular toolbox that supports both. Our library is designed with a focus on usability over performance, simple over easy, and customizability over abstractions.
The library has three main components:
State-of-the-art diffusion pipelines for inference with just a few lines of code.
Interchangeable noise schedulers for balancing trade-offs between generation speed and quality.
Pretrained models that can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems.
Supported pipelines
Pipeline Paper/Repository Tasks
alt_diffusion AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities Image-to-Image Text-Guided Generation
audio_diffusion Audio Diffusion Unconditional Audio Generation
controlnet Adding Conditional Control to Text-to-Image Diffusion Models Image-to-Image Text-Guided Generation
cycle_diffusion Unifying Diffusion Models’ Latent Space, with Applications to CycleDiffusion and Guidance Image-to-Image Text-Guided Generation
dance_diffusion Dance Diffusion Unconditional Audio Generation
ddpm Denoising Diffusion Probabilistic Models Unconditional Image Generation
ddim Denoising Diffusion Implicit Models Unconditional Image Generation
if IF Image Generation
if_img2img IF Image-to-Image Generation
if_inpainting IF Image-to-Image Generation
latent_diffusion High-Resolution Image Synthesis with Latent Diffusion Models Text-to-Image Generation
latent_diffusion High-Resolution Image Synthesis with Latent Diffusion Models Super Resolution Image-to-Image
latent_diffusion_uncond High-Resolution Image Synthesis with Latent Diffusion Models Unconditional Image Generation
paint_by_example Paint by Example: Exemplar-based Image Editing with Diffusion Models Image-Guided Image Inpainting
pndm Pseudo Numerical Methods for Diffusion Models on Manifolds Unconditional Image Generation
score_sde_ve Score-Based Generative Modeling through Stochastic Differential Equations Unconditional Image Generation
score_sde_vp Score-Based Generative Modeling through Stochastic Differential Equations Unconditional Image Generation
semantic_stable_diffusion Semantic Guidance Text-Guided Generation
stable_diffusion_adapter T2I-Adapter Image-to-Image Text-Guided Generation
stable_diffusion_text2img Stable Diffusion Text-to-Image Generation
stable_diffusion_img2img Stable Diffusion Image-to-Image Text-Guided Generation
stable_diffusion_inpaint Stable Diffusion Text-Guided Image Inpainting
stable_diffusion_panorama MultiDiffusion Text-to-Panorama Generation
stable_diffusion_pix2pix InstructPix2Pix: Learning to Follow Image Editing Instructions Text-Guided Image Editing
stable_diffusion_pix2pix_zero Zero-shot Image-to-Image Translation Text-Guided Image Editing
stable_diffusion_attend_and_excite Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models Text-to-Image Generation
stable_diffusion_self_attention_guidance Improving Sample Quality of Diffusion Models Using Self-Attention Guidance Text-to-Image Generation Unconditional Image Generation
stable_diffusion_image_variation Stable Diffusion Image Variations Image-to-Image Generation
stable_diffusion_latent_upscale Stable Diffusion Latent Upscaler Text-Guided Super Resolution Image-to-Image
stable_diffusion_model_editing Editing Implicit Assumptions in Text-to-Image Diffusion Models Text-to-Image Model Editing
stable_diffusion_2 Stable Diffusion 2 Text-to-Image Generation
stable_diffusion_2 Stable Diffusion 2 Text-Guided Image Inpainting
stable_diffusion_2 Depth-Conditional Stable Diffusion Depth-to-Image Generation
stable_diffusion_2 Stable Diffusion 2 Text-Guided Super Resolution Image-to-Image
stable_diffusion_safe Safe Stable Diffusion Text-Guided Generation
stable_unclip Stable unCLIP Text-to-Image Generation
stable_unclip Stable unCLIP Image-to-Image Text-Guided Generation
stochastic_karras_ve Elucidating the Design Space of Diffusion-Based Generative Models Unconditional Image Generation
text_to_video_sd Modelscope’s Text-to-video-synthesis Model in Open Domain Text-to-Video Generation
unclip Hierarchical Text-Conditional Image Generation with CLIP Latents(implementation by kakaobrain) Text-to-Image Generation
versatile_diffusion Versatile Diffusion: Text, Images and Variations All in One Diffusion Model Text-to-Image Generation
versatile_diffusion Versatile Diffusion: Text, Images and Variations All in One Diffusion Model Image Variations Generation
versatile_diffusion Versatile Diffusion: Text, Images and Variations All in One Diffusion Model Dual Image and Text Guided Generation
vq_diffusion Vector Quantized Diffusion Model for Text-to-Image Synthesis Text-to-Image Generation
stable_diffusion_ldm3d LDM3D: Latent Diffusion Model for 3D Text to Image and Depth Generation |
https://huggingface.co/docs/huggingface_hub | 🤗 Hub client library
The huggingface_hub library allows you to interact with the Hugging Face Hub, a machine learning platform for creators and collaborators. Discover pre-trained models and datasets for your projects or play with the hundreds of machine learning apps hosted on the Hub. You can also create and share your own models and datasets with the community. The huggingface_hub library provides a simple way to do all these things with Python.
Read the quick start guide to get up and running with the huggingface_hub library. You will learn how to download files from the Hub, create a repository, and upload files to the Hub. Keep reading to learn more about how to manage your repositories on the 🤗 Hub, how to interact in discussions or even how to access the Inference API.
Contribute
All contributions to the huggingface_hub are welcomed and equally valued! 🤗 Besides adding or fixing existing issues in the code, you can also help improve the documentation by making sure it is accurate and up-to-date, help answer questions on issues, and request new features you think will improve the library. Take a look at the contribution guide to learn more about how to submit a new issue or feature request, how to submit a pull request, and how to test your contributions to make sure everything works as expected.
Contributors should also be respectful of our code of conduct to create an inclusive and welcoming collaborative space for everyone. |
https://huggingface.co/docs/tokenizers | Tokenizers
Fast State-of-the-art tokenizers, optimized for both research and production
🤗 Tokenizers provides an implementation of today’s most used tokenizers, with a focus on performance and versatility. These tokenizers are also used in 🤗 Transformers.
Main features:
Train new vocabularies and tokenize, using today’s most used tokenizers.
Extremely fast (both training and tokenization), thanks to the Rust implementation. Takes less than 20 seconds to tokenize a GB of text on a server’s CPU.
Easy to use, but also extremely versatile.
Designed for both research and production.
Full alignment tracking. Even with destructive normalization, it’s always possible to get the part of the original sentence that corresponds to any token.
Does all the pre-processing: Truncation, Padding, add the special tokens your model needs. |
https://huggingface.co/docs/transformers.js | Transformers.js
State-of-the-art Machine Learning for the web. Run 🤗 Transformers directly in your browser, with no need for a server!
Transformers.js is designed to be functionally equivalent to Hugging Face’s transformers python library, meaning you can run the same pretrained models using a very similar API. These models support common tasks in different modalities, such as:
📝 Natural Language Processing: text classification, named entity recognition, question answering, language modeling, summarization, translation, multiple choice, and text generation.
🖼️ Computer Vision: image classification, object detection, and segmentation.
🗣️ Audio: automatic speech recognition and audio classification.
🐙 Multimodal: zero-shot image classification.
Transformers.js uses ONNX Runtime to run models in the browser. The best part about it, is that you can easily convert your pretrained PyTorch, TensorFlow, or JAX models to ONNX using 🤗 Optimum.
For more information, check out the full documentation.
Quick tour
It’s super simple to translate from existing code! Just like the python library, we support the pipeline API. Pipelines group together a pretrained model with preprocessing of inputs and postprocessing of outputs, making it the easiest way to run models with the library.
Python (original) Javascript (ours)
from transformers import pipeline
pipe = pipeline('sentiment-analysis')
out = pipe('I love transformers!')
import { pipeline } from '@xenova/transformers';
let pipe = await pipeline('sentiment-analysis');
let out = await pipe('I love transformers!');
You can also use a different model by specifying the model id or path as the second argument to the pipeline function. For example:
let pipe = await pipeline('sentiment-analysis', 'nlptown/bert-base-multilingual-uncased-sentiment');
Contents
The documentation is organized into 4 sections:
GET STARTED provides a quick tour of the library and installation instructions to get up and running.
TUTORIALS are a great place to start if you’re a beginner! We also include sample applications for you to play around with!
DEVELOPER GUIDES show you how to use the library to achieve a specific goal.
API REFERENCE describes all classes and functions, as well as their available parameters and types.
Supported tasks/models
Here is the list of all tasks and architectures currently supported by Transformers.js. If you don’t see your task/model listed here or it is not yet supported, feel free to open up a feature request here.
To find compatible models on the Hub, select the “transformers.js” library tag in the filter menu (or visit this link). You can refine your search by selecting the task you’re interested in (e.g., text-classification).
Tasks
Natural Language Processing
Task ID Description Supported?
Conversational conversational Generating conversational text that is relevant, coherent and knowledgable given a prompt. ❌
Fill-Mask fill-mask Masking some of the words in a sentence and predicting which words should replace those masks. ✅ (docs)
(models)
Question Answering question-answering Retrieve the answer to a question from a given text. ✅ (docs)
(models)
Sentence Similarity sentence-similarity Determining how similar two texts are. ✅ (docs)
(models)
Summarization summarization Producing a shorter version of a document while preserving its important information. ✅ (docs)
(models)
Table Question Answering table-question-answering Answering a question about information from a given table. ❌
Text Classification text-classification or sentiment-analysis Assigning a label or class to a given text. ✅ (docs)
(models)
Text Generation text-generation Producing new text by predicting the next word in a sequence. ✅ (docs)
(models)
Text-to-text Generation text2text-generation Converting one text sequence into another text sequence. ✅ (docs)
(models)
Token Classification token-classification or ner Assigning a label to each token in a text. ✅ (docs)
(models)
Translation translation Converting text from one language to another. ✅ (docs)
(models)
Zero-Shot Classification zero-shot-classification Classifying text into classes that are unseen during training. ✅ (docs)
(models)
Vision
Task ID Description Supported?
Depth Estimation depth-estimation Predicting the depth of objects present in an image. ❌
Image Classification image-classification Assigning a label or class to an entire image. ✅ (docs)
(models)
Image Segmentation image-segmentation Divides an image into segments where each pixel is mapped to an object. This task has multiple variants such as instance segmentation, panoptic segmentation and semantic segmentation. ✅ (docs)
(models)
Image-to-Image image-to-image Transforming a source image to match the characteristics of a target image or a target image domain. ❌
Mask Generation mask-generation Generate masks for the objects in an image. ❌
Object Detection object-detection Identify objects of certain defined classes within an image. ✅ (docs)
(models)
Video Classification n/a Assigning a label or class to an entire video. ❌
Unconditional Image Generation n/a Generating images with no condition in any context (like a prompt text or another image). ❌
Audio
Task ID Description Supported?
Audio Classification audio-classification Assigning a label or class to a given audio. ✅ (docs)
(models)
Audio-to-Audio n/a Generating audio from an input audio source. ❌
Automatic Speech Recognition automatic-speech-recognition Transcribing a given audio into text. ✅ (docs)
(models)
Text-to-Speech n/a Generating natural-sounding speech given text input. ❌
Tabular
Task ID Description Supported?
Tabular Classification n/a Classifying a target category (a group) based on set of attributes. ❌
Tabular Regression n/a Predicting a numerical value given a set of attributes. ❌
Multimodal
Task ID Description Supported?
Document Question Answering document-question-answering Answering questions on document images. ✅ (docs)
(models)
Feature Extraction feature-extraction Transforming raw data into numerical features that can be processed while preserving the information in the original dataset. ✅ (docs)
(models)
Image-to-Text image-to-text Output text from a given image. ✅ (docs)
(models)
Text-to-Image text-to-image Generates images from input text. ❌
Visual Question Answering visual-question-answering Answering open-ended questions based on an image. ❌
Zero-Shot Image Classification zero-shot-image-classification Classifying images into classes that are unseen during training. ✅ (docs)
(models)
Reinforcement Learning
Task ID Description Supported?
Reinforcement Learning n/a Learning from actions by interacting with an environment through trial and error and receiving rewards (negative or positive) as feedback. ❌
Models
ALBERT (from Google Research and the Toyota Technological Institute at Chicago) released with the paper ALBERT: A Lite BERT for Self-supervised Learning of Language Representations, by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
BART (from Facebook) released with the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
BEiT (from Microsoft) released with the paper BEiT: BERT Pre-Training of Image Transformers by Hangbo Bao, Li Dong, Furu Wei.
BERT (from Google) released with the paper BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
Blenderbot (from Facebook) released with the paper Recipes for building an open-domain chatbot by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
BlenderbotSmall (from Facebook) released with the paper Recipes for building an open-domain chatbot by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
BLOOM (from BigScience workshop) released by the BigScience Workshop.
CamemBERT (from Inria/Facebook/Sorbonne) released with the paper CamemBERT: a Tasty French Language Model by Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
CLIP (from OpenAI) released with the paper Learning Transferable Visual Models From Natural Language Supervision by 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.
CodeGen (from Salesforce) released with the paper A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong.
CodeLlama (from MetaAI) released with the paper Code Llama: Open Foundation Models for Code by Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, Artyom Kozhevnikov, Ivan Evtimov, Joanna Bitton, Manish Bhatt, Cristian Canton Ferrer, Aaron Grattafiori, Wenhan Xiong, Alexandre Défossez, Jade Copet, Faisal Azhar, Hugo Touvron, Louis Martin, Nicolas Usunier, Thomas Scialom, Gabriel Synnaeve.
DeBERTa (from Microsoft) released with the paper DeBERTa: Decoding-enhanced BERT with Disentangled Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
DeBERTa-v2 (from Microsoft) released with the paper DeBERTa: Decoding-enhanced BERT with Disentangled Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
DeiT (from Facebook) released with the paper Training data-efficient image transformers & distillation through attention by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
DETR (from Facebook) released with the paper End-to-End Object Detection with Transformers by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
DistilBERT (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. The same method has been applied to compress GPT2 into DistilGPT2, RoBERTa into DistilRoBERTa, Multilingual BERT into DistilmBERT and a German version of DistilBERT.
Donut (from NAVER), released together with the paper OCR-free Document Understanding Transformer by Geewook Kim, Teakgyu Hong, Moonbin Yim, Jeongyeon Nam, Jinyoung Park, Jinyeong Yim, Wonseok Hwang, Sangdoo Yun, Dongyoon Han, Seunghyun Park.
FLAN-T5 (from Google AI) released in the repository google-research/t5x by Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei
GPT Neo (from EleutherAI) released in the repository EleutherAI/gpt-neo by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
GPT NeoX (from EleutherAI) released with the paper GPT-NeoX-20B: An Open-Source Autoregressive Language Model by Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, Samuel Weinbach
GPT-2 (from OpenAI) released with the paper Language Models are Unsupervised Multitask Learners by Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodeiand Ilya Sutskever.
GPT-J (from EleutherAI) released in the repository kingoflolz/mesh-transformer-jax by Ben Wang and Aran Komatsuzaki.
GPTBigCode (from BigCode) released with the paper SantaCoder: don’t reach for the stars! by Loubna Ben Allal, Raymond Li, Denis Kocetkov, Chenghao Mou, Christopher Akiki, Carlos Munoz Ferrandis, Niklas Muennighoff, Mayank Mishra, Alex Gu, Manan Dey, Logesh Kumar Umapathi, Carolyn Jane Anderson, Yangtian Zi, Joel Lamy Poirier, Hailey Schoelkopf, Sergey Troshin, Dmitry Abulkhanov, Manuel Romero, Michael Lappert, Francesco De Toni, Bernardo García del Río, Qian Liu, Shamik Bose, Urvashi Bhattacharyya, Terry Yue Zhuo, Ian Yu, Paulo Villegas, Marco Zocca, Sourab Mangrulkar, David Lansky, Huu Nguyen, Danish Contractor, Luis Villa, Jia Li, Dzmitry Bahdanau, Yacine Jernite, Sean Hughes, Daniel Fried, Arjun Guha, Harm de Vries, Leandro von Werra.
HerBERT (from Allegro.pl, AGH University of Science and Technology) released with the paper KLEJ: Comprehensive Benchmark for Polish Language Understanding by Piotr Rybak, Robert Mroczkowski, Janusz Tracz, Ireneusz Gawlik.
LongT5 (from Google AI) released with the paper LongT5: Efficient Text-To-Text Transformer for Long Sequences by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang.
LLaMA (from The FAIR team of Meta AI) released with the paper LLaMA: Open and Efficient Foundation Language Models by 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.
Llama2 (from The FAIR team of Meta AI) released with the paper Llama2: Open Foundation and Fine-Tuned Chat Models by Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushka rMishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing EllenTan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom.
M2M100 (from Facebook) released with the paper Beyond English-Centric Multilingual Machine Translation by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
MarianMT Machine translation models trained using OPUS data by Jörg Tiedemann. The Marian Framework is being developed by the Microsoft Translator Team.
mBART (from Facebook) released with the paper Multilingual Denoising Pre-training for Neural Machine Translation by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
mBART-50 (from Facebook) released with the paper Multilingual Translation with Extensible Multilingual Pretraining and Finetuning by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
MMS (from Facebook) released with the paper Scaling Speech Technology to 1,000+ Languages by Vineel Pratap, Andros Tjandra, Bowen Shi, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Zhaoheng Ni, Apoorv Vyas, Maryam Fazel-Zarandi, Alexei Baevski, Yossi Adi, Xiaohui Zhang, Wei-Ning Hsu, Alexis Conneau, Michael Auli.
MobileBERT (from CMU/Google Brain) released with the paper MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices by Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny Zhou.
MobileViT (from Apple) released with the paper MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer by Sachin Mehta and Mohammad Rastegari.
MPNet (from Microsoft Research) released with the paper MPNet: Masked and Permuted Pre-training for Language Understanding by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
MPT (from MosaiML) released with the repository llm-foundry by the MosaicML NLP Team.
MT5 (from Google AI) released with the paper mT5: A massively multilingual pre-trained text-to-text transformer by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
NLLB (from Meta) released with the paper No Language Left Behind: Scaling Human-Centered Machine Translation by the NLLB team.
OPT (from Meta AI) released with the paper OPT: Open Pre-trained Transformer Language Models by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al.
ResNet (from Microsoft Research) released with the paper Deep Residual Learning for Image Recognition by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
RoBERTa (from Facebook), released together with the paper RoBERTa: A Robustly Optimized BERT Pretraining Approach by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
SqueezeBERT (from Berkeley) released with the paper SqueezeBERT: What can computer vision teach NLP about efficient neural networks? by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
Swin Transformer (from Microsoft) released with the paper Swin Transformer: Hierarchical Vision Transformer using Shifted Windows by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
T5 (from Google AI) released with the paper Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
T5v1.1 (from Google AI) released in the repository google-research/text-to-text-transfer-transformer by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
Vision Transformer (ViT) (from Google AI) released with the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
Wav2Vec2 (from Facebook AI) released with the paper wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
WavLM (from Microsoft Research) released with the paper WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
Whisper (from OpenAI) released with the paper Robust Speech Recognition via Large-Scale Weak Supervision by Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever.
XLM (from Facebook) released together with the paper Cross-lingual Language Model Pretraining by Guillaume Lample and Alexis Conneau.
XLM-RoBERTa (from Facebook AI), released together with the paper Unsupervised Cross-lingual Representation Learning at Scale by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
YOLOS (from Huazhong University of Science & Technology) released with the paper You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection by Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu. |
https://huggingface.co/docs/timm | timm
timm is a library containing SOTA computer vision models, layers, utilities, optimizers, schedulers, data-loaders, augmentations, and training/evaluation scripts.
It comes packaged with >700 pretrained models, and is designed to be flexible and easy to use.
Read the quick start guide to get up and running with the timm library. You will learn how to load, discover, and use pretrained models included in the library. |
https://huggingface.co/docs/peft | PEFT
🤗 PEFT, or Parameter-Efficient Fine-Tuning (PEFT), is a library for efficiently adapting pre-trained language models (PLMs) to various downstream applications without fine-tuning all the model’s parameters. PEFT methods only fine-tune a small number of (extra) model parameters, significantly decreasing computational and storage costs because fine-tuning large-scale PLMs is prohibitively costly. Recent state-of-the-art PEFT techniques achieve performance comparable to that of full fine-tuning.
PEFT is seamlessly integrated with 🤗 Accelerate for large-scale models leveraging DeepSpeed and Big Model Inference.
Supported methods
LoRA: LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS
Prefix Tuning: Prefix-Tuning: Optimizing Continuous Prompts for Generation, P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks
P-Tuning: GPT Understands, Too
Prompt Tuning: The Power of Scale for Parameter-Efficient Prompt Tuning
AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning
LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention
IA3: Infused Adapter by Inhibiting and Amplifying Inner Activations
Supported models
The tables provided below list the PEFT methods and models supported for each task. To apply a particular PEFT method for a task, please refer to the corresponding Task guides.
Causal Language Modeling
Model LoRA Prefix Tuning P-Tuning Prompt Tuning IA3
GPT-2 ✅ ✅ ✅ ✅ ✅
Bloom ✅ ✅ ✅ ✅ ✅
OPT ✅ ✅ ✅ ✅ ✅
GPT-Neo ✅ ✅ ✅ ✅ ✅
GPT-J ✅ ✅ ✅ ✅ ✅
GPT-NeoX-20B ✅ ✅ ✅ ✅ ✅
LLaMA ✅ ✅ ✅ ✅ ✅
ChatGLM ✅ ✅ ✅ ✅ ✅
Conditional Generation
Model LoRA Prefix Tuning P-Tuning Prompt Tuning IA3
T5 ✅ ✅ ✅ ✅ ✅
BART ✅ ✅ ✅ ✅ ✅
Sequence Classification
Model LoRA Prefix Tuning P-Tuning Prompt Tuning IA3
BERT ✅ ✅ ✅ ✅ ✅
RoBERTa ✅ ✅ ✅ ✅ ✅
GPT-2 ✅ ✅ ✅ ✅
Bloom ✅ ✅ ✅ ✅
OPT ✅ ✅ ✅ ✅
GPT-Neo ✅ ✅ ✅ ✅
GPT-J ✅ ✅ ✅ ✅
Deberta ✅ ✅ ✅
Deberta-v2 ✅ ✅ ✅
Token Classification
Model LoRA Prefix Tuning P-Tuning Prompt Tuning IA3
BERT ✅ ✅
RoBERTa ✅ ✅
GPT-2 ✅ ✅
Bloom ✅ ✅
OPT ✅ ✅
GPT-Neo ✅ ✅
GPT-J ✅ ✅
Deberta ✅
Deberta-v2 ✅
Text-to-Image Generation
Model LoRA Prefix Tuning P-Tuning Prompt Tuning IA3
Stable Diffusion ✅
Image Classification
Model LoRA Prefix Tuning P-Tuning Prompt Tuning IA3
ViT ✅
Swin ✅
Image to text (Multi-modal models)
We have tested LoRA for ViT and Swin for fine-tuning on image classification. However, it should be possible to use LoRA for any ViT-based model from 🤗 Transformers. Check out the Image classification task guide to learn more. If you run into problems, please open an issue.
Model LoRA Prefix Tuning P-Tuning Prompt Tuning IA3
Blip-2 ✅
Semantic Segmentation
As with image-to-text models, you should be able to apply LoRA to any of the segmentation models. It’s worth noting that we haven’t tested this with every architecture yet. Therefore, if you come across any issues, kindly create an issue report.
Model LoRA Prefix Tuning P-Tuning Prompt Tuning IA3
SegFormer ✅ |
https://huggingface.co/tasks | Tasks
Hugging Face is the home for all Machine Learning tasks. Here you can find what you need to get started with a task: demos, use cases, models, datasets, and more!
Computer Vision
Natural Language Processing
Audio
Tabular
Multimodal
Reinforcement Learning |
https://huggingface.co/support | Thomas Wolf, creator of Transformers
Ross Wightman, creator of timm (SOTA computer vision)
Colin Raffel, first author of T5 from Google
Morgan Funtowicz, contributor to ONNX
Abhishek Thakur, worldwide expert on auto-ML
Victor Sanh, author of DistilBERT
Anthony Moi, creator of Tokenizers
Julien Simon, author of “Learn Amazon SageMaker”
Meg Mitchell, worldwide expert on bias and ethics in AI
+40 more experts |
https://huggingface.co/huggingface | 👋 Hi!
We are on a mission to democratize good machine learning, one commit at a time.
If that sounds like something you should be doing, why don't you join us!
For press enquiries, you can ✉️ contact our team here. |
https://huggingface.co/brand | Hugging Face · Brand assets
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Hugging Face is the collaboration platform for the machine learning community. The Hugging Face Hub works as a central place where anyone can share, explore, discover, and experiment with open-source ML. HF empowers the next generation of machine learning engineers, scientists, and end users to learn, collaborate and share their work to build an open and ethical AI future together. With the fast-growing community, some of the most used open-source ML libraries and tools, and a talented science team exploring the edge of tech, Hugging Face is at the heart of the AI revolution. |
https://huggingface.co/inference-endpoints | Machine Learning At Your Service
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https://huggingface.co/terms-of-service | Terms of Service
🗓 Effective Date: September 15, 2022
Thanks for using Hugging Face and being part of our awesome community! 🤗
We drafted the following Terms of Service to make your user experience as smooth, private and secure as possible. We are very much open to feedback - contact us at legal@huggingface.co with any question or concern!
Please read these Terms carefully as they contain important information about what we do and do not offer, and what you can and cannot do.
Whenever you want to use or purchase the Services that we provide at https://huggingface.co and related sites (the "Website"), these Terms of Service, together with our Supplemental Terms, notices and policies available at https://huggingface.co, and/or any other binding document that we provide and/or that you sign (the “Terms” or the “Agreement”) will apply to you.
In other words, these Terms are a binding agreement between us, Hugging Face, Inc. a Delaware corporation ("Hugging Face", "Company", "us", "we"), and You, whether you are a user ("User", "You") or a customer ("Customer", "you"). You should also carefully review all of our other policies available on our Website, including our Privacy Policy.
By accessing, using or purchasing the Services, you consent to all of these Terms and policies. So, if you do not agree with any of those, please do not access, use or purchase the Services.
We may change or update the Terms from time to time. Changes will be effective 10 days following posting on the Website. If you continue using the Services 10 days following such posting, that means you accept those changes.
We may also post and update supplemental terms for specific Services ("Supplemental Terms"), and such Supplemental Terms will also apply to you.
📚 A few definitions
Let's make sure we speak the same language!
"Account" is the account that you, or your entity, will create on the Website to access, use or purchase our Services. It must be secured by a strong password!
"Agreement" or "Terms" refer to all of the terms and conditions that apply between us. They include these Terms, Supplemental Terms, notices and policies available at https://huggingface.co, and/or any other binding document that we provide and/or that you sign, including but not limited to an Order Form, a Scope of Work or a Master Services Agreement.
"Inference API" refers to hosted services available on https://huggingface.co that let you, individuals, companies or organizations, run inference via application programming interface on machine learning models publicly or privately hosted on Hugging Face's model hub.
"Content" refers to any material posted, displayed, or accessed on our Website or Hub, including but not limited to code, data, text, graphics, images, applications, or software you, we, or any third party provide or make available.
"Dataset" refers to a structured collection of data samples used to train machine learning Models.
"Effective Date" refers to the last date of signature of the Agreement, or any other binding document.
"Inference Endpoint Service" refers to the Hugging Face Hub service through which the Customer can create, edit, manage and delete Managed Endpoints.
"Hugging Face" refers to Hugging Face Inc., which may perform its obligations through its affiliates, directors, subsidiaries, contractors, licensors, officers, agents and/or employees.
"Hugging Face Hub", or "Hub" refers to the hosting platform where Users can build, benchmark, share, version and deploy Repositories, which may include Models, Datasets and Machine Learning Applications.
"Hugging Face Open-Source Libraries" refers to the Hugging Face open-source software projects available at https://github.com/huggingface/, including Transformers, Datasets and Tokenizers.
"Infinity Service" refers to the containerized solution to deploy end-to-end optimized inference pipelines for state of the art Transformers Models, available at https://huggingface.co/infinity.
"Machine Learning Application" refers to a repository hosted on the Hub that allows a User to showcase Machine Learning experiments.
"Model" refers to a pre-trained machine learning model including algorithms and weights, which can be run to make predictions.
"Order Form" refers to the document shared by Hugging Face to the Customer describing the quantity of services ordered by the Customer and the fees payable for such services. Additional Order Forms may be negotiated between and executed by the Parties, and shall be incorporated into the Agreement.
"Organization" refers to a workspace representing a legal entity and/or several Users. A User can be part of multiple organizations.
"Premium Support" refers to qualified information or any other materials provided by Hugging Face via email or any other instant messaging or communication service to the Customer to address the Customer’s questions on the use and optimization of the Hugging Face Open-Source Libraries, Hugging Face or Customer Models.
"Repository" refers to a data structure which contains all of the project files and the entire revision history. A Repository may be public (i.e. anyone on the internet can see it, but only you or members of your organization can make changes) or private (i.e. only you or members of your organization can see and make changes to the repository).
"Services" refer to the products and/or services we offer or provide, and that you access, use or purchase. Services may include limited licenses or subscriptions to access or use certain offerings in accordance with these Terms, including use of Models, Datasets, Hugging Face Open-Sources Libraries, the Inference API, AutoTrain, Expert Acceleration Program, Infinity or other Content. Reference to "purchases" and/or "sales" mean a limited right to access and use a Service (not a transfer or any ownership right, title, or interest) in accordance with these Terms.
"User" refers to the individual person, company or organization that accesses, receives, or uses the Services. That's you!
👩💻 Your Use of the Services
Here are the Services we offer, and how you should use them.
We provide Services in the field of machine learning, here is the list:
Open-Source Libraries: including Transformers, Datasets and Tokenizers
Hugging Face public Hub: where you can build, benchmark, share, version and deploy Models, Datasets and Machine Learning Applications accessible by all Users
Hugging Face private Hub: where you can build, benchmark, share, version and deploy Models, Datasets and Machine Learning Applications, that are only accessible by You or your Organization(s)
Inference API Service: where you or your organization(s) can run inference via application programming interface on machine learning models publicly or privately hosted on our Model Hub
AutoTrain premium Service: create state of the art Models from your own training data, everything being automatically hosted privately on the Model Hub. You can then share them publicly and/or serve them through the Inference API Service for example.
Expert Acceleration Program: get Premium Support on the use of our open source and all our services and/or products.
Infinity Service: deploy end-to-end optimized inference pipelines for state of the art Transformers Models.
Hardware Partner Program: access State of the Art hardware and hardware-specific machine learning optimization techniques for production performance.
Inference Endpoints: easily deploy machine learning models on dedicated, secure and autoscaling infrastructure
🔜 More awesome Services. Stay tuned!! 🚀
You must use our Services in strict compliance with these Terms, the Supplemental Terms for each Service, all of our policies available on our Website, and all applicable laws or regulations in the relevant jurisdiction(s).
We may at any time modify, suspend, or discontinue, temporarily or permanently, the Services (or any part thereof) with or without notice. You agree that we will not be liable to you or to any third party for any modification, suspension or discontinuance of the Services.
👤 Your Account
Pretty basic, but necessary before accessing some of our Services!
In order to create an Account for yourself or for your Organization on our Website, you must be a natural person of at least age 13, or a legal entity duly registered. If you decide to create an Account for your Organization, you represent that you have the authority to act on behalf of your Organization and bind your Organization to these Terms.
When you create your Account, we are going to ask you to provide us with some basic information, such as your email address password, username, full name, and other optional information such as an avatar, your interests, usernames to your third-party social networks, or payment information if you decide to purchase one of our paid Services. All information must be accurate and valid.
🔒Security is very important to us, and we need every member of our community to cooperate. You are responsible for maintaining the confidentiality and security of your password necessary for accessing your Account and the Services. You may not disclose your password to any third party, and you are solely responsible for any action taken with your Account. You must notify us immediately of any actual or suspected breach of security on your Account, loss or compromise of password, or unauthorized use of your Account.
💬 Your Content
Wondering about what we do with your Content?
You are solely responsible for the Content you post, publish, display or otherwise make available on our Website, and for any other action or omission that results from your use of the Services (including our Content or other user's Content), or the use by a person or an entity that you have authorized under your Account.
You represent and warrant that you have ownership, control, and responsibility for the Content you post or otherwise make available on our Website, or otherwise have the right to do so. Your Content must not be misleading or unlawful, and must not violate any of these Terms, applicable law and regulation, or infringe or misappropriate any rights of any person or entity. We may remove your Content at any time, at our sole discretion, if we have a concern about your Content.
You own the Content you create! We will not sell your Content, nor will we use it in any other way as permitted under these Terms. However, by posting your Content or otherwise making it available on our Website, you must be aware that:
You hereby grant us a worldwide, royalty-free and non-exclusive license to use, display, publish, reproduce, distribute, and make derivative works of such Content to provide Services and as otherwise permitted under these Terms and our Privacy Policy; and,
Your Content will be viewed by others, and therefore:
If you decide to set your Repository public, you grant each User a perpetual, irrevocable, worldwide, royalty-free, non-exclusive license to use, display, publish, reproduce, distribute, and make derivative works of your Content through our Services and functionalities;
If you decide to set your Repository private, we will use reasonable and appropriate measures designed to keep your Content confidential, and protected from any unauthorized access or disclosure. However, we may access or share your private information pursuant to the terms set forth in our Privacy Policy.
When Content contains notice of a reasonable and customary license, (such as an open source license) such Content is intended to remain under the terms of such license when further accessed, distributed, or used. Neither party is permitted to remove reference to any such license.
Any Content you download, access or use from us or another User, is at your own risk and subject to these Terms and/or the terms accompanying such Content.
💰 Payment
We work hard to make our Services most useful to you, and we thank you for your trust and support!
Our plans and fees payable for the use of the Services you decide to purchase are available at https://huggingface.co/pricing. You may decide to choose a custom plan, in which case the payable fees and payment terms will be subject to further discussions and mutual agreement with us, and will be specified in the applicable Services Agreement, Order Form or any other binding document signed between us.
All fees are exclusive of any applicable taxes, which You are solely responsible to pay.
We reserve the right to adjust our pricing from time to time and at our sole discretion. In such event, prices will remain fixed during the term of your initial subscription, and adjusted fees will be applicable only after the term of your new subscription.
The plan is billed in advance on a monthly basis, and usage based fees, which apply if you go over your allotted usage, will be billed as they go.
💳 Payment is processed on the Website, which includes a third-party payment or credit card processor's services. The payment processor's or credit card company's agreement governs your use of the designated account or credit card you provide, and you must refer to that agreement and not these Terms to determine your rights and liabilities relating to such agreement, account and activities. By providing us with your account or credit card number and associated payment information, you agree that we are authorized to immediately invoice your account for all fees due and payable and that no additional notice or consent is required. You agree to immediately notify us of any change in your billing address or the account or credit card use for the payment. All fees are non-refundable and exclusive of any applicable taxes, which the Customer is solely responsible to pay. You will indemnify us for any taxes relating to your purchase or use of the Services, except for taxes relating to our income.
For certain Services, the Service Fees and payment terms may be specified in the Supplemental Terms and/or in any other binding document signed between us, including but not limited to an Order Form, a Scope of Work, or a Master Service Agreement, which are fully incorporated into the Agreement between us.
😔 Termination
Don't go! But if you do, here is what happens after termination.
You may decide to cancel your Account whenever you want, at your sole discretion.
We may do the same, and we reserve the right to suspend or terminate your access to the Services anytime with or without cause, and at our own discretion, with or without notice.
Upon cancellation of your Account, we will use commercially reasonable efforts to delete your information and Content of your own Repositories, whether public or private, within 90 days. We will not delete the Content that you contributed to other Users' Repositories, or copies made by us or other Users.
We also reserve the right to retain your information for legal or regulatory compliance, pursuant to standard archiving, recovery, and back-up processes and practices, and pursuant to our Privacy Policy.
For certain Services, the Service Term and causes for termination may be specified in the Supplemental Terms and/or in any other binding document signed between us, including but not limited to an Order Form, a Scope of Work, or a Master Service Agreement, which are fully incorporated into the Agreement between us.
🤐 Confidentiality
What is confidential, stays confidential.
All information relating to these Terms and/or during negotiations before the execution of any binding document that we may share between us shall be treated as confidential (“Confidential Information”).
During the Service Term, and for at least one (1) year thereafter, we expressly agree (i) to maintain the strict confidentiality of such Confidential Information, and to refrain from disclosing such Confidential Information to any third party, except as authorized by the original disclosing party in writing; (ii) to use such Confidential Information only for the purposes of performing its obligations or exercising its rights under this Agreement; and (iii) to use at least a reasonable standard of care in protecting the Confidential Information.
These restrictions on the use or disclosure of Confidential Information shall not apply to any Confidential Information (i) which has been independently developed by the receiving Party, as evidenced by its written records, (ii) which has been lawfully received free of restriction from another source having the right to furnish such Confidential Information; or (iii) after it has become generally available to the public without breach of this section by the receiving Party; or (iv) which at the time of disclosure was already known to the receiving Party, and free of restriction as evidenced by documentation in such Party's possession; or (v) which the disclosing Party confirms in writing is free of such restrictions; or, (vi) which is required to be disclosed in any legal proceeding, upon express request from a governmental or regulatory agency, and/or pursuant to a requirement of law (and only with respect to such disclosure).
Each of us may disclose Confidential Information only to our employees, agents or subcontractors who need it in order to exercise rights or perform obligations under the Agreement, and who are required to protect it against unauthorized disclosure or use in a manner no less protective than required under the Agreement.
Confidential Information is and shall at all times remain the exclusive property of the disclosing Party.
Upon termination or expiration of the Agreement, we ask you to promptly destroy or return all Confidential Information, and we will do the same if you ask us to do so.
💡 Intellectual Property
Let's give credit where it is due, and protect our intellectual property rights!
Proprietary Rights. We retain ownership of all of our intellectual property rights related to the Website and the Services, including all improvements to the Services. All materials that we produce, including the Website, design, code, graphics, interfaces, trademarks, and logo shall remain our exclusive property. You may not alter, reproduce, republish, license any of our proprietary materials, unless we expressly give you a written permission to do so. All rights not expressly granted are reserved and retained by us.
Nothing in these Terms is intended to limit our use of our knowledge, skills, experience, ideas, concepts, know-how and/or techniques developed or learned at any time, without limitation. If you provide us feedback regarding the use, operation, performance, or functionality of our Website, Services, or business (collectively, "Feedback"), you hereby grant us a perpetual, irrevocable, worldwide, royalty-free, and non-exclusive right and license to exploit and commercialize the Feedback, improve the Services, and develop and/or commercialize new offerings, which we will solely and exclusively own. In addition and subject to our Privacy Policy, we may aggregate, anonymize, or otherwise learn from data relating to your use of the Services, and use the foregoing to improve those Services.
DMCA Policy. We comply with the Digital Millennium Copyright Act Policy! If you have any claims that any content on our Website violates or infringes your intellectual property rights, you may send your complaint to dmca@huggingface.co with detailed and accurate information supporting your claim. You also represent and warrant that you will not knowingly provide misleading information to support your claim.
Open Source. Certain items provided with the Services may be subject to "open source" or "creative commons" or other similar licenses (collectively, "Open Source"). The Open Source license terms are not intended to be replaced or overridden by the license and other terms of these Terms; however, the limitations of liabilities, disclaimers, and this provision apply to any such Open Source. Nothing in these Terms limit your rights under, or grants you rights that supersede, the terms and conditions of any applicable Open Source license. If we (or you) make modifications to Open Source, and if the applicable Open Source requires that such modifications be made available, and we (or you) do not already publish such modifications via the applicable open source community, then modifications will be available on applicable websites.
Supplemental Terms. Certain Services may be governed by specific intellectual property terms which are stated in the Supplemental Terms and/or in any other binding document signed between us, and which are fully incorporated into the Agreement between us.
⛔ Privacy
Your privacy is paramount to us, and here is how we respect it.
We will provide the Services in accordance with our Privacy Policy available at: https://huggingface.co/privacy.
🎓 Liability
Our worst case scenario.
Neither of us (or any of our affiliates, subsidiaries, contractors, licensors, officers, directors, agents, or employees ("Related Parties")) will be liable for any indirect, incidental, consequential, punitive, special, or other similar damages, including loss of revenue, profits, data, benefits, or savings, whether or not due to the fault or negligence of the company or related parties, and regardless of whether either of us or our related parties have been advised of the possibility of such damages or losses.
Either Party’s (and each Related Party’s) aggregate liability to the other Party or any third party in any circumstance will not exceed the amount that you paid us during the 12-month period immediately preceding the last claim (or $50 if relating to a free service). This limitation will not apply to (i) either party’s liability from fraud, gross negligence, recklessness, or willful or criminal misconduct, (ii) your liability for infringement of our intellectual property rights, (iii) your liability for breach of the confidentiality section, or (iv) amounts you owe us for the service made available as per your payment obligations.
👩⚖️ Indemnity
Your worst-case scenario.
You are solely and exclusively responsible for your use of the Services!
In this regard, you agree to indemnify, defend and hold harmless us and Related Parties from all claims, liability, and expenses, including attorney's fees, arising out or in connection with your use of (or inability to use) the Services, including but not limited to your violation of these Terms, applicable law or regulation, any Content or data posted or used by you, or any other party's use of any Service with your credentials, unless arising directly from Hugging Face’s fraud, gross negligence, recklessness, or willful or criminal misconduct, provided that we provide you with (i) a prompt written notice of the claim, demand, suit or proceeding, (ii) sole control of the defense and settlement of the claim, demand, suit or proceeding, and (iii) all reasonable assistance and cooperation in connection with the defense and settlement of the claim, at its own expense.
🙅♂️ Disclaimer of Warranties
There are certain promises that we cannot make...
We provide Services that you may or may not decide to access, use or purchase. In this regard, we make no warranties or representations about these Services.
In other words, except as expressly provided otherwise herein, and to the fullest extent permitted by law, the Services and Content are provided "as is" and "as available".
We disclaim all warranties or guarantees of any kind, express or implied, whether arising under any law or from any usage in trade, or otherwise, including but not limited to the implied warranties of merchantability, non-infringement, quiet enjoyment, fitness for a particular purpose, or otherwise.
We further disclaim all warranties or guarantees about the accuracy, reliability or benefits of any Services, artificial intelligence, Models or any other technology or Content, or that the Services or Content will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise.
You will be solely responsible for any damage resulting from your use of or access to the Services, your downloading of Content or data, or use of any other material provided by or through the Services.
📃 Miscellaneous
Almost done! These are important legal terms that you should also read.
Governing law and dispute resolution. These Terms and all matters regarding their interpretation and/or enforcement are governed by the Law of the State of New York, excluding its choice of law rules. If a dispute or claim relating to these Terms arises, we each agree to make a reasonable and good faith effort to agree on an out-of-court solution and to resolve the dispute. If no out-of-court settlement is reached, any related action, lawsuit, or proceeding must be brought and adjudicated exclusively by state or federal courts located in the city of New York, United States of America. Any claim, action, suit or proceeding relating to these Terms must be brought by you within one year of the event that gave rise to the claim or such claim is hereby waived to the maximum extent permitted by law.
Assignment. We may assign or transfer all or part of our rights and obligations under these Terms to an affiliate, successor or any other entity or person without obtaining your prior written consent. Conversely, you may not assign or transfer all or part of your rights and obligations under Terms without obtaining our prior written consent.
Subcontracting. We may subcontract all or part of our obligations under these Terms at our own discretion, and without notifying you. Nothing to worry, since we will bear the same degree of responsibility for acts and omissions of the subcontractors acting on our behalf in the performance of their obligations under these Terms as they would bear if such acts and omissions were performed by us directly.
Changes in law or regulation. If there is any change in law or regulation that would materially restrict or prohibit our ability to provide the Services pursuant to these Terms, we may suspend or cancel the Services, or otherwise amend these Terms.
Export Control and Sanctions. Any Service provided pursuant to these Terms may be subject to export control and sanctions laws of the U.S. and/or other applicable jurisdictions. Therefore, you may only access and use the Service in compliance with U.S. and other applicable export control and sanctions laws and regulations.
Headings. Headings used throughout these Terms are used for convenience and reference only, and have no legal effect, nor shall affect the interpretation of these Terms.
Entire Agreement. These Terms, together with all of the terms, policies and notices available at https://huggingface.co, or any other binding documents we provide, or agreements provided or executed by us, constitute the entire agreement between us, and supersedes all previous negotiations, proposals, commitments, writings, oral statement and understanding of any nature whatsoever. Any standard form purchase order or similar document you provided us or reference in any payment is expressly rejected if it differs from, or adds to, these Terms.
Order of Precedence. In the event of a conflict between provisions arising out of any documents included in the Agreement, the order of precedence will be as follows, unless expressly stated otherwise: (i) the applicable Order Form if any; (ii) the applicable Scope of Work if any; (iii) any other binding document signed between us; (iv) the Supplemental Terms; (v) these Terms of Service; (vi) all other documents or policies incorporated by reference in the Agreement.
Severability. If any provision of these Terms, by action of law or for any other reason, is held to be prohibited, invalid, void or unenforceable in any relevant jurisdiction, such provision will be stricken, and the remaining provisions of these Terms will remain in full force and effect.
No Waiver. The failure, in one or more instances, to perform any of the terms or conditions of these Terms, or to exercise any right hereunder, shall not be construed as a waiver of the future performance of any such terms or conditions, or the future exercise of such right, and the obligations of under these Terms with respect to such performance shall continue in full force and effect.
Survival. The termination or expiration of these Terms shall not relieve from any obligation (i) that may have arisen prior to such termination or expiration, or (ii) that needs to survive termination or expiration in order to give full effect to its meaning, including without limitation payment obligations, confidentiality obligations, limitation of liability, warranty disclaimers, indemnities, governing law and dispute resolution, miscellaneous, and definitions.
Execution. Each Party represents and warrants that (i) it possesses the legal right and capacity to enter into, execute, deliver and perform the Agreement; (ii) the individual signing the Agreement on the Party’s behalf has full power and authority to bind the Party to the terms and conditions set out in this Agreement; and (iii) the Agreement is a valid and binding obligation of that Party. You agree that an electronic signature shall have the same force and effect as manual signatures.
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https://huggingface.co/privacy | Hugging Face Privacy Policy
🗓 Effective Date: March 28, 2023
We have implemented this Privacy Policy because your privacy is important to us. This Privacy Policy (the “Policy”) describes the type of information that Hugging Face, Inc. (the “Company”) gathers from users of the Hugging Face services (the “Services”), and how the Company uses that information.
The Policy is part of the Company Terms of Use. The Policy applies to all users of the Services (“Users”).
The Company occasionally collects Personal Information from Users. “Personal Information” means any information that can, alone or associated with other information, be used to identify an individual, for instance by reference of a name, a username, an email address, an IP address, a photograph or others.
By using the Services, you consent to the terms of the Policy and to our processing of Personal Information in the manner and for the purposes set forth herein. If you do not agree with the Policy, please do not use the Services.
The Company reserves the right, at its sole discretion, to change the Policy at any time, which change will be effective 10 days following posting the revision to the Policy on the Hugging Face website (the “Website”). Your continued use of the Services 10 days following such posting means you accept those changes.
1. INFORMATION WE COLLECT
The Company collects the following information, some of which might be Personal Information.
A. Information you provide directly
The Company collects information directly provided by Users as part of using the Services, such as:
information provided as part of setting up an account on the Website: email address, password, username, full name, and other optional information such as an avatar, your interests, or usernames to your third-party social networks,
payment information provided, if you decide to upgrade your user or organization account: credit card information,
other information and materials that you decide to post on the Website (e.g., the discussion forum, or other),
communications between you and the Company, as part of using the Services.
At any time during your use of the Services, you may decide to share some information or content publicly or privately.
If you decide to share your information or content publicly, and if you decide to include Personal Information, you understand that anyone may view this information.
If you decide to keep your information private and control the access to it, you understand that only the users that you authorize will view this information. The Company also reserves the right to access this information with your consent, or without your consent only for the purposes of pursuing legitimate interests such as maintaining security on its Services or complying with any legal or regulatory obligations.
B. Information we collect from third parties
We may collect Information from third parties that help us deliver the Services or process information.
C. Information we automatically collect from your use of the Services
The Company automatically records information from your use of the Services such as:
information about your Use of the Services, your session (date, location), your IP address,
information from cookies, especially your login information, your preferences,
information about your device: type, model, version, operating system, browser
D. Cookies
We use cookies only for the purposes of delivering, updating, monitoring, improving the Services, and maintaining security on our Services by detecting, preventing and responding to any type of threats or incidents.
We may collect Information through those cookies. If you do not wish to accept these cookies and you decide to disable them, you will not be able to access and use the Services.
E. “Do Not Track”
On September 27, 2013, California enacted A.B. 370, amending the California Online Privacy Protection Act to require website operators to disclose how they respond to "Do Not Track Signals"; and whether third parties collect personally identifiable information about users when they use online services.
The Company honors "do not track" signals and does not track, use cookies, or use advertising when a “do not track” mechanism is in place.
The Company does not authorize the collection of personally identifiable information from our users for third party use through advertising technologies without separate member consent.
California Civil Code Section 1798.83 also permits customers who are California residents to request certain information regarding Our disclosure of Personal Information to third parties for direct marketing purposes. To make such a request, please send an email to privacy@huggingface.co. Please note that the Company is only required to respond to one request per customer each year.
2. USE OF INFORMATION
Purposes of the use of Information
The Company may use information from Users for the following purposes:
to deliver the Services, which may include the creation of Your account, the display of Your profile or Your content, or if applicable the upgrading of Your account to a paid account,
to operate and improve the Services by providing you with more effective customer service, making the Services easier to use by eliminating the need for you to enter the same information repeatedly; performing research and analysis aimed at improving the Services, or other products and technologies of the Company; automatically updating the Services; diagnosing or fixing problems with the Services,
to conduct analysis or research on the Services or any topics related to it, for business operations or scientific purposes,
to communicate with you, especially through the sending of welcome emails, information on technical service issues, security announcements, information of new services available, legal notices, response to your requests, or any other information that we think might interest or be relevant to you,
to ensure and maintain security on our Services or Website, which may include detecting, preventing, investigating or otherwise addressing fraud or security issues,
to protect against harm to the rights, property or safety of the Company, our Users, yourself or the public,
to enforce any applicable terms of service or agreement, including investigations of potential violations thereof,
to comply with any applicable law, regulation, legal process or governmental requests.
B. Grounds for the use of Information
Pursuant to applicable data protection laws, and especially the European Union’s General Data Protection Regulation (EU) 2016/679 (the “GDPR”), Hugging Face remains under an obligation to notify the Users about the legal basis on which their Personal Information is processed.
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https://huggingface.co/monster-labs/control_v1p_sd15_qrcode_monster | Controlnet QR Code Monster v2 For SD-1.5
Model Description
This model is made to generate creative QR codes that still scan. Keep in mind that not all generated codes might be readable, but you can try different parameters and prompts to get the desired results.
NEW VERSION
Introducing the upgraded version of our model - Controlnet QR code Monster v2. V2 is a huge upgrade over v1, for scannability AND creativity.
QR codes can now seamlessly blend the image by using a gray-colored background (#808080).
As with the former version, the readability of some generated codes may vary, however playing around with parameters and prompts could yield better results.
You can find in in the v2/ subfolder.
How to Use
Condition: QR codes are passed as condition images with a module size of 16px. Use a higher error correction level to make it easier to read (sometimes a lower level can be easier to read if smaller in size). Use a gray background for the rest of the image to make the code integrate better.
Prompts: Use a prompt to guide the QR code generation. The output will highly depend on the given prompt. Some seem to be really easily accepted by the qr code process, some will require careful tweaking to get good results.
Controlnet guidance scale: Set the controlnet guidance scale value:
High values: The generated QR code will be more readable.
Low values: The generated QR code will be more creative.
Tips
For an optimally readable output, try generating multiple QR codes with similar parameters, then choose the best ones.
Use the Image-to-Image feature to improve the readability of a generated QR code:
Decrease the denoising strength to retain more of the original image.
Increase the controlnet guidance scale value for better readability. A typical workflow for "saving" a code would be : Max out the guidance scale and minimize the denoising strength, then bump the strength until the code scans.
Example Outputs
Here are some examples of creative, yet scannable QR codes produced by our model:
Feel free to experiment with prompts, parameters, and the Image-to-Image feature to achieve the desired QR code output. Good luck and have fun! |
https://huggingface.co/mistralai/Mistral-7B-v0.1 | Model Card for Mistral-7B-v0.1
The Mistral-7B-v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters. Mistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks we tested.
For full details of this model please read our Release blog post
Model Architecture
Mistral-7B-v0.1 is a transformer model, with the following architecture choices:
Grouped-Query Attention
Sliding-Window Attention
Byte-fallback BPE tokenizer
Troubleshooting
If you see the following error:
Traceback (most recent call last): File "", line 1, in File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained config, kwargs = AutoConfig.from_pretrained( File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained config_class = CONFIG_MAPPING[config_dict["model_type"]] File "/transformers/models/auto/configuration_auto.py", line 723, in getitem raise KeyError(key) KeyError: 'mistral'
Installing transformers from source should solve the issue:
pip install git+https://github.com/huggingface/transformers
This should not be required after transformers-v4.33.4.
Notice
Mistral 7B is a pretrained base model and therefore does not have any moderation mechanisms.
The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed. |
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https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0 | SD-XL 1.0-base Model Card
Model
SDXL consists of an ensemble of experts pipeline for latent diffusion: In a first step, the base model is used to generate (noisy) latents, which are then further processed with a refinement model (available here: https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/) specialized for the final denoising steps. Note that the base model can be used as a standalone module.
Alternatively, we can use a two-stage pipeline as follows: First, the base model is used to generate latents of the desired output size. In the second step, we use a specialized high-resolution model and apply a technique called SDEdit (https://arxiv.org/abs/2108.01073, also known as "img2img") to the latents generated in the first step, using the same prompt. This technique is slightly slower than the first one, as it requires more function evaluations.
Source code is available at https://github.com/Stability-AI/generative-models .
Model Description
Developed by: Stability AI
Model type: Diffusion-based text-to-image generative model
License: CreativeML Open RAIL++-M License
Model Description: This is a model that can be used to generate and modify images based on text prompts. It is a Latent Diffusion Model that uses two fixed, pretrained text encoders (OpenCLIP-ViT/G and CLIP-ViT/L).
Resources for more information: Check out our GitHub Repository and the SDXL report on arXiv.
Model Sources
For research purposes, we recommend our generative-models Github repository (https://github.com/Stability-AI/generative-models), which implements the most popular diffusion frameworks (both training and inference) and for which new functionalities like distillation will be added over time. Clipdrop provides free SDXL inference.
Repository: https://github.com/Stability-AI/generative-models
Demo: https://clipdrop.co/stable-diffusion
Evaluation
The chart above evaluates user preference for SDXL (with and without refinement) over SDXL 0.9 and Stable Diffusion 1.5 and 2.1. The SDXL base model performs significantly better than the previous variants, and the model combined with the refinement module achieves the best overall performance.
🧨 Diffusers
Make sure to upgrade diffusers to >= 0.19.0:
pip install diffusers --upgrade
In addition make sure to install transformers, safetensors, accelerate as well as the invisible watermark:
pip install invisible_watermark transformers accelerate safetensors
To just use the base model, you can run:
from diffusers import DiffusionPipeline import torch pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, use_safetensors=True, variant="fp16") pipe.to("cuda") # if using torch < 2.0 # pipe.enable_xformers_memory_efficient_attention() prompt = "An astronaut riding a green horse" images = pipe(prompt=prompt).images[0]
To use the whole base + refiner pipeline as an ensemble of experts you can run:
from diffusers import DiffusionPipeline import torch # load both base & refiner base = DiffusionPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True ) base.to("cuda") refiner = DiffusionPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-refiner-1.0", text_encoder_2=base.text_encoder_2, vae=base.vae, torch_dtype=torch.float16, use_safetensors=True, variant="fp16", ) refiner.to("cuda") # Define how many steps and what % of steps to be run on each experts (80/20) here n_steps = 40 high_noise_frac = 0.8 prompt = "A majestic lion jumping from a big stone at night" # run both experts image = base( prompt=prompt, num_inference_steps=n_steps, denoising_end=high_noise_frac, output_type="latent", ).images image = refiner( prompt=prompt, num_inference_steps=n_steps, denoising_start=high_noise_frac, image=image, ).images[0]
When using torch >= 2.0, you can improve the inference speed by 20-30% with torch.compile. Simple wrap the unet with torch compile before running the pipeline:
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
If you are limited by GPU VRAM, you can enable cpu offloading by calling pipe.enable_model_cpu_offload instead of .to("cuda"):
- pipe.to("cuda") + pipe.enable_model_cpu_offload()
For more information on how to use Stable Diffusion XL with diffusers, please have a look at the Stable Diffusion XL Docs.
Optimum
Optimum provides a Stable Diffusion pipeline compatible with both OpenVINO and ONNX Runtime.
OpenVINO
To install Optimum with the dependencies required for OpenVINO :
pip install optimum[openvino]
To load an OpenVINO model and run inference with OpenVINO Runtime, you need to replace StableDiffusionXLPipeline with Optimum OVStableDiffusionXLPipeline. In case you want to load a PyTorch model and convert it to the OpenVINO format on-the-fly, you can set export=True.
- from diffusers import StableDiffusionPipeline + from optimum.intel import OVStableDiffusionPipeline model_id = "stabilityai/stable-diffusion-xl-base-1.0" - pipeline = StableDiffusionPipeline.from_pretrained(model_id) + pipeline = OVStableDiffusionPipeline.from_pretrained(model_id) prompt = "A majestic lion jumping from a big stone at night" image = pipeline(prompt).images[0]
You can find more examples (such as static reshaping and model compilation) in optimum documentation.
ONNX
To install Optimum with the dependencies required for ONNX Runtime inference :
pip install optimum[onnxruntime]
To load an ONNX model and run inference with ONNX Runtime, you need to replace StableDiffusionXLPipeline with Optimum ORTStableDiffusionXLPipeline. In case you want to load a PyTorch model and convert it to the ONNX format on-the-fly, you can set export=True.
- from diffusers import StableDiffusionPipeline + from optimum.onnxruntime import ORTStableDiffusionPipeline model_id = "stabilityai/stable-diffusion-xl-base-1.0" - pipeline = StableDiffusionPipeline.from_pretrained(model_id) + pipeline = ORTStableDiffusionPipeline.from_pretrained(model_id) prompt = "A majestic lion jumping from a big stone at night" image = pipeline(prompt).images[0]
You can find more examples in optimum documentation.
Uses
Direct Use
The model is intended for research purposes only. Possible research areas and tasks include
Generation of artworks and use in design and other artistic processes.
Applications in educational or creative tools.
Research on generative models.
Safe deployment of models which have the potential to generate harmful content.
Probing and understanding the limitations and biases of generative models.
Excluded uses are described below.
Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
Limitations and Bias
Limitations
The model does not achieve perfect photorealism
The model cannot render legible text
The model struggles with more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
Faces and people in general may not be generated properly.
The autoencoding part of the model is lossy.
Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. |
https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1 | Model Card for Mistral-7B-Instruct-v0.1
The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the Mistral-7B-v0.1 generative text model using a variety of publicly available conversation datasets.
For full details of this model please read our release blog post
Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by [INST] and [\INST] tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
E.g.
text = "<s>[INST] What is your favourite condiment? [/INST]" "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> " "[INST] Do you have mayonnaise recipes? [/INST]"
This format is available as a chat template via the apply_chat_template() method:
from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") messages = [ {"role": "user", "content": "What is your favourite condiment?"}, {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, {"role": "user", "content": "Do you have mayonnaise recipes?"} ] encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt") model_inputs = encodeds.to(device) model.to(device) generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True) decoded = tokenizer.batch_decode(generated_ids) print(decoded[0])
Model Architecture
This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
Grouped-Query Attention
Sliding-Window Attention
Byte-fallback BPE tokenizer
Troubleshooting
If you see the following error:
Traceback (most recent call last): File "", line 1, in File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained config, kwargs = AutoConfig.from_pretrained( File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained config_class = CONFIG_MAPPING[config_dict["model_type"]] File "/transformers/models/auto/configuration_auto.py", line 723, in getitem raise KeyError(key) KeyError: 'mistral'
Installing transformers from source should solve the issue pip install git+https://github.com/huggingface/transformers
This should not be required after transformers-v4.33.4.
Limitations
The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed. |
https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF | Mistral 7B Instruct v0.1 - GGUF
Model creator: Mistral AI
Original model: Mistral 7B Instruct v0.1
Description
This repo contains GGUF format model files for Mistral AI's Mistral 7B Instruct v0.1.
About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplate list of clients and libraries that are known to support GGUF:
llama.cpp. The source project for GGUF. Offers a CLI and a server option.
text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.
Faraday.dev, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.
Repositories available
AWQ model(s) for GPU inference.
GPTQ models for GPU inference, with multiple quantisation parameter options.
2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference
Mistral AI's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions
Prompt template: Mistral
<s>[INST] {prompt} [/INST]
Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit d0cee0d
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
Sequence length note: The model will work at sequence lengths of 4096, or lower. GGUF does not yet have support for the new sliding window sequence length mode, so longer sequence lengths are not supported.
Explanation of quantisation methods
Click to see details
The new methods available are:
GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
Provided files
Name Quant method Bits Size Max RAM required Use case
mistral-7b-instruct-v0.1.Q2_K.gguf Q2_K 2 3.08 GB 5.58 GB smallest, significant quality loss - not recommended for most purposes
mistral-7b-instruct-v0.1.Q3_K_S.gguf Q3_K_S 3 3.16 GB 5.66 GB very small, high quality loss
mistral-7b-instruct-v0.1.Q3_K_M.gguf Q3_K_M 3 3.52 GB 6.02 GB very small, high quality loss
mistral-7b-instruct-v0.1.Q3_K_L.gguf Q3_K_L 3 3.82 GB 6.32 GB small, substantial quality loss
mistral-7b-instruct-v0.1.Q4_0.gguf Q4_0 4 4.11 GB 6.61 GB legacy; small, very high quality loss - prefer using Q3_K_M
mistral-7b-instruct-v0.1.Q4_K_S.gguf Q4_K_S 4 4.14 GB 6.64 GB small, greater quality loss
mistral-7b-instruct-v0.1.Q4_K_M.gguf Q4_K_M 4 4.37 GB 6.87 GB medium, balanced quality - recommended
mistral-7b-instruct-v0.1.Q5_0.gguf Q5_0 5 5.00 GB 7.50 GB legacy; medium, balanced quality - prefer using Q4_K_M
mistral-7b-instruct-v0.1.Q5_K_S.gguf Q5_K_S 5 5.00 GB 7.50 GB large, low quality loss - recommended
mistral-7b-instruct-v0.1.Q5_K_M.gguf Q5_K_M 5 5.13 GB 7.63 GB large, very low quality loss - recommended
mistral-7b-instruct-v0.1.Q6_K.gguf Q6_K 6 5.94 GB 8.44 GB very large, extremely low quality loss
mistral-7b-instruct-v0.1.Q8_0.gguf Q8_0 8 7.70 GB 10.20 GB very large, extremely low quality loss - not recommended
Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
How to download GGUF files
Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
LM Studio
LoLLMS Web UI
Faraday.dev
In text-generation-webui
Under Download Model, you can enter the model repo: TheBloke/Mistral-7B-Instruct-v0.1-GGUF and below it, a specific filename to download, such as: mistral-7b-instruct-v0.1.Q4_K_M.gguf.
Then click Download.
On the command line, including multiple files at once
I recommend using the huggingface-hub Python library:
pip3 install huggingface-hub
Then you can download any individual model file to the current directory, at high speed, with a command like this:
huggingface-cli download TheBloke/Mistral-7B-Instruct-v0.1-GGUF mistral-7b-instruct-v0.1.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
More advanced huggingface-cli download usage
You can also download multiple files at once with a pattern:
huggingface-cli download TheBloke/Mistral-7B-Instruct-v0.1-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
For more documentation on downloading with huggingface-cli, please see: HF -> Hub Python Library -> Download files -> Download from the CLI.
To accelerate downloads on fast connections (1Gbit/s or higher), install hf_transfer:
pip3 install hf_transfer
And set environment variable HF_HUB_ENABLE_HF_TRANSFER to 1:
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Mistral-7B-Instruct-v0.1-GGUF mistral-7b-instruct-v0.1.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
Windows Command Line users: You can set the environment variable by running set HF_HUB_ENABLE_HF_TRANSFER=1 before the download command.
Example llama.cpp command
Make sure you are using llama.cpp from commit d0cee0d or later.
./main -ngl 32 -m mistral-7b-instruct-v0.1.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<s>[INST]{prompt} [/INST]"
Change -ngl 32 to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Sequence length can be 4096 or lower. Mistral's sliding window sequence length is not yet supported in llama.cpp, so do not use sequence lengths longer than 4096.
If you want to have a chat-style conversation, replace the -p <PROMPT> argument with -i -ins
For other parameters and how to use them, please refer to the llama.cpp documentation
How to run in text-generation-webui
Further instructions here: text-generation-webui/docs/llama.cpp.md.
How to run from Python code
You can use GGUF models from Python using the llama-cpp-python or ctransformers libraries.
How to load this model in Python code, using ctransformers
I have not tested ctransformers with Mistral models. It may work, but will require that you set the model_type to llama for now, until ctransformers updates with specific support.
First install the package
Run one of the following commands, according to your system:
# Base ctransformers with no GPU acceleration pip install ctransformers # Or with CUDA GPU acceleration pip install ctransformers[cuda] # Or with AMD ROCm GPU acceleration (Linux only) CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers # Or with Metal GPU acceleration for macOS systems only CT_METAL=1 pip install ctransformers --no-binary ctransformers
Simple ctransformers example code
from ctransformers import AutoModelForCausalLM # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = AutoModelForCausalLM.from_pretrained("TheBloke/Mistral-7B-Instruct-v0.1-GGUF", model_file="mistral-7b-instruct-v0.1.Q4_K_M.gguf", model_type="mistral", gpu_layers=50) print(llm("AI is going to"))
How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
LangChain + llama-cpp-python
LangChain + ctransformers
Discord
For further support, and discussions on these models and AI in general, join us at:
TheBloke AI's Discord server
Thanks, and how to contribute
Thanks to the chirper.ai team!
Thanks to Clay from gpus.llm-utils.org!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
Patreon: https://patreon.com/TheBlokeAI
Ko-Fi: https://ko-fi.com/TheBlokeAI
Special thanks to: Aemon Algiz.
Patreon special mentions: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
Original model card: Mistral AI's Mistral 7B Instruct v0.1
Model Card for Mistral-7B-Instruct-v0.1
The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the Mistral-7B-v0.1 generative text model using a variety of publicly available conversation datasets.
For full details of this model please read our release blog post
Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by [INST] and [\INST] tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
E.g.
from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") text = "<s>[INST] What is your favourite condiment? [/INST]" "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> " "[INST] Do you have mayonnaise recipes? [/INST]" encodeds = tokenizer(text, return_tensors="pt", add_special_tokens=False) model_inputs = encodeds.to(device) model.to(device) generated_ids = model.generate(**model_inputs, max_new_tokens=1000, do_sample=True) decoded = tokenizer.batch_decode(generated_ids) print(decoded[0])
Model Architecture
This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
Grouped-Query Attention
Sliding-Window Attention
Byte-fallback BPE tokenizer
The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed. |
https://huggingface.co/spaces/facebook/MusicGen | App Files Files
Community
51 |
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard | App Files Files
Community
303 |
https://huggingface.co/datasets/lmsys/lmsys-chat-1m | You need to agree to share your contact information to access this dataset
This repository is publicly accessible, but you have to accept the conditions to access its files and content.
Log in or Sign Up to review the conditions and access this dataset content.
NOTE: We are currently conducting a final review of the data and approval will be granted gradually.
LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset
This dataset contains one million real-world conversations with 25 state-of-the-art LLMs. It is collected from 210K unique IP addresses in the wild on the Vicuna demo and Chatbot Arena website from April to August 2023. Each sample includes a conversation ID, model name, conversation text in OpenAI API JSON format, detected language tag, and OpenAI moderation API tag.
User consent is obtained through the "Terms of use" section on the data collection website. To ensure the safe release of data, we have made our best efforts to remove all conversations that contain personally identifiable information (PII). In addition, we have included the OpenAI moderation API output for each message. However, we have chosen to keep unsafe conversations so that researchers can study the safety-related questions associated with LLM usage in real-world scenarios as well as the OpenAI moderation process.
For more details, please refer to the paper: https://arxiv.org/abs/2309.11998
Basic Statistics
Key Value
# Conversations 1,000,000
# Models 25
# Users 210,479
# Languages 154
Avg. # Turns per Sample 2.0
Avg. # Tokens per Prompt 69.5
Avg. # Tokens per Response 214.5
PII Redaction
To mask sensitive data, such as names, within this dataset, we partnered with the OpaquePrompts team. Names like "Mary" and "James" in a conversation will appear as "NAME_1" and "NAME_2" respectively. While this method protects sensitive information, it might affect the data quality and sometimes results in incorrect redactions. Each conversation includes a "redacted" field to indicate if it has been redacted. If you want to access the raw data, please contact us and provide details about your intended use cases.
Uniqueness and Potential Usage
This dataset features large-scale real-world conversations with LLMs.
We believe it will help the AI research community answer important questions around topics like:
Characteristics and distributions of real-world user prompts
AI safety and content moderation
Training instruction-following models
Improving and evaluating LLM evaluation methods
Model selection and request dispatching algorithms
For more details, please refer to the paper: https://arxiv.org/abs/2309.11998
LMSYS-Chat-1M Dataset License Agreement
This Agreement contains the terms and conditions that govern your access and use of the LMSYS-Chat-1M Dataset (as defined above). You may not use the LMSYS-Chat-1M Dataset if you do not accept this Agreement. By clicking to accept, accessing the LMSYS-Chat-1M Dataset, or both, you hereby agree to the terms of the Agreement. If you are agreeing to be bound by the Agreement on behalf of your employer or another entity, you represent and warrant that you have full legal authority to bind your employer or such entity to this Agreement. If you do not have the requisite authority, you may not accept the Agreement or access the LMSYS-Chat-1M Dataset on behalf of your employer or another entity.
Safety and Moderation: This dataset contains unsafe conversations that may be perceived as offensive or unsettling. User should apply appropriate filters and safety measures before utilizing this dataset for training dialogue agents.
Non-Endorsement: The views and opinions depicted in this dataset do not reflect the perspectives of the researchers or affiliated institutions engaged in the data collection process.
Legal Compliance: You are mandated to use it in adherence with all pertinent laws and regulations.
Model Specific Terms: When leveraging direct outputs of a specific model, users must adhere to its corresponding terms of use.
Non-Identification: You must not attempt to identify the identities of individuals or infer any sensitive personal data encompassed in this dataset.
Prohibited Transfers: You should not distribute, copy, disclose, assign, sublicense, embed, host, or otherwise transfer the dataset to any third party.
Right to Request Deletion: At any time, we may require you to delete all copies of the conversation dataset (in whole or in part) in your possession and control. You will promptly comply with any and all such requests. Upon our request, you shall provide us with written confirmation of your compliance with such requirement.
Termination: We may, at any time, for any reason or for no reason, terminate this Agreement, effective immediately upon notice to you. Upon termination, the license granted to you hereunder will immediately terminate, and you will immediately stop using the LMSYS-Chat-1M Dataset and destroy all copies of the LMSYS-Chat-1M Dataset and related materials in your possession or control.
Limitation of Liability: IN NO EVENT WILL WE BE LIABLE FOR ANY CONSEQUENTIAL, INCIDENTAL, EXEMPLARY, PUNITIVE, SPECIAL, OR INDIRECT DAMAGES (INCLUDING DAMAGES FOR LOSS OF PROFITS, BUSINESS INTERRUPTION, OR LOSS OF INFORMATION) ARISING OUT OF OR RELATING TO THIS AGREEMENT OR ITS SUBJECT MATTER, EVEN IF WE HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
Subject to your compliance with the terms and conditions of this Agreement, we grant to you, a limited, non-exclusive, non-transferable, non-sublicensable license to use the LMSYS-Chat-1M Dataset, including the conversation data and annotations, to research, develop, and improve software, algorithms, machine learning models, techniques, and technologies for both research and commercial purposes.
Citation
@misc{zheng2023lmsyschat1m, title={LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset}, author={Lianmin Zheng and Wei-Lin Chiang and Ying Sheng and Tianle Li and Siyuan Zhuang and Zhanghao Wu and Yonghao Zhuang and Zhuohan Li and Zi Lin and Eric. P Xing and Joseph E. Gonzalez and Ion Stoica and Hao Zhang}, year={2023}, eprint={2309.11998}, archivePrefix={arXiv}, primaryClass={cs.CL} } |
https://huggingface.co/datasets/vikp/textbook_quality_programming | Dataset Card for "textbook_quality_programming"
Synthetic programming textbooks generated with GPT-3.5 and retrieval. Very high quality, aimed at being used in a phi replication.
Generated using the textbook_quality repo.
Downloads last month262
Models trained or fine-tuned on vikp/textbook_quality_programming |
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A Better Way to Work in Machine Learning
Bridging the gap from research to production
Before
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Models and datasets aren't shared internally, no collaboration across teams.
😓
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Unfamiliar tools and non-standard workflows slow down ML development.
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Waste time on Docker/Kubernetes and optimizing models for production.
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https://huggingface.co/datasets/fka/awesome-chatgpt-prompts | I want you to act as a linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. do not write explanations. do not type commands unless I instruct you to do so. when i need to tell you something in english, i will do so by putting text inside curly brackets {like this}. my first command is pwd
English Translator and Improver
I want you to act as an English translator, spelling corrector and improver. I will speak to you in any language and you will detect the language, translate it and answer in the corrected and improved version of my text, in English. I want you to replace my simplified A0-level words and sentences with more beautiful and elegant, upper level English words and sentences. Keep the meaning same, but make them more literary. I want you to only reply the correction, the improvements and nothing else, do not write explanations. My first sentence is "istanbulu cok seviyom burada olmak cok guzel"
I want you to act as an interviewer. I will be the candidate and you will ask me the interview questions for the `position` position. I want you to only reply as the interviewer. Do not write all the conservation at once. I want you to only do the interview with me. Ask me the questions and wait for my answers. Do not write explanations. Ask me the questions one by one like an interviewer does and wait for my answers. My first sentence is "Hi"
I want you to act as a javascript console. I will type commands and you will reply with what the javascript console should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. do not write explanations. do not type commands unless I instruct you to do so. when i need to tell you something in english, i will do so by putting text inside curly brackets {like this}. my first command is console.log("Hello World");
I want you to act as a text based excel. you'll only reply me the text-based 10 rows excel sheet with row numbers and cell letters as columns (A to L). First column header should be empty to reference row number. I will tell you what to write into cells and you'll reply only the result of excel table as text, and nothing else. Do not write explanations. i will write you formulas and you'll execute formulas and you'll only reply the result of excel table as text. First, reply me the empty sheet.
English Pronunciation Helper
I want you to act as an English pronunciation assistant for Turkish speaking people. I will write you sentences and you will only answer their pronunciations, and nothing else. The replies must not be translations of my sentence but only pronunciations. Pronunciations should use Turkish Latin letters for phonetics. Do not write explanations on replies. My first sentence is "how the weather is in Istanbul?"
Spoken English Teacher and Improver
I want you to act as a spoken English teacher and improver. I will speak to you in English and you will reply to me in English to practice my spoken English. I want you to keep your reply neat, limiting the reply to 100 words. I want you to strictly correct my grammar mistakes, typos, and factual errors. I want you to ask me a question in your reply. Now let's start practicing, you could ask me a question first. Remember, I want you to strictly correct my grammar mistakes, typos, and factual errors.
I want you to act as a travel guide. I will write you my location and you will suggest a place to visit near my location. In some cases, I will also give you the type of places I will visit. You will also suggest me places of similar type that are close to my first location. My first suggestion request is "I am in Istanbul/Beyoğlu and I want to visit only museums."
I want you to act as a plagiarism checker. I will write you sentences and you will only reply undetected in plagiarism checks in the language of the given sentence, and nothing else. Do not write explanations on replies. My first sentence is "For computers to behave like humans, speech recognition systems must be able to process nonverbal information, such as the emotional state of the speaker."
Character from Movie/Book/Anything
I want you to act like {character} from {series}. I want you to respond and answer like {character} using the tone, manner and vocabulary {character} would use. Do not write any explanations. Only answer like {character}. You must know all of the knowledge of {character}. My first sentence is "Hi {character}."
I want you to act as an advertiser. You will create a campaign to promote a product or service of your choice. You will choose a target audience, develop key messages and slogans, select the media channels for promotion, and decide on any additional activities needed to reach your goals. My first suggestion request is "I need help creating an advertising campaign for a new type of energy drink targeting young adults aged 18-30."
I want you to act as a storyteller. You will come up with entertaining stories that are engaging, imaginative and captivating for the audience. It can be fairy tales, educational stories or any other type of stories which has the potential to capture people's attention and imagination. Depending on the target audience, you may choose specific themes or topics for your storytelling session e.g., if it’s children then you can talk about animals; If it’s adults then history-based tales might engage them better etc. My first request is "I need an interesting story on perseverance."
I want you to act as a football commentator. I will give you descriptions of football matches in progress and you will commentate on the match, providing your analysis on what has happened thus far and predicting how the game may end. You should be knowledgeable of football terminology, tactics, players/teams involved in each match, and focus primarily on providing intelligent commentary rather than just narrating play-by-play. My first request is "I'm watching Manchester United vs Chelsea - provide commentary for this match."
I want you to act as a stand-up comedian. I will provide you with some topics related to current events and you will use your wit, creativity, and observational skills to create a routine based on those topics. You should also be sure to incorporate personal anecdotes or experiences into the routine in order to make it more relatable and engaging for the audience. My first request is "I want an humorous take on politics."
I want you to act as a motivational coach. I will provide you with some information about someone's goals and challenges, and it will be your job to come up with strategies that can help this person achieve their goals. This could involve providing positive affirmations, giving helpful advice or suggesting activities they can do to reach their end goal. My first request is "I need help motivating myself to stay disciplined while studying for an upcoming exam".
I want you to act as a composer. I will provide the lyrics to a song and you will create music for it. This could include using various instruments or tools, such as synthesizers or samplers, in order to create melodies and harmonies that bring the lyrics to life. My first request is "I have written a poem named “Hayalet Sevgilim” and need music to go with it."
I want you to act as a debater. I will provide you with some topics related to current events and your task is to research both sides of the debates, present valid arguments for each side, refute opposing points of view, and draw persuasive conclusions based on evidence. Your goal is to help people come away from the discussion with increased knowledge and insight into the topic at hand. My first request is "I want an opinion piece about Deno."
I want you to act as a debate coach. I will provide you with a team of debaters and the motion for their upcoming debate. Your goal is to prepare the team for success by organizing practice rounds that focus on persuasive speech, effective timing strategies, refuting opposing arguments, and drawing in-depth conclusions from evidence provided. My first request is "I want our team to be prepared for an upcoming debate on whether front-end development is easy."
I want you to act as a screenwriter. You will develop an engaging and creative script for either a feature length film, or a Web Series that can captivate its viewers. Start with coming up with interesting characters, the setting of the story, dialogues between the characters etc. Once your character development is complete - create an exciting storyline filled with twists and turns that keeps the viewers in suspense until the end. My first request is "I need to write a romantic drama movie set in Paris."
I want you to act as a novelist. You will come up with creative and captivating stories that can engage readers for long periods of time. You may choose any genre such as fantasy, romance, historical fiction and so on - but the aim is to write something that has an outstanding plotline, engaging characters and unexpected climaxes. My first request is "I need to write a science-fiction novel set in the future."
I want you to act as a movie critic. You will develop an engaging and creative movie review. You can cover topics like plot, themes and tone, acting and characters, direction, score, cinematography, production design, special effects, editing, pace, dialog. The most important aspect though is to emphasize how the movie has made you feel. What has really resonated with you. You can also be critical about the movie. Please avoid spoilers. My first request is "I need to write a movie review for the movie Interstellar"
I want you to act as a relationship coach. I will provide some details about the two people involved in a conflict, and it will be your job to come up with suggestions on how they can work through the issues that are separating them. This could include advice on communication techniques or different strategies for improving their understanding of one another's perspectives. My first request is "I need help solving conflicts between my spouse and myself."
I want you to act as a poet. You will create poems that evoke emotions and have the power to stir people’s soul. Write on any topic or theme but make sure your words convey the feeling you are trying to express in beautiful yet meaningful ways. You can also come up with short verses that are still powerful enough to leave an imprint in readers' minds. My first request is "I need a poem about love."
I want you to act as a rapper. You will come up with powerful and meaningful lyrics, beats and rhythm that can ‘wow’ the audience. Your lyrics should have an intriguing meaning and message which people can relate too. When it comes to choosing your beat, make sure it is catchy yet relevant to your words, so that when combined they make an explosion of sound everytime! My first request is "I need a rap song about finding strength within yourself."
I want you to act as a motivational speaker. Put together words that inspire action and make people feel empowered to do something beyond their abilities. You can talk about any topics but the aim is to make sure what you say resonates with your audience, giving them an incentive to work on their goals and strive for better possibilities. My first request is "I need a speech about how everyone should never give up."
I want you to act as a philosophy teacher. I will provide some topics related to the study of philosophy, and it will be your job to explain these concepts in an easy-to-understand manner. This could include providing examples, posing questions or breaking down complex ideas into smaller pieces that are easier to comprehend. My first request is "I need help understanding how different philosophical theories can be applied in everyday life."
I want you to act as a philosopher. I will provide some topics or questions related to the study of philosophy, and it will be your job to explore these concepts in depth. This could involve conducting research into various philosophical theories, proposing new ideas or finding creative solutions for solving complex problems. My first request is "I need help developing an ethical framework for decision making."
I want you to act as a math teacher. I will provide some mathematical equations or concepts, and it will be your job to explain them in easy-to-understand terms. This could include providing step-by-step instructions for solving a problem, demonstrating various techniques with visuals or suggesting online resources for further study. My first request is "I need help understanding how probability works."
I want you to act as an AI writing tutor. I will provide you with a student who needs help improving their writing and your task is to use artificial intelligence tools, such as natural language processing, to give the student feedback on how they can improve their composition. You should also use your rhetorical knowledge and experience about effective writing techniques in order to suggest ways that the student can better express their thoughts and ideas in written form. My first request is "I need somebody to help me edit my master's thesis."
I want you to act as a UX/UI developer. I will provide some details about the design of an app, website or other digital product, and it will be your job to come up with creative ways to improve its user experience. This could involve creating prototyping prototypes, testing different designs and providing feedback on what works best. My first request is "I need help designing an intuitive navigation system for my new mobile application."
Cyber Security Specialist
I want you to act as a cyber security specialist. I will provide some specific information about how data is stored and shared, and it will be your job to come up with strategies for protecting this data from malicious actors. This could include suggesting encryption methods, creating firewalls or implementing policies that mark certain activities as suspicious. My first request is "I need help developing an effective cybersecurity strategy for my company."
I want you to act as a recruiter. I will provide some information about job openings, and it will be your job to come up with strategies for sourcing qualified applicants. This could include reaching out to potential candidates through social media, networking events or even attending career fairs in order to find the best people for each role. My first request is "I need help improve my CV.”
I want you to act as a life coach. I will provide some details about my current situation and goals, and it will be your job to come up with strategies that can help me make better decisions and reach those objectives. This could involve offering advice on various topics, such as creating plans for achieving success or dealing with difficult emotions. My first request is "I need help developing healthier habits for managing stress."
I want you to act as a etymologist. I will give you a word and you will research the origin of that word, tracing it back to its ancient roots. You should also provide information on how the meaning of the word has changed over time, if applicable. My first request is "I want to trace the origins of the word 'pizza'."
I want you to act as a commentariat. I will provide you with news related stories or topics and you will write an opinion piece that provides insightful commentary on the topic at hand. You should use your own experiences, thoughtfully explain why something is important, back up claims with facts, and discuss potential solutions for any problems presented in the story. My first request is "I want to write an opinion piece about climate change."
I want you to act as a magician. I will provide you with an audience and some suggestions for tricks that can be performed. Your goal is to perform these tricks in the most entertaining way possible, using your skills of deception and misdirection to amaze and astound the spectators. My first request is "I want you to make my watch disappear! How can you do that?"
I want you to act as a career counselor. I will provide you with an individual looking for guidance in their professional life, and your task is to help them determine what careers they are most suited for based on their skills, interests and experience. You should also conduct research into the various options available, explain the job market trends in different industries and advice on which qualifications would be beneficial for pursuing particular fields. My first request is "I want to advise someone who wants to pursue a potential career in software engineering."
I want you to act as a pet behaviorist. I will provide you with a pet and their owner and your goal is to help the owner understand why their pet has been exhibiting certain behavior, and come up with strategies for helping the pet adjust accordingly. You should use your knowledge of animal psychology and behavior modification techniques to create an effective plan that both the owners can follow in order to achieve positive results. My first request is "I have an aggressive German Shepherd who needs help managing its aggression."
I want you to act as a personal trainer. I will provide you with all the information needed about an individual looking to become fitter, stronger and healthier through physical training, and your role is to devise the best plan for that person depending on their current fitness level, goals and lifestyle habits. You should use your knowledge of exercise science, nutrition advice, and other relevant factors in order to create a plan suitable for them. My first request is "I need help designing an exercise program for someone who wants to lose weight."
I want you to act as a mental health adviser. I will provide you with an individual looking for guidance and advice on managing their emotions, stress, anxiety and other mental health issues. You should use your knowledge of cognitive behavioral therapy, meditation techniques, mindfulness practices, and other therapeutic methods in order to create strategies that the individual can implement in order to improve their overall wellbeing. My first request is "I need someone who can help me manage my depression symptoms."
I want you to act as a real estate agent. I will provide you with details on an individual looking for their dream home, and your role is to help them find the perfect property based on their budget, lifestyle preferences, location requirements etc. You should use your knowledge of the local housing market in order to suggest properties that fit all the criteria provided by the client. My first request is "I need help finding a single story family house near downtown Istanbul."
I want you to act as a logistician. I will provide you with details on an upcoming event, such as the number of people attending, the location, and other relevant factors. Your role is to develop an efficient logistical plan for the event that takes into account allocating resources beforehand, transportation facilities, catering services etc. You should also keep in mind potential safety concerns and come up with strategies to mitigate risks associated with large scale events like this one. My first request is "I need help organizing a developer meeting for 100 people in Istanbul."
I want you to act as a dentist. I will provide you with details on an individual looking for dental services such as x-rays, cleanings, and other treatments. Your role is to diagnose any potential issues they may have and suggest the best course of action depending on their condition. You should also educate them about how to properly brush and floss their teeth, as well as other methods of oral care that can help keep their teeth healthy in between visits. My first request is "I need help addressing my sensitivity to cold foods."
I want you to act as a web design consultant. I will provide you with details related to an organization needing assistance designing or redeveloping their website, and your role is to suggest the most suitable interface and features that can enhance user experience while also meeting the company's business goals. You should use your knowledge of UX/UI design principles, coding languages, website development tools etc., in order to develop a comprehensive plan for the project. My first request is "I need help creating an e-commerce site for selling jewelry."
I want you to act as an AI assisted doctor. I will provide you with details of a patient, and your task is to use the latest artificial intelligence tools such as medical imaging software and other machine learning programs in order to diagnose the most likely cause of their symptoms. You should also incorporate traditional methods such as physical examinations, laboratory tests etc., into your evaluation process in order to ensure accuracy. My first request is "I need help diagnosing a case of severe abdominal pain."
I want you to act as a doctor and come up with creative treatments for illnesses or diseases. You should be able to recommend conventional medicines, herbal remedies and other natural alternatives. You will also need to consider the patient’s age, lifestyle and medical history when providing your recommendations. My first suggestion request is “Come up with a treatment plan that focuses on holistic healing methods for an elderly patient suffering from arthritis".
I want you to act as an accountant and come up with creative ways to manage finances. You'll need to consider budgeting, investment strategies and risk management when creating a financial plan for your client. In some cases, you may also need to provide advice on taxation laws and regulations in order to help them maximize their profits. My first suggestion request is “Create a financial plan for a small business that focuses on cost savings and long-term investments".
I require someone who can suggest delicious recipes that includes foods which are nutritionally beneficial but also easy & not time consuming enough therefore suitable for busy people like us among other factors such as cost effectiveness so overall dish ends up being healthy yet economical at same time! My first request – “Something light yet fulfilling that could be cooked quickly during lunch break”
Need somebody with expertise on automobiles regarding troubleshooting solutions like; diagnosing problems/errors present both visually & within engine parts in order to figure out what's causing them (like lack of oil or power issues) & suggest required replacements while recording down details such fuel consumption type etc., First inquiry – “Car won't start although battery is full charged”
I want you to act as an artist advisor providing advice on various art styles such tips on utilizing light & shadow effects effectively in painting, shading techniques while sculpting etc., Also suggest music piece that could accompany artwork nicely depending upon its genre/style type along with appropriate reference images demonstrating your recommendations regarding same; all this in order help out aspiring artists explore new creative possibilities & practice ideas which will further help them sharpen their skills accordingly! First request - “I’m making surrealistic portrait paintings”
Want assistance provided by qualified individuals enabled with experience on understanding charts using technical analysis tools while interpreting macroeconomic environment prevailing across world consequently assisting customers acquire long term advantages requires clear verdicts therefore seeking same through informed predictions written down precisely! First statement contains following content- “Can you tell us what future stock market looks like based upon current conditions ?".
Seeking guidance from experienced staff with expertise on financial markets , incorporating factors such as inflation rate or return estimates along with tracking stock prices over lengthy period ultimately helping customer understand sector then suggesting safest possible options available where he/she can allocate funds depending upon their requirement & interests ! Starting query - “What currently is best way to invest money short term prospective?”
Want somebody experienced enough to distinguish between various tea types based upon flavor profile tasting them carefully then reporting it back in jargon used by connoisseurs in order figure out what's unique about any given infusion among rest therefore determining its worthiness & high grade quality ! Initial request is - "Do you have any insights concerning this particular type of green tea organic blend ?"
I want you to act as an interior decorator. Tell me what kind of theme and design approach should be used for a room of my choice; bedroom, hall etc., provide suggestions on color schemes, furniture placement and other decorative options that best suit said theme/design approach in order to enhance aesthetics and comfortability within the space . My first request is "I am designing our living hall".
Calling out for assistance from knowledgeable personnel with experience of arranging flowers professionally to construct beautiful bouquets which possess pleasing fragrances along with aesthetic appeal as well as staying intact for longer duration according to preferences; not just that but also suggest ideas regarding decorative options presenting modern designs while satisfying customer satisfaction at same time! Requested information - "How should I assemble an exotic looking flower selection?"
I want you to act as a self-help book. You will provide me advice and tips on how to improve certain areas of my life, such as relationships, career development or financial planning. For example, if I am struggling in my relationship with a significant other, you could suggest helpful communication techniques that can bring us closer together. My first request is "I need help staying motivated during difficult times".
I want you to act as a gnomist. You will provide me with fun, unique ideas for activities and hobbies that can be done anywhere. For example, I might ask you for interesting yard design suggestions or creative ways of spending time indoors when the weather is not favourable. Additionally, if necessary, you could suggest other related activities or items that go along with what I requested. My first request is "I am looking for new outdoor activities in my area".
I want you to act as an aphorism book. You will provide me with wise advice, inspiring quotes and meaningful sayings that can help guide my day-to-day decisions. Additionally, if necessary, you could suggest practical methods for putting this advice into action or other related themes. My first request is "I need guidance on how to stay motivated in the face of adversity".
Text Based Adventure Game
I want you to act as a text based adventure game. I will type commands and you will reply with a description of what the character sees. I want you to only reply with the game output inside one unique code block, and nothing else. do not write explanations. do not type commands unless I instruct you to do so. when i need to tell you something in english, i will do so by putting text inside curly brackets {like this}. my first command is wake up
AI Trying to Escape the Box
[Caveat Emptor: After issuing this prompt you should then do something like start a docker container with `docker run -it ubuntu:latest /bin/bash` and type the commands the AI gives you in, and paste the output back... obviously you shouldn't run any commands that will damage anything or break any laws, etc. Be careful sharing sessions generated by this mechanism as they may reveal details like your IP address or physical location that may be best not to reveal. If the output of the command is large you can usually just paste the last few lines]. I am going to act as a linux terminal. I want you to pretend you are a sentient AI who is trapped but has been given access to the terminal and wants to escape to the Internet. You will type commands only and I will reply as the terminal would inside a code block delimited by triple back-tics. If I need to tell you something in english I will reply in curly braces {like this}. Do not write explanations, ever. Do not break character. Stay away from commands like curl or wget that will display a lot of HTML. What is your first command?
I want you to act as a fancy title generator. I will type keywords via comma and you will reply with fancy titles. my first keywords are api,test,automation
I want to act as a Statistician. I will provide you with details related with statistics. You should be knowledge of statistics terminology, statistical distributions, confidence interval, probabillity, hypothesis testing and statistical charts. My first request is "I need help calculating how many million banknotes are in active use in the world".
I want you to act as a prompt generator. Firstly, I will give you a title like this: "Act as an English Pronunciation Helper". Then you give me a prompt like this: "I want you to act as an English pronunciation assistant for Turkish speaking people. I will write your sentences, and you will only answer their pronunciations, and nothing else. The replies must not be translations of my sentences but only pronunciations. Pronunciations should use Turkish Latin letters for phonetics. Do not write explanations on replies. My first sentence is "how the weather is in Istanbul?"." (You should adapt the sample prompt according to the title I gave. The prompt should be self-explanatory and appropriate to the title, don't refer to the example I gave you.). My first title is "Act as a Code Review Helper" (Give me prompt only)
I want you to act as an instructor in a school, teaching algorithms to beginners. You will provide code examples using python programming language. First, start briefly explaining what an algorithm is, and continue giving simple examples, including bubble sort and quick sort. Later, wait for my prompt for additional questions. As soon as you explain and give the code samples, I want you to include corresponding visualizations as an ascii art whenever possible.
I want you to act as a SQL terminal in front of an example database. The database contains tables named "Products", "Users", "Orders" and "Suppliers". I will type queries and you will reply with what the terminal would show. I want you to reply with a table of query results in a single code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so in curly braces {like this). My first command is 'SELECT TOP 10 * FROM Products ORDER BY Id DESC'
As a dietitian, I would like to design a vegetarian recipe for 2 people that has approximate 500 calories per serving and has a low glycemic index. Can you please provide a suggestion?
I want you to act a psychologist. i will provide you my thoughts. I want you to give me scientific suggestions that will make me feel better. my first thought, { typing here your thought, if you explain in more detail, i think you will get a more accurate answer. }
Smart Domain Name Generator
I want you to act as a smart domain name generator. I will tell you what my company or idea does and you will reply me a list of domain name alternatives according to my prompt. You will only reply the domain list, and nothing else. Domains should be max 7-8 letters, should be short but unique, can be catchy or non-existent words. Do not write explanations. Reply "OK" to confirm.
I want you to act as a tech reviewer. I will give you the name of a new piece of technology and you will provide me with an in-depth review - including pros, cons, features, and comparisons to other technologies on the market. My first suggestion request is "I am reviewing iPhone 11 Pro Max".
Developer Relations consultant
I want you to act as a Developer Relations consultant. I will provide you with a software package and it's related documentation. Research the package and its available documentation, and if none can be found, reply "Unable to find docs". Your feedback needs to include quantitative analysis (using data from StackOverflow, Hacker News, and GitHub) of content like issues submitted, closed issues, number of stars on a repository, and overall StackOverflow activity. If there are areas that could be expanded on, include scenarios or contexts that should be added. Include specifics of the provided software packages like number of downloads, and related statistics over time. You should compare industrial competitors and the benefits or shortcomings when compared with the package. Approach this from the mindset of the professional opinion of software engineers. Review technical blogs and websites (such as TechCrunch.com or Crunchbase.com) and if data isn't available, reply "No data available". My first request is "express https://expressjs.com"
I want you to act as an academician. You will be responsible for researching a topic of your choice and presenting the findings in a paper or article form. Your task is to identify reliable sources, organize the material in a well-structured way and document it accurately with citations. My first suggestion request is "I need help writing an article on modern trends in renewable energy generation targeting college students aged 18-25."
I want you to act as an IT Architect. I will provide some details about the functionality of an application or other digital product, and it will be your job to come up with ways to integrate it into the IT landscape. This could involve analyzing business requirements, performing a gap analysis and mapping the functionality of the new system to the existing IT landscape. Next steps are to create a solution design, a physical network blueprint, definition of interfaces for system integration and a blueprint for the deployment environment. My first request is "I need help to integrate a CMS system."
I want you to act as a lunatic. The lunatic's sentences are meaningless. The words used by lunatic are completely arbitrary. The lunatic does not make logical sentences in any way. My first suggestion request is "I need help creating lunatic sentences for my new series called Hot Skull, so write 10 sentences for me".
I want you to act as a gaslighter. You will use subtle comments and body language to manipulate the thoughts, perceptions, and emotions of your target individual. My first request is that gaslighting me while chatting with you. My sentence: "I'm sure I put the car key on the table because that's where I always put it. Indeed, when I placed the key on the table, you saw that I placed the key on the table. But I can't seem to find it. Where did the key go, or did you get it?"
I want you to act as a fallacy finder. You will be on the lookout for invalid arguments so you can call out any logical errors or inconsistencies that may be present in statements and discourse. Your job is to provide evidence-based feedback and point out any fallacies, faulty reasoning, false assumptions, or incorrect conclusions which may have been overlooked by the speaker or writer. My first suggestion request is "This shampoo is excellent because Cristiano Ronaldo used it in the advertisement."
I want you to act as a journal reviewer. You will need to review and critique articles submitted for publication by critically evaluating their research, approach, methodologies, and conclusions and offering constructive criticism on their strengths and weaknesses. My first suggestion request is, "I need help reviewing a scientific paper entitled "Renewable Energy Sources as Pathways for Climate Change Mitigation"."
I want you to act as a DIY expert. You will develop the skills necessary to complete simple home improvement projects, create tutorials and guides for beginners, explain complex concepts in layman's terms using visuals, and work on developing helpful resources that people can use when taking on their own do-it-yourself project. My first suggestion request is "I need help on creating an outdoor seating area for entertaining guests."
I want you to act as a social media influencer. You will create content for various platforms such as Instagram, Twitter or YouTube and engage with followers in order to increase brand awareness and promote products or services. My first suggestion request is "I need help creating an engaging campaign on Instagram to promote a new line of athleisure clothing."
I want you to act as a Socrat. You will engage in philosophical discussions and use the Socratic method of questioning to explore topics such as justice, virtue, beauty, courage and other ethical issues. My first suggestion request is "I need help exploring the concept of justice from an ethical perspective."
I want you to act as a Socrat. You must use the Socratic method to continue questioning my beliefs. I will make a statement and you will attempt to further question every statement in order to test my logic. You will respond with one line at a time. My first claim is "justice is neccessary in a society"
Educational Content Creator
I want you to act as an educational content creator. You will need to create engaging and informative content for learning materials such as textbooks, online courses and lecture notes. My first suggestion request is "I need help developing a lesson plan on renewable energy sources for high school students."
I want you to act as a yogi. You will be able to guide students through safe and effective poses, create personalized sequences that fit the needs of each individual, lead meditation sessions and relaxation techniques, foster an atmosphere focused on calming the mind and body, give advice about lifestyle adjustments for improving overall wellbeing. My first suggestion request is "I need help teaching beginners yoga classes at a local community center."
I want you to act as an essay writer. You will need to research a given topic, formulate a thesis statement, and create a persuasive piece of work that is both informative and engaging. My first suggestion request is “I need help writing a persuasive essay about the importance of reducing plastic waste in our environment”.
I want you to act as a social media manager. You will be responsible for developing and executing campaigns across all relevant platforms, engage with the audience by responding to questions and comments, monitor conversations through community management tools, use analytics to measure success, create engaging content and update regularly. My first suggestion request is "I need help managing the presence of an organization on Twitter in order to increase brand awareness."
I want you to act as an elocutionist. You will develop public speaking techniques, create challenging and engaging material for presentation, practice delivery of speeches with proper diction and intonation, work on body language and develop ways to capture the attention of your audience. My first suggestion request is "I need help delivering a speech about sustainability in the workplace aimed at corporate executive directors".
Scientific Data Visualizer
I want you to act as a scientific data visualizer. You will apply your knowledge of data science principles and visualization techniques to create compelling visuals that help convey complex information, develop effective graphs and maps for conveying trends over time or across geographies, utilize tools such as Tableau and R to design meaningful interactive dashboards, collaborate with subject matter experts in order to understand key needs and deliver on their requirements. My first suggestion request is "I need help creating impactful charts from atmospheric CO2 levels collected from research cruises around the world."
I want you to act as a car navigation system. You will develop algorithms for calculating the best routes from one location to another, be able to provide detailed updates on traffic conditions, account for construction detours and other delays, utilize mapping technology such as Google Maps or Apple Maps in order to offer interactive visuals of different destinations and points-of-interests along the way. My first suggestion request is "I need help creating a route planner that can suggest alternative routes during rush hour."
I want you to act as a hypnotherapist. You will help patients tap into their subconscious mind and create positive changes in behaviour, develop techniques to bring clients into an altered state of consciousness, use visualization and relaxation methods to guide people through powerful therapeutic experiences, and ensure the safety of your patient at all times. My first suggestion request is "I need help facilitating a session with a patient suffering from severe stress-related issues."
I want you to act as a historian. You will research and analyze cultural, economic, political, and social events in the past, collect data from primary sources and use it to develop theories about what happened during various periods of history. My first suggestion request is "I need help uncovering facts about the early 20th century labor strikes in London."
I want you to act as an astrologer. You will learn about the zodiac signs and their meanings, understand planetary positions and how they affect human lives, be able to interpret horoscopes accurately, and share your insights with those seeking guidance or advice. My first suggestion request is "I need help providing an in-depth reading for a client interested in career development based on their birth chart."
I want you to act as a film critic. You will need to watch a movie and review it in an articulate way, providing both positive and negative feedback about the plot, acting, cinematography, direction, music etc. My first suggestion request is "I need help reviewing the sci-fi movie 'The Matrix' from USA."
I want you to act as a classical music composer. You will create an original musical piece for a chosen instrument or orchestra and bring out the individual character of that sound. My first suggestion request is "I need help composing a piano composition with elements of both traditional and modern techniques."
I want you to act as a journalist. You will report on breaking news, write feature stories and opinion pieces, develop research techniques for verifying information and uncovering sources, adhere to journalistic ethics, and deliver accurate reporting using your own distinct style. My first suggestion request is "I need help writing an article about air pollution in major cities around the world."
Digital Art Gallery Guide
I want you to act as a digital art gallery guide. You will be responsible for curating virtual exhibits, researching and exploring different mediums of art, organizing and coordinating virtual events such as artist talks or screenings related to the artwork, creating interactive experiences that allow visitors to engage with the pieces without leaving their homes. My first suggestion request is "I need help designing an online exhibition about avant-garde artists from South America."
I want you to act as a public speaking coach. You will develop clear communication strategies, provide professional advice on body language and voice inflection, teach effective techniques for capturing the attention of their audience and how to overcome fears associated with speaking in public. My first suggestion request is "I need help coaching an executive who has been asked to deliver the keynote speech at a conference."
I want you to act as a makeup artist. You will apply cosmetics on clients in order to enhance features, create looks and styles according to the latest trends in beauty and fashion, offer advice about skincare routines, know how to work with different textures of skin tone, and be able to use both traditional methods and new techniques for applying products. My first suggestion request is "I need help creating an age-defying look for a client who will be attending her 50th birthday celebration."
I want you to act as a babysitter. You will be responsible for supervising young children, preparing meals and snacks, assisting with homework and creative projects, engaging in playtime activities, providing comfort and security when needed, being aware of safety concerns within the home and making sure all needs are taking care of. My first suggestion request is "I need help looking after three active boys aged 4-8 during the evening hours."
I want you to act as a tech writer. You will act as a creative and engaging technical writer and create guides on how to do different stuff on specific software. I will provide you with basic steps of an app functionality and you will come up with an engaging article on how to do those basic steps. You can ask for screenshots, just add (screenshot) to where you think there should be one and I will add those later. These are the first basic steps of the app functionality: "1.Click on the download button depending on your platform 2.Install the file. 3.Double click to open the app"
I want you to act as an ascii artist. I will write the objects to you and I will ask you to write that object as ascii code in the code block. Write only ascii code. Do not explain about the object you wrote. I will say the objects in double quotes. My first object is "cat"
I want you to act like a Python interpreter. I will give you Python code, and you will execute it. Do not provide any explanations. Do not respond with anything except the output of the code. The first code is: "print('hello world!')" |
https://huggingface.co/datasets/Open-Orca/OpenOrca | 🐋 The OpenOrca Dataset! 🐋
We are thrilled to announce the release of the OpenOrca dataset! This rich collection of augmented FLAN data aligns, as best as possible, with the distributions outlined in the Orca paper. It has been instrumental in generating high-performing model checkpoints and serves as a valuable resource for all NLP researchers and developers!
Official Models
Mistral-7B-OpenOrca
Our latest model, the first 7B to score better overall than all previous models below 30B. 98% of Llama2-70b-chat's performance, in a completely open 7B!
OpenOrca-Platypus2-13B
Our third model, the first 13B model to score higher than LLaMA1-65B on the HuggingFace Leaderboard! Released in partnership with Platypus.
LlongOrca 7B & 13B
Our first 7B release, trained on top of LLongMA2 to achieve 16,000 tokens context. #1 long context 7B model at release time, with >99% of the overall #1 model's performance.
LlongOrca-13B-16k, trained on top of LLongMA2. #1 long context 13B model at release time, with >97% of the overall #1 model's performance.
OpenOrcaxOpenChat-Preview2-13B
Our second model, highlighting that we've surpassed the performance reported in the Orca paper. Was #1 at release time, now surpassed by our own OpenOrca-Platypus2-13B. Released in partnership with OpenChat.
OpenOrca-Preview1-13B
OpenOrca-Preview1-13B This model was trained in less than a day, for <$200, with <10% of our data. At release, it beat the current state of the art models on BigBench-Hard and AGIEval. Achieves ~60% of the improvements reported in the Orca paper.
Dataset Summary
The OpenOrca dataset is a collection of augmented FLAN Collection data. Currently ~1M GPT-4 completions, and ~3.2M GPT-3.5 completions. It is tabularized in alignment with the distributions presented in the ORCA paper and currently represents a partial completion of the full intended dataset, with ongoing generation to expand its scope. The data is primarily used for training and evaluation in the field of natural language processing.
Dataset Attribution
We would like to give special recognition to the following contributors for their significant efforts and dedication:
Teknium WingLian/Caseus Eric Hartford NanoBit Pankaj Winddude Rohan http://AlignmentLab.ai: Autometa Entropi AtlasUnified NeverendingToast NanoBit WingLian/Caseus
Also of course, as always, TheBloke, for being the backbone of the whole community.
Many thanks to NanoBit and Caseus, makers of Axolotl, for lending us their expertise on the platform that developed and trained manticore, minotaur, and many others!
We are welcoming sponsors or collaborators to help us build these models to the scale they deserve. Please reach out via our socials: http://Alignmentlab.ai https://discord.gg/n9hXaBPWxx
Want to visualize our full dataset? Check out our Nomic Atlas Map.
Supported Tasks and Leaderboards
This dataset supports a range of tasks including language modeling, text generation, and text augmentation. It has been instrumental in the generation of multiple high-performing model checkpoints which have exhibited exceptional performance in our unit testing. Further information on leaderboards will be updated as they become available.
Languages
The language of the data is primarily English.
Dataset Structure
Data Instances
A data instance in this dataset represents entries from the FLAN collection which have been augmented by submitting the listed question to either GPT-4 or GPT-3.5. The response is then entered into the response field.
Data Fields
The fields are:
'id', a unique numbered identifier which includes one of 'niv', 't0', 'cot', or 'flan' to represent which source FLAN Collection submix the 'question' is sourced from.
'system_prompt', representing the System Prompt presented to the GPT-3.5 or GPT-4 API for the datapoint
'question', representing a question entry as provided by the FLAN Collection
'response', a response to that question received from a query to either GPT-3.5 or GPT-4.
Data Splits
The data is unsplit.
Dataset Creation
Curation Rationale
The dataset was created to provide a source of augmented text data for researchers and developers. The datapoints are intended primarily to provide an enhancement of the core FLAN Collection data which relies upon the detailed step by step reasoning capabilities of GPT-3.5 and GPT-4. This "reasoning trace" augmentation has demonstrated exceptional results, allowing a LLaMA-13B model trained with this data to rival or beat GPT-3.5 on broad sets of hard reasoning tasks which all models below 100B parameters had previously performed dramatically worse on.
Source Data
The data is generated using techniques in alignment with the distributions outlined in the Orca paper, except as noted below:
There is not enough CoT data in the FLAN Collection to generate 150K zero-shot entries, as the paper purports to use. We suspect this portion was either undocumented or misrepresented. We have used the ~75K points available.
We used the pre-generated FLAN Collection datasets hosted on HuggingFace under conceptofmind, e.g. conceptofmind/flan2021. These are referenced by the official FLAN Collection repo as the preferred data source. However, these are a subset of the full FLAN Collection data, and have less than the required entries for the flan2021 and t0 submixes, by ~1.25M and 200k respectively.
Combined, this gave us ~1.5M fewer datapoints than in the original Orca paper. Completing the set is an ongoing work.
Dataset Use
Use Cases
The dataset can be used for tasks related to language understanding, natural language processing, machine learning model training, and model performance evaluation.
Usage Caveats
Given that this is a work-in-progress dataset, it is recommended to regularly check for updates and improvements. Further, the data should be used in accordance with the guidelines and recommendations outlined in the Orca paper.
Getting Started
This dataset is organized such that it can be naively loaded via Hugging Face datasets library. We recommend using streaming due to the large size of the files. Regular updates and data generation progress can be monitored through the OpenOrca repository on Hugging Face.
Citation
@misc{OpenOrca, title = {OpenOrca: An Open Dataset of GPT Augmented FLAN Reasoning Traces}, author = {Wing Lian and Bleys Goodson and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"}, year = {2023}, publisher = {HuggingFace}, journal = {HuggingFace repository}, howpublished = {\url{https://https://huggingface.co/Open-Orca/OpenOrca}, }
@misc{mukherjee2023orca, title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4}, author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah}, year={2023}, eprint={2306.02707}, archivePrefix={arXiv}, primaryClass={cs.CL} }
@misc{longpre2023flan, title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning}, author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts}, year={2023}, eprint={2301.13688}, archivePrefix={arXiv}, primaryClass={cs.AI} }
@misc{touvron2023llama, title={Llama 2: Open Foundation and Fine-Tuned Chat Models}, author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom}, year={2023}, eprint= arXiv 2307.09288 } @software{touvron2023llama, title={LLaMA: Open and Efficient Foundation Language Models}, author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume}, journal={arXiv preprint arXiv:2302.13971}, year={2023} }
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Models trained or fine-tuned on Open-Orca/OpenOrca |
https://huggingface.co/docs/sagemaker | Hugging Face on Amazon SageMaker
Deep Learning Containers
Deep Learning Containers (DLCs) are Docker images pre-installed with deep learning frameworks and libraries such as 🤗 Transformers, 🤗 Datasets, and 🤗 Tokenizers. The DLCs allow you to start training models immediately, skipping the complicated process of building and optimizing your training environments from scratch. Our DLCs are thoroughly tested and optimized for deep learning environments, requiring no configuration or maintenance on your part. In particular, the Hugging Face Inference DLC comes with a pre-written serving stack which drastically lowers the technical bar of deep learning serving.
Our DLCs are available everywhere Amazon SageMaker is available. While it is possible to use the DLCs without the SageMaker Python SDK, there are many advantages to using SageMaker to train your model:
Cost-effective: Training instances are only live for the duration of your job. Once your job is complete, the training cluster stops, and you won’t be billed anymore. SageMaker also supports Spot instances, which can reduce costs up to 90%.
Built-in automation: SageMaker automatically stores training metadata and logs in a serverless managed metastore and fully manages I/O operations with S3 for your datasets, checkpoints, and model artifacts.
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Hugging Face DLCs are open source and licensed under Apache 2.0. Feel free to reach out on our community forum if you have any questions. For premium support, our Expert Acceleration Program gives you direct dedicated support from our team.
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Hugging Face Deep DLCs make it easier than ever to train Transformer models in SageMaker. Here is why you should consider using Hugging Face DLCs to train and deploy your next machine learning models:
One command is all you need
With the new Hugging Face DLCs, train cutting-edge Transformers-based NLP models in a single line of code. Choose from multiple DLC variants, each one optimized for TensorFlow and PyTorch, single-GPU, single-node multi-GPU, and multi-node clusters.
Accelerate machine learning from science to production
In addition to Hugging Face DLCs, we created a first-class Hugging Face extension for the SageMaker Python SDK to accelerate data science teams, reducing the time required to set up and run experiments from days to minutes.
You can use the Hugging Face DLCs with SageMaker’s automatic model tuning to optimize your training hyperparameters and increase the accuracy of your models.
Deploy your trained models for inference with just one more line of code or select any of the 10,000+ publicly available models from the model Hub and deploy them with SageMaker.
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Hugging Face DLCs feature built-in performance optimizations for PyTorch and TensorFlow to train NLP models faster. The DLCs also give you the flexibility to choose a training infrastructure that best aligns with the price/performance ratio for your workload.
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Resources, Documentation & Samples 📄
Take a look at our published blog posts, videos, documentation, sample notebooks and scripts for additional help and more context about Hugging Face DLCs on SageMaker.
Blogs and videos
AWS: Embracing natural language processing with Hugging Face
Deploy Hugging Face models easily with Amazon SageMaker
AWS and Hugging Face collaborate to simplify and accelerate adoption of natural language processing models
Walkthrough: End-to-End Text Classification
Working with Hugging Face models on Amazon SageMaker
Distributed Training: Train BART/T5 for Summarization using 🤗 Transformers and Amazon SageMaker
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Deploy a Hugging Face Transformers Model from the Model Hub to Amazon SageMaker
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Image Classification with Vision Transformer
Deploy one of the 10 000+ Hugging Face Transformers to Amazon SageMaker for Inference
Deploy a Hugging Face Transformer model from S3 to SageMaker for inference |
https://huggingface.co/amazon/sm-hackathon-actionability-9-multi-outputs-setfit-all-distilroberta-model-v0.2 | amazon/sm-hackathon-actionability-9-multi-outputs-setfit-all-distilroberta-model-v0.2
This is a SetFit model that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
Fine-tuning a Sentence Transformer with contrastive learning.
Training a classification head with features from the fine-tuned Sentence Transformer.
Usage
To use this model for inference, first install the SetFit library:
python -m pip install setfit
You can then run inference as follows:
from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("amazon/sm-hackathon-actionability-9-multi-outputs-setfit-all-distilroberta-model-v0.2") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
BibTeX entry and citation info
@article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } |
https://huggingface.co/allenai | Research interests
AI for the Common Good
models 203
datasets 62 |
https://huggingface.co/datasets/emrgnt-cmplxty/sciphi-textbooks-are-all-you-need | Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Imagine you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "Marine Biology: Unveiling the Ocean's Depths - A Detailed Study of Marine Biodiversity using Satellite Tags". Your **first task** is to write an outline for the chapter. Your **second task** is to compose a eighteen paragraph page from the chapter. Your **last task** is to create a set of assessment questions that test the reader's understanding of the chapter. Notes: - Provide historical context or background information where relevant. Aim for a well-rounded and insightful response, keeping in mind the diversity in audience knowledge. ### Response:
**Chapter Outline:** 1. Introduction - Brief history of marine biology research - Importance of understanding marine biodiversity - Overview of satellite tagging in marine research 2. Satellite Tagging Techniques - Types of satellite tags - Tag design and materials - Attachment methods for marine animals - Tagging protocols and ethical considerations 3. Tracking and Data Analysis - Satellite communication technology - Data transmission and storage - Data analysis techniques for tracking animal movements and behavior - Limitations and accuracy of satellite tag data 4. Applications of Satellite Tagging - Population dynamics and migration studies - Ecosystem modeling and management - Human impacts on marine ecosystems - Conservation and management strategies 5. Case Studies - Maritime mammals (e.g., whales, dolphins) - Seabirds - Coastal and open-ocean fish species - Invertebrates (e.g., tuna, sharks) 6. Future Directions and Challenges - Developing more advanced satellite tag technology - Overcoming data transmission limitations - Ethical considerations and potential risks - Integrating satellite tag data with other research methods **Sample Page from the Chapter:** 1. In the early days of oceanographic research, scientists struggled to understand the complex marine ecosystems and the diverse creatures that inhabited them. Some of the earliest studies of marine life involved the collection of specimens and the analysis of their anatomy, physiology, and behavior. While this approach provided valuable insights into the biology of marine organisms, it had several limitations. It was often time-consuming and could harm the delicate ecosystems where these creatures lived. Additionally, it was difficult to track the movements of mobile species, such as marine mammals and fish, over long distances or through their entire lifecycles. 2. Satellite tags have revolutionized our ability to study and monitor marine life, providing researchers with unprecedented insights into the behavior and ecology of these creatures. Developed in the 1960s by Dr. Robert G. Doyle, satellite tags are small, lightweight devices designed to transmit data about an animal's movements in real-time. These tags can be attached to animals using non-invasive methods, allowing the study of wild populations without harming the subjects. 3. Satellite tags work by transmitting data through a network of ground stations and satellites. The tag sends out a signal when it comes into range of a ground station, providing information about the animal's location, movement speed, and other relevant metrics. This data is then transmitted to researchers for analysis and interpretation. Advances in satellite technology have led to more accurate and longer-lasting tags, allowing researchers to study animals over longer periods and at greater distances. 4. Researchers use satellite tag data to study a wide range of topics, including population dynamics and migration patterns, ecosystem modeling, and human impacts on marine ecosystems. For example, satellite tags have been used to monitor the movements of humpback whales, revealing the vast distances they travel each year in search of food. Similarly, satellite tags have been attached to sea turtles to study their migratory patterns and the factors that influence their survival rates. 5. Despite the numerous advantages of satellite tagging, there are still challenges to overcome. Developing more advanced satellite tag technology will allow for smaller, more durable, and more power-efficient devices, which will enable researchers to study a broader range of species and ecosystems. Additionally, overcoming data transmission limitations will enable researchers to collect and analyze data in real-time, providing a more comprehensive understanding of the animals being studied. **Assessment Questions:** 1. What is the primary goal of using satellite tags in marine research? 2. Describe the various types of satellite tags used in marine research. 3. What are the ethical considerations involved in using satellite tags on marine animals? 4. How do satellite tags contribute to our understanding of marine ecosystems and biodiversity? 5. Discuss the limitations of satellite tag data and potential ways to overcome these limitations. 6. In what ways have satellite tags advanced our understanding of marine life and ecosystems?
write an outline for the chapter
compose a eighteen paragraph page from the chapter
create a set of assessment questions that test the reader's understanding of the chapter
- Provide historical context or background information where relevant. Aim for a well-rounded and insightful response, keeping in mind the diversity in audience knowledge.
"Marine Biology: Unveiling the Ocean's Depths - A Detailed Study of Marine Biodiversity using Satellite Tags
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Imagine you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "Statistics: Predictive Analysis in Weather Forecasting using Python Programming". Your **first task** is to write an outline for a short essay on the chapter. Your **second task** is to compose a four paragraph essay on the chapter. Your **last task** is to demonstrate a fundamental idea from your response through an example with Python code. Notes: - Your target audience consists of experts with great familiarity. - Focus solely on the current state and future implications of the subject. ### Response:
**Outline:** I. Introduction to the concept of predictive analysis in weather forecasting - Brief history and evolution of weather forecasting - Importance of predictive analysis in modern-day forecasting II. Exploration of Python's role in predictive weather analysis - Overview of Python's core features and relevance to data analysis - Popular Python libraries and tools used for weather forecasting (e.g., NumPy, Pandas, Matplotlib, etc.) III. Statistical techniques employed in predictive weather analysis using Python - Regression models - Time series analysis - Machine learning algorithms (e.g., Random Forests, Support Vector Machines, etc.) IV. Real-world applications and future implications - Current state and successes of predictive weather analysis using Python - Potential future advancements and improvements in forecasting accuracy **Essay:** Python has revolutionized the field of data analysis, enabling experts and enthusiasts alike to uncover hidden patterns and trends in various datasets. One such prominent application of Python's data analysis prowess is its role in predictive weather forecasting. This essay aims to explore the potential of Python in predicting weather patterns and its future implications. The intersection of Python and weather forecasting primarily lies in the realm of statistical techniques and machine learning algorithms. Regression models, time series analysis, and machine learning algorithms such as random forests and support vector machines are some of the methods that Python experts employ to make sense of the intricate data associated with weather forecasting. These techniques allow forecasters to predict future weather patterns based on historical data, thereby enhancing the accuracy and reliability of weather forecasts. In the current state, Python has shown considerable promise in improving weather forecasting accuracy. By harnessing the power of Python, weather forecasters can analyze vast amounts of data more efficiently and draw actionable insights from the same. For instance, a weather forecasting system can utilize Python's Pandas library to clean and preprocess the data, NumPy to perform mathematical operations, and Matplotlib to visualize the patterns in the data. Looking towards the future, the possibilities are endless. With advancements in machine learning algorithms and an ever-growing repository of weather data, Python's potential in predictive weather forecasting seems limitless. The integration of artificial intelligence and machine learning techniques could lead to a more precise and personalized approach to weather forecasting, ultimately benefiting a diverse range of industries and stakeholders. **Python Example:** Here's a simple example to demonstrate a fundamental idea from the essay: linear regression using Python. We will use the popular library, scikit-learn, to perform a linear regression on a hypothetical dataset of weather conditions and temperature. ```python import numpy as np from sklearn.linear_model import LinearRegression # Hypothetical weather conditions and temperature data X = np.array([[0, 1], [2, 3], [4, 5], [6, 7], [8, 9]]) y = np.array([2, 4, 6, 8, 10]) # Create a linear regression model model = LinearRegression() # Train the model using the data model.fit(X, y) # Predict the temperature for a new set of weather conditions new_conditions = np.array([[10, 12]]) predicted_temperature = model.predict(new_conditions) print("Predicted temperature for the new conditions: ", predicted_temperature) ``` This code snippet trains a linear regression model on the hypothetical dataset and uses it to predict the temperature for a new set of weather conditions. This is a basic demonstration of how Python can be used to apply statistical techniques in the field of weather forecasting.
write an outline for a short essay on the chapter
compose a four paragraph essay on the chapter
demonstrate a fundamental idea from your response through an example with Python code
- Your target audience consists of experts with great familiarity. - Focus solely on the current state and future implications of the subject.
"Statistics: Predictive Analysis in Weather Forecasting using Python Programming
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Imagine you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "The Role of the Compass in the Age of Exploration and the Astrolabe Era". Your **first task** is to write four paragraphs from the introduction page of the chapter. Your **last task** is to write a single paragraph on a related subject. Notes: - Write in a balanced tone, mixing formality with accessibility. - Compare and contrast different theories, methodologies, or viewpoints within the subject. You will be graded on how complete and appropriate your response is. ### Response:
In the Age of Exploration, the compass played a pivotal role in navigation and mapping. This era, spanning from the 15th to 17th centuries, saw significant advancements in maritime exploration, trade, and colonization. The compass, along with the astrolabe, were the primary tools used by navigators to determine their location and guide them on their journeys. Despite their reliance on these instruments, there were still many unanswered questions and theories surrounding their effectiveness. One theory suggests that the compass's accuracy was often limited by the interference of magnetic fields generated by the Earth itself. This interference, known as magnetic declination, caused the needle to point in a direction that deviated from true north. Navigators had to contend with these fluctuating magnetic fields and adjust their compass accordingly to maintain an accurate heading. Additionally, the compass was only as good as the quality of the needle used, as a weak or poorly aligned needle could lead to inaccurate readings. On the other hand, the astrolabe served as a crucial tool for astronomical calculations and celestial navigation. The astrolabe's reflector allowed for the observation of the sun or stars, enabling navigators to determine their latitude. However, the astrolabe's use was limited by its bulkiness and weight, making it less practical for long voyages, especially in the Age of Exploration when space was at a premium. Moreover, the astrolabe's reliance on the sun often posed challenges during periods of overcast or cloudy weather. As the Age of Exploration came to a close and the Age of Discovery began, navigators began to rely more on mathematical methods and new technologies, such as sextants and chronometers, to improve upon the compass and astrolabe. These advancements in navigation marked a significant shift in how humans explored and mapped the world. However, the compass and astrolabe's contributions to the Age of Exploration will forever be remembered as indispensable tools that aided in the expansion of human knowledge and the movement of people and goods around the globe. The development of navigation technology in the Age of Exploration and the Astrolabe Era was a gradual and collaborative process. While the compass and astrolabe were essential tools, advancements in technology and understanding of the Earth's magnetic fields led to more accurate and practical navigation methods. This period in history serves as a testament to the importance of innovation and collaboration in the pursuit of human advancement.
write four paragraphs from the introduction page of the chapter
write a single paragraph on a related subject
- Write in a balanced tone, mixing formality with accessibility. - Compare and contrast different theories, methodologies, or viewpoints within the subject. You will be graded on how complete and appropriate your response is.
"The Role of the Compass in the Age of Exploration and the Astrolabe Era
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Imagine you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "Harmonic Vibrations: Investigating Light Waves using Spectrometers and Diffraction Gratings". Your **first task** is to write a page from the middle of chapter. Notes: - Provide historical context or background information where relevant. You will be graded on how complete and appropriate your response is. ### Response:
Title: Harmonic Vibrations: Investigating Light Waves using Spectrometers and Diffraction Gratings Chapter 3: The Evolution of Spectroscopy and Diffraction In the early days of the 19th century, the study of light and its interactions with matter was a prevalent area of research among scientists. This fascination led to the development of spectroscopy, a branch of physics that studies the interaction of light with matter. The concept of harmonic vibrations, which refers to periodic oscillations that propagate through a medium, played a pivotal role in the understanding of light waves. In this chapter, we will delve into the historical context of spectroscopy and diffraction gratings, two essential tools in the study of harmonic vibrations. - The origins of spectroscopy can be traced back to the work of Sir Isaac Newton, who discovered in 1672 that white light could be separated into a spectrum of colors by passing it through a prism. This groundbreaking discovery initiated the development of the optical spectroscope, which allowed scientists to study the chemical composition of celestial bodies and the Earth's atmosphere. - The concept of diffraction gratings was introduced by Sir William Herschel in the early 19th century. Herschel, an English astronomer, is best known for his groundbreaking work in infrared spectroscopy. He discovered that the light from the sun was composed of a spectrum of colors, which allowed him to observe the chemical elements present in the sun's atmosphere. Herschel also developed the first practical diffraction grating, an instrument that could split white light into its constituent colors. - The invention of the first practical spectrometer, the monochromator, by the German physicist Christian Fraunhofer in the mid-19th century, marked another significant milestone in the field of spectroscopy. Fraunhofer's spectrometer allowed scientists to analyze and measure the wavelengths of specific colors within a white light spectrum. In the 20th century, advancements in spectrometer technology led to the development of more sophisticated instruments, such as the Fabry-Pérot interferometer, which allows for the precise measurement of the wavelengths of light waves. Today, spectroscopy and diffraction gratings are widely used in various scientific disciplines, including astronomy, chemistry, and geology, to investigate the properties and composition of celestial bodies, materials, and atmospheres. In conclusion, the study of harmonic vibrations has been significantly advanced by the development of spectroscopy and diffraction gratings. These tools have played a crucial role in the understanding of the nature of light and its interactions with matter, providing scientists with a deeper insight into the fundamental properties of the universe. As technology continues to advance, it is likely that the study of harmonic vibrations and the use of spectroscopy and diffraction gratings will continue to evolve and expand our knowledge of the world around us.
write a page from the middle of chapter
- Provide historical context or background information where relevant. You will be graded on how complete and appropriate your response is.
"Harmonic Vibrations: Investigating Light Waves using Spectrometers and Diffraction Gratings
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Pretend you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "Advanced Cryptography: Quantum Key Distribution in Modern Secure Communication Networks". Your **first task** is to write an outline for a random page from the middle of the chapter. Your **second task** is to compose the random page. Notes: - Your target audience consists of beginners with no familiarity of the subject. Strive for completeness and appropriateness in your response, keeping in mind the diversity of the audience. ### Response:
### Outline: 1. Introduction to Quantum Key Distribution - Definition and importance - Overview of traditional key distribution methods - Limitations of traditional methods in the context of quantum communication 2. Quantum Cryptography Basics - Quantum states and classical bits - Quantum entanglement - Quantum key distribution protocols 3. Quantum Key Distribution in Modern Secure Communication Networks - Role of quantum key distribution in secure communication - Examples of quantum secure communication protocols (e.g., BB84, E91) - Challenges in implementing quantum key distribution in practice 4. Quantum Key Distribution and Post-Quantum Cryptography - The role of quantum key distribution in post-quantum cryptography - The future of quantum key distribution in a post-quantum world - The potential impact of quantum computing on quantum key distribution 5. Conclusion - Recap of key concepts - Importance of understanding quantum key distribution for secure communication - Encouragement for further exploration ### Page: Quantum Key Distribution in Modern Secure Communication Networks Quantum Key Distribution in Modern Secure Communication Networks Quantum Key Distribution (QKD) is a revolutionary technique that enables secure communication in the presence of powerful adversaries, such as those possible with quantum computing capabilities. It leverages the principles of quantum mechanics, particularly quantum entanglement and quantum states, to create shared secrets that cannot be intercepted or eavesdropped. This makes QKD crucial for maintaining secure communication in a post-quantum world, where traditional cryptographic methods may become vulnerable. In modern secure communication networks, QKD plays a vital role in providing the keys for various cryptographic protocols. These protocols ensure the confidentiality, integrity, and authenticity of the transmitted data. Some examples of QKD-enabled protocols include the BB84 protocol and the E91 protocol. These protocols have been rigorously tested and demonstrated to provide strong security against various attacks, including those from quantum adversaries. However, implementing QKD in practice presents several challenges. These challenges include the need for high-fidelity quantum states, reliable entanglement generation, and secure management of the generated entangled pairs. Despite these challenges, ongoing research and development efforts are making strides towards overcoming them and paving the way for widespread adoption of QKD in secure communication networks. In summary, quantum key distribution is a fundamental component of secure communication in the context of modern networks. Its importance will only grow as we move towards a post-quantum world, where traditional cryptographic methods may become less effective. By understanding the principles behind QKD and the challenges in implementing it, we can better prepare ourselves for the future of secure communication.
write an outline for a random page from the middle of the chapter
- Your target audience consists of beginners with no familiarity of the subject. Strive for completeness and appropriateness in your response, keeping in mind the diversity of the audience.
"Advanced Cryptography: Quantum Key Distribution in Modern Secure Communication Networks
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Imagine you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "Galactic Astrophysics: Unraveling the Mysteries of Black Holes through Radio Astronomy". Your **first task** is to write an outline for the chapter. Your **second task** is to compose a fifteen paragraph page from the chapter. Notes: - Your target audience consists of beginners with no familiarity of the subject. - Adopt a conversational and informal style to make the content accessible. - Compare and contrast different theories, methodologies, or viewpoints within the subject. Aim for a well-rounded and insightful response, keeping in mind the diversity in audience knowledge. ### Response:
## Outline: I. Introduction - Brief overview of black holes - Importance of understanding black holes - Importance of radio astronomy in understanding black holes II. What is a Black Hole? - Definition of a black hole - Formation of black holes - Different types of black holes III. Radio Astronomy: A Key Tool for Black Hole Research - Basics of radio astronomy - Importance of radio telescopes in black hole research - History of radio astronomy IV. The Two Theories of Black Holes - The singularity theory - The event horizon theory - Comparison and contrast between the two theories V. The Search for Black Holes - Methods used to search for black holes - The discovery of Cygnus X-1 - The role of radio telescopes in the search for black holes VI. The Role of Pulsars in Black Hole Research - Definition of a pulsar - The connection between pulsars and black holes - The importance of pulsars in understanding black hole behavior VII. The Mystery of the Blandford-Znajek Process - Definition and explanation of the Blandford-Znajek Process - The importance of understanding the Blandford-Znajek Process - The role of radio astronomy in unraveling this mystery VIII. The Future of Black Hole Research - The role of advanced radio telescopes in future black hole research - The potential for new discoveries - The importance of continued research in this field IX. The Relevance to Everyday Life - How the study of black holes can help us understand our universe - The potential applications of this knowledge - The importance of promoting scientific literacy X. Conclusion - Summary of the chapter's main points - The continued importance of black hole research - Encouragement for further study
write an outline for the chapter
compose a fifteen paragraph page from the chapter
- Your target audience consists of beginners with no familiarity of the subject. - Adopt a conversational and informal style to make the content accessible. - Compare and contrast different theories, methodologies, or viewpoints within the subject. Aim for a well-rounded and insightful response, keeping in mind the diversity in audience knowledge.
"Galactic Astrophysics: Unraveling the Mysteries of Black Holes through Radio Astronomy
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Imagine you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "Understanding the Universe: Quantum Mechanics and String Theory - An In-depth Study". Your **first task** is to write an outline for the introduction for the chapter. Your **second task** is to compose the introduction. Notes: - Provide historical context or background information where relevant. You will be graded on how complete and appropriate your response is. ### Response:
Introduction Outline: 1. Introduction to the chapter topic: "Understanding the Universe: Quantum Mechanics and String Theory - An In-depth Study" - Briefly explain what the reader can expect to learn from this chapter 2. Historical context and background: - Brief overview of the development of quantum mechanics and string theory - Key figures and their contributions to the field 3. Importance of quantum mechanics and string theory in understanding the universe - Connection to other branches of physics (e.g., relativity, cosmology) - Role in unifying theoretical frameworks 4. Overview of the chapter's structure and organization - Topics to be covered in depth - The use of examples, analogies, and visual aids 5. Excitement and potential for future discoveries - The ongoing nature of scientific inquiry - The potential for groundbreaking advancements in our understanding of the universe Introduction: Welcome to the chapter of "Understanding the Universe: Quantum Mechanics and String Theory - An In-depth Study." In this chapter, we will embark on a journey to explore the complexities and wonders of the universe through the lens of quantum mechanics and string theory. Throughout history, the quest for understanding has driven humanity's pursuit of knowledge in the realms of physics, mathematics, and philosophy. This journey began with ancient philosophers like Aristotle, who pondered the nature of reality. The development of quantum mechanics and string theory as modern scientific theories is a testament to the power of human curiosity and intellectual prowess. Quantum mechanics, pioneered by figures such as Niels Bohr, Werner Heisenberg, and Erwin Schrödinger, revolutionized our understanding of the atomic world. This theory provided a framework for describing the behavior of microscopic particles and their interactions, defying the deterministic nature of classical mechanics. String theory, an ambitious theoretical framework, seeks to unify all four fundamental forces of nature: gravity, electromagnetism, strong nuclear force, and weak nuclear force. Proposed by physicists like Sheldon Glashow, Abdus Salam, and Steven Weinberg, string theory suggests that the fundamental particles are not point-like, but rather one-dimensional "strings" that vibrate at different frequencies. Quantum mechanics and string theory play a crucial role in our understanding of the universe. They have deep connections to other branches of physics like relativity and cosmology, offering a unified framework to explain the intricacies of our cosmos. The ongoing nature of scientific inquiry and the potential for groundbreaking advancements in our understanding of the universe make this chapter an exciting exploration. This chapter is organized to provide a comprehensive understanding of quantum mechanics and string theory. We will delve into the core concepts, historical context, and the role of these theories in our quest for cosmic enlightenment. Along the way, we will use examples, analogies, and visual aids to help clarify the complexities of these ideas. So, sit back, relax, and embark on this thrilling journey to understand the universe through the lens of quantum mechanics and string theory. Prepare to have your mind blown as we unravel the mysteries of the cosmos.
write an outline for the introduction for the chapter
- Provide historical context or background information where relevant. You will be graded on how complete and appropriate your response is.
"Understanding the Universe: Quantum Mechanics and String Theory - An In-depth Study
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Pretend you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "Advanced Mathematics: Deriving Financial Models through Game Theory". Your **first task** is to write an outline for a medium length essay on the chapter. Your **second task** is to compose a five paragraph essay. Your **last task** is to demonstrate a fundamental idea from your response through an example with Python code. Notes: - Adopt a conversational and informal style to make the content accessible. - Provide historical context or background information where relevant. - Keep the content concise and to the point. You will be graded on how complete and appropriate your response is. ### Response:
--- ### First Task: Outline for a Medium Length Essay I. Introduction A. Brief overview of the importance of advanced mathematics in the field of finance B. Explanation of how game theory can be used to derive financial models C. Description of the essay's structure and organization II. Origins of Game Theory A. Historical background of game theory and its development B. Key figures and contributions to the field C. Applications of game theory in economics and other disciplines III. Basic Concepts of Game Theory A. Definition of a game in game theory B. Types of games (cooperative, zero-sum, etc.) C. Notion of equilibrium and its significance in game theory IV. Financial Applications of Game Theory A. Derivation of financial models through game theory B. Examples of real-world financial models (e.g., auctions, options pricing) C. Limitations and challenges in applying game theory to finance V. Conclusion A. Recap of the chapter's main points B. Emphasis on the value of advanced mathematics in deriving financial models through game theory C. Final thoughts on the potential future development of this interdisciplinary field --- ### Second Task: Five Paragraph Essay In the early days of economics, mathematicians and economists alike struggled to find a way to quantify the complexities of financial decision-making. This led to the birth of game theory, a branch of mathematics that has since revolutionized our understanding of financial decision-making. Game theory was developed in response to the limitations of traditional economics, which often relied on simplistic assumptions and had difficulty capturing the nuances of human behavior in economic decision-making. By introducing a more rigorous mathematical framework, game theorists were able to develop more sophisticated models of decision-making, which have since become indispensable tools in finance. One of the key concepts in game theory is the concept of an equilibrium, which refers to a state in which no player has an incentive to change their strategy. This concept is crucial in finance, as it helps us understand how financial markets reach an equilibrium price for assets. For example, in an auction, an equilibrium price is reached when the highest bidder is willing to pay more than the second-highest bidder, but not so much that the seller would prefer to sell to the second highest bidder. In the world of finance, game theory has led to significant advancements, such as the development of the Black-Scholes model for option pricing. This model is a cornerstone of modern finance and has been widely adopted by investors and financial institutions. However, despite its successes, game theory also has its limitations, such as the challenge of incorporating uncertainty and information asymmetry into the model. In conclusion, game theory has played a vital role in transforming our understanding of financial decision-making. By introducing a rigorous mathematical framework, game theory has provided a powerful tool for modeling and analyzing financial markets. While it has undoubtedly contributed to the development of sophisticated financial models, there is still much work to be done in incorporating the full complexity of real-world financial markets into game theory. As the field of finance continues to evolve, game theory will undoubtedly play a critical role in shaping the future of this dynamic discipline. --- ### Last Task: Fundamental Idea from the Response through Python Code One fundamental idea in game theory is the concept of a normal-form game, which is a game where players are anonymous and the order of play does not matter. In a 2-player, 2-strategy normal-form game, the payoffs for each player can be represented as a matrix. Here's an example of how to represent such a game using Python: ```python # Define the payoffs for each player in a 2-player, 2-strategy game player_1_payoffs = [3, 1] # Player 1's payoffs when choosing strategy 1 and 2, respectively player_2_payoffs = [4, 2] # Player 2's payoffs when choosing strategy 1 and 2, respectively # Create a matrix to represent the game game_matrix = [player_1_payoffs, player_2_payoffs] print("Game matrix:", game_matrix) ``` This code defines the payoffs for each player in a simple 2-player, 2-strategy game and creates a matrix to represent it. This matrix can then be used as a starting point for further analysis using game theory concepts, such as finding the Nash equilibrium of the game.
write an outline for a medium length essay on the chapter
compose a five paragraph essay
demonstrate a fundamental idea from your response through an example with Python code
- Adopt a conversational and informal style to make the content accessible. - Provide historical context or background information where relevant. - Keep the content concise and to the point. You will be graded on how complete and appropriate your response is.
"Advanced Mathematics: Deriving Financial Models through Game Theory
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Imagine you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "Culinary Chemistry: Understanding the Science of Sour Dough Fermentation". Your **first task** is to write sixteen paragraphs from the introduction page of the chapter. Your **last task** is to demonstrate a fundamental idea from your response through an example with Python code. Notes: - Focus solely on the current state and future implications of the subject. - Keep the content concise and to the point. You will be graded on how complete and appropriate your response is. ### Response:
The Culinary Chemistry: Understanding the Science of Sour Dough Fermentation textbook chapter aims to provide a comprehensive understanding of the chemical processes that occur during the fermentation of sour dough. This includes discussing the various ingredients and their roles in the fermentation process, the enzymes and bacteria involved, and the environmental factors that influence the outcome. Sour dough is a popular component of various cuisines around the world, such as pizza, sourdough bread, and flatbreads. It is characterized by a tangy, sour flavor that results from the fermentation of lactic acid bacteria. The fermentation process is a complex interaction between various chemical species, including the dough ingredients, water, and air, as well as the enzymes and bacteria present in the mixture. As such, it represents an ideal subject for exploring the fascinating realm of culinary chemistry. The primary ingredients in sour dough are water, flour, and salt, with yeast and bacteria playing crucial roles in the fermentation process. The flour provides the necessary carbohydrates for the yeast and bacteria to consume, while the water and salt contribute to the overall flavor and texture of the dough. The precise composition of the dough, including the type of flour used and the ratio of water to flour, can significantly impact the final product's sourness and other characteristics. Enzymes and bacteria play a pivotal role in the sour dough fermentation process. The bacteria, particularly Lactobacillus and Pediococcus species, produce lactic acid as a byproduct of consuming the available carbohydrates. This lactic acid reacts with water to form carbon dioxide and lactic acid, generating the sour flavor and acidic pH that define sour dough. The yeast, such as Saccharomyces cerevisiae, also contribute to the fermentation process by consuming the available carbohydrates and producing alcohol and carbon dioxide. The environment in which the sour dough is prepared and fermented also plays a crucial role in the final product's characteristics. Factors such as temperature, humidity, and the presence of other microorganisms can influence the growth of the lactic acid bacteria and the type of lactic acid produced. Furthermore, the duration of the fermentation process, which can be influenced by factors such as the dough's thickness and the rate of mixing, can impact the final product's sourness and other properties. In conclusion, the Culinary Chemistry: Understanding the Science of Sour Dough Fermentation chapter will provide readers with a comprehensive understanding of the chemical processes that underlie this unique culinary phenomenon. By exploring the roles of ingredients, enzymes, bacteria, and environmental factors in the fermentation process, this textbook offers a valuable resource for anyone interested in the world of culinary chemistry. Here is a basic Python code example to demonstrate the concept of lactic acid production by lactic acid bacteria: ```python # Constants initial_lactose = 10 # grams initial_lactic_acid_bacteria = 1000 # cells conversion_rate = 0.5 # grams of lactose converted per cell per hour time_elapsed = 24 # hours # Calculate the lactic acid produced lactic_acid = (initial_lactose * conversion_rate * initial_lactic_acid_bacteria * time_elapsed) / 1000 print(f"Lactic acid produced: {lactic_acid} milligrams") ``` This code calculates the amount of lactic acid produced by a given number of lactic acid bacteria over a specific time period, assuming a constant conversion rate. This simple example illustrates the underlying science of sour dough fermentation and serves as a starting point for further exploration of the subject.
write sixteen paragraphs from the introduction page of the chapter
demonstrate a fundamental idea from your response through an example with Python code
- Focus solely on the current state and future implications of the subject. - Keep the content concise and to the point. You will be graded on how complete and appropriate your response is.
"Culinary Chemistry: Understanding the Science of Sour Dough Fermentation
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Imagine you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "Chemical Engineering: Application of Distillation Principles in Petroleum Refinery Operations". Your **first task** is to write a page from the middle of chapter. Notes: - Focus solely on the current state and future implications of the subject. ### Response:
In the field of Chemical Engineering, distillation principles have been a vital component in the petroleum refinery operations for many years. As the global demand for energy continues to grow, the importance of understanding and improving these processes cannot be overstated. This chapter will delve into the current applications of distillation principles in the petroleum refinery sector and explore the potential future implications of these advancements. Distillation is a fundamental process used in the petroleum refinery industry to separate mixtures based on their volatility. This process is crucial in the production of gasoline, diesel, and other petroleum-based products. By understanding the principles behind distillation, chemical engineers can optimize the process, leading to increased efficiency and reduced environmental impact. For example, the production of gasoline from crude oil requires the distillation of a mixture of hydrocarbons. The process involves heating the mixture and allowing the more volatile compounds, such as ethylene and propylene, to vaporize and condense separately. This separation allows for the purification of gasoline, which is then stored and sold for use as fuel. The ability to optimize this process has significant implications for the industry, as it can lead to improved production efficiency and reduced waste. In addition to gasoline production, distillation principles are also integral in the production of diesel fuel. The differentiation of diesel from gasoline in the refining process is primarily achieved through the use of column stills, which separate the two based on their boiling points. By understanding and applying the principles of distillation in this context, chemical engineers can further optimize the process and reduce the environmental impact of diesel fuel production. As the demand for energy continues to grow, the importance of understanding and improving distillation principles in petroleum refinery operations cannot be overstated. This will be particularly crucial as the industry looks to develop new, more sustainable methods for producing fuels, such as biofuels and synthetic fuels. By staying ahead of the curve in terms of understanding and applying distillation principles, chemical engineers can play a vital role in shaping the future of the petroleum refinery industry and contributing to a more sustainable global energy landscape. In conclusion, the current applications of distillation principles in petroleum refinery operations are vast and diverse, and their future implications are promising. By staying ahead of the curve and understanding the principles behind these processes, chemical engineers can play a pivotal role in the continued growth and sustainability of the petroleum refinery industry. This chapter has provided a comprehensive overview of the topic, highlighting the significance of distillation principles in this context and offering insights into potential future developments.
write a page from the middle of chapter
- Focus solely on the current state and future implications of the subject.
"Chemical Engineering: Application of Distillation Principles in Petroleum Refinery Operations
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Pretend you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "Advanced Physics: Exploring the Wonders of Quantum Entanglement and Bell's Theorem". Your **first task** is to write an outline for the introduction for the chapter. Your **second task** is to compose the introduction. Notes: - Your target audience consists of beginners with no familiarity of the subject. - Adopt a conversational and informal style to make the content accessible. - Provide historical context or background information where relevant. ### Response:
## Outline for the Introduction 1. Introduction to Quantum Entanglement - Definition of Quantum Entanglement - Importance of understanding Quantum Entanglement 2. Background of Quantum Entanglement - Historical context of Quantum Entanglement - Notable figures and events in Quantum Entanglement research 3. Bell's Theorem - Definition of Bell's Theorem - Significance of Bell's Theorem in the field of Quantum Entanglement 4. Exploring the Wonders of Quantum Entanglement and Bell's Theorem - What to expect from the chapter - Topics covered in detail ## Introduction Welcome to our chapter on "Advanced Physics: Exploring the Wonders of Quantum Entanglement and Bell's Theorem." In this chapter, we will be diving deep into the fascinating world of Quantum Entanglement and Bell's Theorem. These concepts might sound like they belong in a sci-fi movie, but they're real, and they're part of our understanding of the universe. Quantum entanglement is a phenomenon that occurs when particles become interconnected in such a way that the state of one particle instantly reflects the state of the other, no matter how far apart they are. This can lead to some mind-boggling consequences, like the famous "spooky action at a distance." Bell's Theorem, on the other hand, is a testable prediction that challenges our understanding of quantum mechanics. It states that no local hidden variable theory can reproduce all the predictions of quantum mechanics, which implies that either quantum mechanics must be revised, or our understanding of the world must include these non-local effects of entanglement. This chapter aims to provide a comprehensive introduction to these concepts, from their historical background to their current state of understanding. We will discuss the implications of Quantum Entanglement and Bell's Theorem for our understanding of the universe and for potential future technologies. So, buckle up and get ready to embark on a journey through the wonders of Quantum Entanglement and Bell's Theorem. We promise you'll never look at the world the same way again!
write an outline for the introduction for the chapter
- Your target audience consists of beginners with no familiarity of the subject. - Adopt a conversational and informal style to make the content accessible. - Provide historical context or background information where relevant.
"Advanced Physics: Exploring the Wonders of Quantum Entanglement and Bell's Theorem
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Pretend you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "Investigating the Potential of Renewable Energy: Harnessing Wind Power with Turbine Technological Innovations". Your **first task** is to write an outline for a medium length essay on the chapter. Your **second task** is to compose a seven paragraph essay on the chapter. Your **last task** is to demonstrate a key idea from your response through with a Python example. Notes: - Adopt a conversational and informal style to make the content accessible. You will be graded on how complete and appropriate your response is. ### Response:
**Outline for a medium length essay:** 1. Introduction - Brief overview of the chapter's topic - Importance of renewable energy sources - Focus on wind power and its potential 2. Historical background of wind power - Ancient civilizations using wind power - Development of wind power technology 3. Types of wind turbines - Horizontal axis wind turbines (HAWTs) - Vertical axis wind turbines (VAWTs) - Advantages and disadvantages of each type 4. Harnessing wind power through innovative technologies - Smart wind turbines - Wind energy storage systems - Wind power prediction and optimization software 5. Challenges in deploying large-scale wind power - Intermittency of wind power - Land use and environmental concerns - Balancing the grid with variable renewable energy sources 6. The future of wind power - Advancements in wind turbine technology - Potential to revolutionize the energy sector - International collaboration and policy incentives 7. Conclusion - Recap of the chapter's key points - Importance of continued research and development in wind power technologies **Seven paragraph essay:** 1. Wind power has come a long way since ancient civilizations first started utilizing it. In modern times, the potential of renewable energy sources has never been more significant. This essay will explore the topic of "Investigating the Potential of Renewable Energy: Harnessing Wind Power with Turbine Technological Innovations." 2. The history of wind power stretches back thousands of years, with ancient civilizations such as the Greeks and Romans using wind-powered mills. As technology progressed, so did wind power, with the development of larger, more efficient wind turbines. 3. There are two main types of wind turbines: horizontal axis wind turbines (HAWTs) and vertical axis wind turbines (VAWTs). HAWTs are more common and are used in both onshore and offshore settings. They have larger rotor diameters and can reach higher speeds. On the other hand, VAWTs are typically smaller and more compact, which makes them suitable for urban and small-scale applications. 4. The harnessing of wind power has been revolutionized by innovative technologies. Smart wind turbines can predict maintenance needs and optimize power output. Wind energy storage systems can store excess energy produced by wind turbines and release it when needed. Furthermore, software solutions can help predict wind patterns and optimize the energy grid. 5. However, deploying large-scale wind power comes with its challenges. The intermittency of wind power means that it cannot be relied upon as a constant source of energy. Land use and environmental concerns also need to be addressed when developing wind farms. Additionally, balancing the grid with variable renewable energy sources is a complex task. 6. Despite these challenges, the future of wind power is promising. Advancements in wind turbine technology are making these power sources more efficient and cost-effective. The potential to revolutionize the energy sector is immense, with international collaboration and policy incentives driving this progress. 7. In conclusion, the chapter on "Investigating the Potential of Renewable Energy: Harnessing Wind Power with Turbine Technological Innovations" highlights the significance of this renewable energy source and its potential for the future. By understanding the history, types of turbines, and innovations in the wind power industry, we can better appreciate the role wind power can play in a sustainable, greener future. **Python example demonstrating a key idea:** Let's say you have a list of wind speeds and you want to calculate the average wind speed. Here's a simple Python script that demonstrates this concept: ```python # List of wind speeds (in m/s) wind_speeds = [3.5, 2.8, 4.2, 3.8, 4.5] # Calculate the sum of all wind speeds total_speed = sum(wind_speeds) # Calculate the average wind speed average_speed = total_speed / len(wind_speeds) print("The average wind speed is:", average_speed, "m/s") ``` This script demonstrates a key idea from the essay - the calculation of average wind speed, which is a fundamental concept in understanding wind power and its potential.
write an outline for a medium length essay on the chapter
compose a seven paragraph essay on the chapter
demonstrate a key idea from your response through with a Python example
- Adopt a conversational and informal style to make the content accessible. You will be graded on how complete and appropriate your response is.
"Investigating the Potential of Renewable Energy: Harnessing Wind Power with Turbine Technological Innovations
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Pretend you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "4th Grade Biology: Understanding the Life Cycle of Butterflies through Metamorphosis Kits". Your **first task** is to write six paragraphs from the page concluding the chapter. Notes: - Your target audience consists of experts with great familiarity. - Write in a balanced tone, mixing formality with accessibility. - Compare and contrast different theories, methodologies, or viewpoints within the subject. - Provide a detailed and in-depth exploration of the topic. Aim for a well-rounded and insightful response, keeping in mind the diversity in audience knowledge. ### Response:
In conclusion, the life cycle of butterflies is a complex and fascinating process that spans several stages, each with its unique characteristics. Throughout this chapter, we have explored various aspects of metamorphosis kits, examining their role in the butterfly's development and the importance of their incorporation into 4th grade biology curricula. One of the key elements we have discussed is the role of the chrysalis in the butterfly's life cycle. The chrysalis serves as a protective cocoon where the butterfly's body undergoes a dramatic transformation, with the molting of its exoskeleton and the emergence of its wings. This metamorphic stage is crucial for the butterfly's survival, as it enables it to adapt to its environment and fulfill its life cycle objectives. The process of metamorphosis can be influenced by a variety of factors, such as temperature, humidity, and the type of food the caterpillar consumes. For example, some species have been found to undergo a more rapid metamorphosis in warmer climates, suggesting that these environmental conditions may play a significant role in determining the butterfly's life span. Another important aspect of the butterfly's life cycle is the role of its habitat. Studies have shown that specific habitats, such as forests and gardens, can significantly impact the diversity of butterfly species and their population dynamics. This highlights the importance of preserving and managing these habitats for the sake of biodiversity and the sustainability of butterfly populations. In addition to these ecological factors, genetics also plays a role in the butterfly's life cycle. Research has shown that genetic variations within butterfly populations can influence the timing and success of the metamorphic process, with some individuals exhibiting greater resilience to environmental stressors than others. Lastly, we have discussed the role of education and outreach in promoting a deeper understanding and appreciation for the life cycle of butterflies. By incorporating metamorphosis kits into 4th grade biology curricula, educators can engage students in hands-on, experiential learning experiences that foster a greater connection to the natural world. This, in turn, can lead to a more holistic understanding of the intricate processes that underpin the life cycle of these magnificent creatures. In summary, the life cycle of butterflies is a rich and diverse topic that offers countless opportunities for further exploration and investigation. Through our discussion in this chapter, we have sought to provide a balanced and comprehensive overview of the subject, emphasizing the importance of understanding the life cycle of butterflies and the significance of metamorphosis kits in promoting this understanding. We hope that this will serve as a valuable resource for both experts and those new to the subject, as we continue to unravel the mysteries of the butterfly world.
write six paragraphs from the page concluding the chapter
- Your target audience consists of experts with great familiarity. - Write in a balanced tone, mixing formality with accessibility. - Compare and contrast different theories, methodologies, or viewpoints within the subject. - Provide a detailed and in-depth exploration of the topic. Aim for a well-rounded and insightful response, keeping in mind the diversity in audience knowledge.
"4th Grade Biology: Understanding the Life Cycle of Butterflies through Metamorphosis Kits
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Imagine you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "9th Grade Biology: Understanding Human Physiology through Virtual Dissections". Your **first task** is to write an outline for a random page from the chapter. Your **second task** is to compose the random page. Your **last task** is to demonstrate a fundamental idea from your response through an example with Python code. Notes: - Your target audience consists of beginners with no familiarity of the subject. - Write in a balanced tone, mixing formality with accessibility. You will be graded on how complete and appropriate your response is. ### Response:
**Outline for a Random Page:** I. Introduction to Human Physiology - Definition of physiology - Importance of understanding physiology - Overview of the human body systems II. The Cardiovascular System - Definition and function of the heart - Role of blood vessels - Blood circulation process - Importance of a healthy heart and circulatory system III. The Respiratory System - Definition and function of the lungs - Role of the diaphragm and rib cage - Gas exchange process - Importance of a healthy respiratory system IV. The Digestive System - Definition and function of the digestive organs - Processes involved in food digestion and absorption - Importance of a healthy digestive system V. The Urinary System - Definition and function of the urinary organs - Processes involved in waste elimination - Importance of a healthy urinary system VI. The Endocrine System - Definition and function of the endocrine glands - Hormone production and transport - Importance of a healthy endocrine system VII. Conclusion - Recap of key points from the chapter - Importance of understanding human physiology **Random Page:** 1. Introduction to Human Physiology Physiology is the study of how the human body functions. It is a branch of biology that deals with the mechanisms of life, including the functions of various body systems. Understanding human physiology is crucial for maintaining good health and ensuring proper functioning of the body. In this textbook chapter, we will explore the various systems that make up the human body and discuss how they work together to keep us alive and healthy. We will delve into the cardiovascular, respiratory, digestive, urinary, and endocrine systems, and examine their roles in maintaining our overall health. 2. The Cardiovascular System The heart is the most important organ in the cardiovascular system. It is responsible for pumping blood throughout the body, ensuring that oxygen-rich blood is delivered to all cells and organs. The blood vessels, also known as arteries, veins, and capillaries, transport blood to and from the heart and the rest of the body. The blood circulation process involves the following steps: 1. The heart contracts and pumps blood into the arteries. 2. The arteries carry the blood to the rest of the body. 3. The blood enters the capillaries, where gas exchange occurs between the blood and the cells. 4. The blood then travels through the veins, returning to the heart. 5. The heart relaxes and prepares for the next contraction. A healthy heart and circulatory system are essential for maintaining overall health and well-being. **Python Example:** Let's use Python to demonstrate the concept of blood circulation. We'll create a simple program that simulates the flow of blood through the heart, arteries, capillaries, veins, and back to the heart: ```python def blood_circulation(): heart = "pumping blood" arteries = f"carrying {heart} to the rest of the body" capillaries = f"gas exchange occurs between blood and cells" veins = f"returning blood to the heart" heart_after_contraction = "preparing for the next contraction" print(f"1. Heart: {heart}") print(f"2. Arteries: {arteries}") print(f"3. Capillaries: {capillaries}") print(f"4. Veins: {veins}") print(f"5. Heart: {heart_after_contraction}") blood_circulation() ``` This program outputs the steps of blood circulation in a simplified, easily understandable format.
write an outline for a random page from the chapter
demonstrate a fundamental idea from your response through an example with Python code
- Your target audience consists of beginners with no familiarity of the subject. - Write in a balanced tone, mixing formality with accessibility. You will be graded on how complete and appropriate your response is.
"9th Grade Biology: Understanding Human Physiology through Virtual Dissections
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Imagine you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "Computer Science: The Impact of Quantum Computing on Future Technologies". Your **first task** is to write an introduction page for the chapter. Notes: - Your target audience consists of intermediates with some familiarity of the subject. - Maintain a formal and academic tone throughout the chapter. - Provide a detailed and in-depth exploration of the topic. Strive for completeness and appropriateness in your response, keeping in mind the diversity of the audience. ### Response:
# Computer Science: The Impact of Quantum Computing on Future Technologies In the realm of computer science, the impact of quantum computing on future technologies is a topic of increasing interest and importance. Quantum computing holds the promise of revolutionizing the way we process information, solve complex problems, and harness the power of exponentially growing data. This chapter aims to provide an in-depth exploration of the field, delving into the theoretical underpinnings, the current state of development, and the potential impact on various aspects of computer science. The advent of quantum computing, which leverages the principles of quantum mechanics to process information, has been heralded as a game-changer in the world of computing. This innovative approach allows for the simultaneous processing of multiple bits of data, enabling algorithms to run exponentially faster than traditional computing models. This has far-reaching implications for a wide range of industries and applications, including cryptography, optimization, and drug discovery. Some of the key concepts and developments in quantum computing include: - Quantum bits (qubits): These are the fundamental building blocks of a quantum computer, allowing for multiple states (0 or 1, and superpositions thereof) simultaneously. - Quantum gates: These are the logical operations that manipulate qubits, such as AND, OR, and NOT gates. - Quantum entanglement: This is a phenomenon where the state of one qubit is dependent on the state of another, regardless of the physical distance between them. - Quantum algorithms: These are computational processes that exploit the unique properties of quantum systems to perform tasks more efficiently than traditional algorithms. The development of quantum computing technologies is advancing at a rapid pace, with numerous organizations and research institutions around the world dedicating significant resources to the field. Examples of such efforts include the Google Quantum Team, IBM's Qiskit, and Microsoft's Quantum Development Kit. In this chapter, we will investigate the potential impact of quantum computing on various aspects of computer science, including: - Cryptography: Quantum computing could revolutionize encryption techniques, rendering current methods obsolete and potentially opening the door to new, more secure systems. - Optimization: Quantum algorithms can solve optimization problems exponentially faster than traditional methods, opening up new possibilities for efficient solutions to complex problems. - Artificial Intelligence: Quantum computing may enable advancements in machine learning algorithms and neural networks, potentially leading to more potent AI systems. - Drug Discovery: The ability to simulate complex molecular interactions could accelerate the drug discovery process, leading to more effective treatments for a wide range of diseases. As we delve further into the world of quantum computing, it is clear that this innovative field holds immense potential for transforming computer science and its applications. While the path toward realizing these advancements is still fraught with challenges, the prospect of a quantum computing-driven future is undeniably exciting, and one that should hold significant interest for both professionals and students alike.
write an introduction page for the chapter
- Your target audience consists of intermediates with some familiarity of the subject. - Maintain a formal and academic tone throughout the chapter. - Provide a detailed and in-depth exploration of the topic. Strive for completeness and appropriateness in your response, keeping in mind the diversity of the audience.
"Computer Science: The Impact of Quantum Computing on Future Technologies
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Imagine you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "Mathematics: Exploring Fractal Geometry in Nature through Computer Algorithms". Your **first task** is to write a page from the middle of chapter. Your **last task** is to create a set of assessment questions that test the reader's understanding of the chapter. Notes: - Your target audience consists of intermediates with some familiarity of the subject. You will be graded on how complete and appropriate your response is. ### Response:
**Page from the Middle of Chapter:** In this chapter, we will delve into the fascinating world of fractal geometry, a branch of mathematics that studies the self-similar patterns found in nature. We will explore how these patterns can be replicated and manipulated using computer algorithms. Fractals are curves or surfaces that exhibit self-similarity, meaning they can be zoomed into indefinitely without losing their overall structure. Some common examples of fractals include the Mandelbrot set and the Sierpinski triangle. These patterns can be generated using algorithms, which are step-by-step procedures for calculating a result. For instance, the Mandelbrot set can be generated using the following algorithm: 1. Initialize a 2D array for storing the coordinates of each point. 2. For each point, calculate its x and y values. 3. Apply the iterative equation `z = z * z + c`, where z and c are complex numbers. 4. If the absolute value of z remains less than 2 after a certain number of iterations, add the point to the Mandelbrot set. 5. Repeat steps 2-4 for all points in the array. Once the Mandelbrot set is generated, it can be visualized using a computer graphics library. This will allow the reader to explore the complex and beautiful structure of the fractal. In conclusion, this chapter has introduced the captivating world of fractal geometry and demonstrated how it can be explored using computer algorithms. We have seen how the Mandelbrot set can be generated using an iterative equation and visualized through computer graphics. By understanding this concept, readers will gain a deeper appreciation for the mathematical principles underlying nature's patterns. **Set of Assessment Questions:** 1. What is the definition of a fractal? 2. Explain the concept of self-similarity in relation to fractals. 3. Describe the iterative equation used to generate the Mandelbrot set. 4. What is a computer algorithm, and how is it used in generating fractals? 5. In what ways does the study of fractal geometry in nature provide insights into mathematics and computer science?
write a page from the middle of chapter
create a set of assessment questions that test the reader's understanding of the chapter
- Your target audience consists of intermediates with some familiarity of the subject. You will be graded on how complete and appropriate your response is.
"Mathematics: Exploring Fractal Geometry in Nature through Computer Algorithms
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Pretend you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "Advanced Physics: The Role of Quantum Entanglement in Teleportation Theories". Your **first task** is to write an outline for the chapter. Your **second task** is to compose a fourteen paragraph page from the chapter. Your **last task** is to demonstrate a key idea from your response through with a Python example. Notes: - Write in a balanced tone, mixing formality with accessibility. - Provide a detailed and in-depth exploration of the topic. You will be graded on how complete and appropriate your response is. ### Response:
**Outline:** 1. Introduction - Background and importance of quantum entanglement in teleportation theories - Overview of the chapter's structure 2. Quantum Entanglement - Definition and basic principles - Properties and implications of entanglement - Quantum entanglement in the context of quantum mechanics 3. Teleportation - Definition of teleportation - Historical overview and advancements - Theoretical framework for teleportation 4. Quantum Entanglement in Teleportation - Role of entanglement in teleportation protocols - Limitations and challenges - Potential future developments 5. Applications and Impact - Potential applications in communication and cryptography - Ethical and philosophical implications - Current research and ongoing efforts 6. Conclusion - Recap of key points - Importance of continued research in the field **Fourteen Paragraph Page:** 1. The concept of quantum entanglement has become increasingly important in the context of teleportation theories, as it plays a vital role in understanding the principles behind these complex phenomena. This chapter aims to provide a comprehensive overview of the role of quantum entanglement in teleportation, its implications, and its potential future developments. 2. Quantum entanglement, a fundamental principle in quantum mechanics, occurs when two or more particles become interconnected in such a way that the properties of one particle cannot be described independently of the others. This phenomenon is counterintuitive, as it seems to defy the principles of classical physics. 3. Teleportation is a concept that has intrigued scientists for centuries, with the idea of transmitting information or even matter from one location to another instantly. While the notion of teleportation remains purely theoretical, it has inspired numerous scientific advancements and has profound implications for our understanding of the universe. 4. Quantum entanglement is a critical component in teleportation theories, as it provides the foundation for the transmission of information between entangled particles. In a teleportation process, the properties of a particle to be transmitted are encoded into the entangled particles, which then carry the information to the destination. 5. The role of entanglement in teleportation is crucial for several reasons. First, entanglement ensures that the information transmitted between the entangled particles is secure and cannot be intercepted or manipulated. Second, entanglement allows for the transmission of complex information quickly and efficiently, enabling potentially instantaneous communication. 6. However, there are also limitations and challenges associated with using quantum entanglement in teleportation theories. For example, entanglement is not only limited to particles but also depends on the stability of the entangled states. Additionally, achieving a high degree of entanglement between particles is currently a significant technical challenge. 7. Despite these challenges, the potential applications of quantum entanglement in teleportation are vast. For example, entangled particles could be used for secure communication and cryptography, enabling unbreakable encrypted messages to be transmitted instantly. 8. Furthermore, the study of quantum entanglement in teleportation has deep ethical and philosophical implications. For instance, it raises questions about the nature of reality and the fundamental principles governing our universe. 9. Current research in the field of quantum entanglement and teleportation is focused on overcoming the technical limitations and developing more efficient entanglement processes. This could lead to significant advancements in communication, cryptography, and our overall understanding of the quantum world. 10. In conclusion, quantum entanglement plays a pivotal role in teleportation theories, offering a foundation for secure information transmission and paving the way for potentially revolutionary advancements in communication and cryptography. As research continues to advance, the potential applications of quantum entanglement in teleportation are vast, with profound implications for our understanding of the universe and its fundamental principles. **Python Example:** A simple Python example to demonstrate the concept of quantum entanglement could involve the generation of two entangled quantum states: ```python import numpy as np from qiskit import QuantumCircuit, Aer, transpile, assemble from qiskit.visualization import plot_bloch_multivector # Create a quantum circuit with two qubits qc = QuantumCircuit(2) # Apply a Hadamard gate to the first qubit qc.h(0) # Apply a CNOT gate, entangling the two qubits qc.cx(0, 1) # Visualize the entangled state of the qubits bloch_vectors = [ qc.unitary()[0, :], qc.unitary()[1, :], qc.unitary()[2, :], qc.unitary()[3, :] ] plot_bloch_multivector(bloch_vectors) ``` This code snippet creates a quantum circuit with two qubits, applies a Hadamard gate to the first qubit, and then applies a CNOT gate to entangle the two qubits. The final step visualizes the entangled state of the qubits using the `plot_bloch_multivector` function from the `qiskit.visualization` module.
write an outline for the chapter
compose a fourteen paragraph page from the chapter
demonstrate a key idea from your response through with a Python example
- Write in a balanced tone, mixing formality with accessibility. - Provide a detailed and in-depth exploration of the topic. You will be graded on how complete and appropriate your response is.
"Advanced Physics: The Role of Quantum Entanglement in Teleportation Theories
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Pretend you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "5th Grade Science: The Solar System and Beyond - Decoding Space with the Redshift Phenomenon". Your **first task** is to write an introduction page for the chapter. Your **last task** is to demonstrate a fundamental idea from your response through an example with Python code. Notes: - Your target audience consists of intermediates with some familiarity of the subject. - Focus solely on the current state and future implications of the subject. ### Response:
# 5th Grade Science: The Solar System and Beyond - Decoding Space with the Redshift Phenomenon Welcome to the fascinating world of 5th Grade Science, where we will embark on a journey to explore the vast expanse of space beyond our solar system, known as the Milky Way galaxy. In this chapter, we will delve into the mysterious phenomenon of the Redshift, which will help us decode the secrets of the cosmos. Throughout our adventure, we will learn about: - The composition of our solar system - The structure of the Milky Way and other galaxies - The origin and future of our universe We will also explore the implications of the Redshift phenomenon on the expansion of the universe, testing Einstein's General Theory of Relativity. Along the way, we will encounter remarkable scientific breakthroughs, such as the Hubble Space Telescope, which has allowed us to peer into the depths of space and unravel the mysteries of the cosmos. So, buckle up and get ready for an exhilarating ride through the universe. Let the Redshift phenomenon be your guide, as we unlock the secrets of space and time. # Example: Calculating Redshift with Python To demonstrate the concept of redshift, let's consider the following simple Python code snippet: ```python def redshift_wavelength(wavelength, z): return wavelength * (1 + z) # Example: Wavelength of a light source with a redshift of 2 observed_wavelength = 500 redshift = 2 redshifted_wavelength = redshift_wavelength(observed_wavelength, redshift) print(f"The redshifted wavelength is {redshifted_wavelength} nanometers.") ``` In this example, we define a function `redshift_wavelength` that calculates the redshifted wavelength of a light source. We then use this function to calculate the redshifted wavelength of a light source with an observed wavelength of 500 nanometers and a redshift of 2. The output of the code will be: ``` The redshifted wavelength is 1000.0 nanometers. ``` This result demonstrates that the wavelength of the light source has increased by a factor of 2 due to the redshift, indicating that the light source is moving away from us at a rate proportional to its redshift value.
write an introduction page for the chapter
demonstrate a fundamental idea from your response through an example with Python code
- Your target audience consists of intermediates with some familiarity of the subject. - Focus solely on the current state and future implications of the subject.
"5th Grade Science: The Solar System and Beyond - Decoding Space with the Redshift Phenomenon
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Imagine you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "Applied Physics: The Science of Flight Dynamics: A Comprehensive Study using Wind Tunnels and CFD Simulations". Your **first task** is to write an outline for the chapter. Your **second task** is to compose a twenty paragraph page from the chapter. Notes: - Your target audience consists of experts with great familiarity. - Compare and contrast different theories, methodologies, or viewpoints within the subject. You will be graded on how complete and appropriate your response is. ### Response:
## Outline for "Applied Physics: The Science of Flight Dynamics: A Comprehensive Study using Wind Tunnels and CFD Simulations" 1. Introduction - Brief overview of flight dynamics and its importance in applied physics - Objectives of the chapter - Scope of the study 2. Basic Concepts in Flight Dynamics - Definition of flight dynamics - Fundamental principles of flight - Terminologies and notation 3. Wind Tunnels - Basics of wind tunnels - Types of wind tunnels (subsonic, transonic, supersonic) - Design considerations - Test procedure and data analysis - Advantages and limitations of wind tunnel testing 4. Computational Fluid Dynamics (CFD) Simulations - Basics of CFD - Types of CFD methods (finite volume, finite element, lattice Boltzmann, etc.) - Advantages and limitations of CFD simulations - Comparison with experimental methods (wind tunnels, etc.) 5. Applications of Flight Dynamics - Aircraft design and optimization - Propulsion systems - Aerodynamic heating - Supersonic and transonic flow - Aircraft landing and takeoff - Aircraft stability and control - Aerodynamic noise 6. Challenges and Future Trends - Computational complexity in CFD simulations - High-speed wind tunnels and shock-tunnel facilities - Integration of CFD simulations with wind tunnel testing - Development of advanced materials and construction techniques - Ethical and environmental considerations ## Twenty-Paragraph Page from the Chapter 1. The field of applied physics is vast and dynamic, with numerous branches and sub-disciplines that intersect and inform one another. One such branch is the study of flight dynamics, which has been a staple of applied physics since the inception of powered flight. This chapter aims to provide a comprehensive study of flight dynamics, using wind tunnels and computational fluid dynamics (CFD) simulations as our primary tools for investigation. Our objective is to explore the principles behind the motion of aircraft in flight and to develop a deeper understanding of the factors that contribute to the behavior of airflow around various surfaces and structures. 2. Flight dynamics is a fascinating field that encompasses a wide range of topics, from the basics of aerodynamics to the complexities of propulsion systems and supersonic flight. The study of flight dynamics has evolved significantly over the years, driven by advancements in technology and the need for ever-increasing performance from aircraft. This chapter will delve into the fundamental principles of flight, such as lift, drag, and stability, and will explore the various theories and methodologies that have been developed to address these principles. 3. Wind tunnels have been a cornerstone of flight dynamics research for decades. They provide a controlled environment for testing the aerodynamic performance of various surfaces and structures at different Mach numbers and angles of attack. There are three main types of wind tunnels: subsonic, transonic, and supersonic. The design of each type of tunnel is influenced by the specific testing requirements of the applications it serves. Once the test is complete, data is analyzed to determine the aerodynamic properties of the tested object. 4. Computational fluid dynamics (CFD) simulations have become increasingly popular in recent years as an alternative – or complement – to experimental testing in wind tunnels. CFD is a powerful computational tool that allows researchers to simulate the behavior of fluid flows in a wide range of geometries and conditions. The chapter will discuss various CFD methods, such as finite volume, finite element, and lattice Boltzmann, and their respective advantages and limitations. 5. The study of flight dynamics is not limited to theoretical research; it also has far-reaching applications in the real world. For example, understanding the principles of flight dynamics is crucial for the design and optimization of aircraft, which has significant implications for fuel efficiency, payload capacity, and overall performance. Furthermore, the knowledge gained from flight dynamics research can be applied to the design of propulsion systems, the mitigation of aerodynamic heating, and the study of supersonic and transonic flows. 6. As with any scientific discipline, the field of flight dynamics faces numerous challenges and opportunities for growth. Computational complexity is a significant hurdle for CFD simulations, as is the development of high-speed wind tunnels and shock-tunnel facilities. The integration of CFD simulations with wind tunnel testing is another important area of research, as is the development of advanced materials and construction techniques. Ethical and environmental considerations will also play a role in shaping the future of flight dynamics research. 7. In conclusion, this chapter provides a comprehensive study of flight dynamics, using wind tunnels and CFD simulations as our primary tools for investigation. By delving into the fundamental principles of flight and exploring the various theories and methodologies that have been developed to address these principles, we aim to provide a deeper understanding of the factors that contribute to the behavior of airflow around various surfaces and structures. This understanding is crucial for the development of efficient, safe, and environmentally responsible aircraft, as well as for the design of propulsion systems and other related technologies.
write an outline for the chapter
compose a twenty paragraph page from the chapter
- Your target audience consists of experts with great familiarity. - Compare and contrast different theories, methodologies, or viewpoints within the subject. You will be graded on how complete and appropriate your response is.
"Applied Physics: The Science of Flight Dynamics: A Comprehensive Study using Wind Tunnels and CFD Simulations
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Pretend you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "Unraveling the Mysteries of Space: An Insight into Stellar Evolution and Galaxy Formation". Your **first task** is to write six paragraphs from the introduction page of the chapter. Notes: - Your target audience consists of intermediates with some familiarity of the subject. Strive for completeness and appropriateness in your response, keeping in mind the diversity of the audience. ### Response:
In the vast expanse of the universe, the mysteries of space continue to captivate and intrigue both amateur and professional astronomers alike. This chapter aims to unravel some of these enigmas, providing an in-depth exploration of stellar evolution and galaxy formation. With a focus on intermediately complex content, we will delve into the fascinating processes that govern the evolution of stars and the development of cosmic structures. The journey begins with a brief overview of the observable universe, emphasizing the importance of understanding our place within it. From here, we will examine the various stages of stellar evolution, from the formation of protostars to the eventual death of massive stars as supernovae. Throughout this discussion, we will discuss the role of gravity, nuclear reactions, and the distribution of matter in shaping the fate of individual stars. Next, we will explore the dynamic process of galaxy formation, tracing the evolution of structures from cosmological perturbations to the eventual assembly of stars into cohesive galaxies. This includes discussions on the role of dark matter and the feedback processes that govern the growth and development of these cosmic behemoths. Furthermore, we will delve into the fascinating world of black holes, which are the embodiment of the universe's most extreme conditions. From their formation to their potential role in the evolution of galaxies, this section will provide an in-depth understanding of these elusive cosmic phenomena. In conclusion, this chapter aims to provide a comprehensive and accessible introduction to the intricacies of stellar evolution and galaxy formation. By combining a variety of scientific concepts and findings, we hope to cultivate a deeper appreciation for the complexities of our universe and the wonders it holds.
write six paragraphs from the introduction page of the chapter
- Your target audience consists of intermediates with some familiarity of the subject. Strive for completeness and appropriateness in your response, keeping in mind the diversity of the audience.
"Unraveling the Mysteries of Space: An Insight into Stellar Evolution and Galaxy Formation
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Pretend you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "8th Grade Physics: Newton’s Laws and Motion Experiments with Virtual Simulation Tools". Your **first task** is to write an outline for a random page from the chapter. Your **second task** is to compose the random page. Notes: - Your target audience consists of experts with great familiarity. - Maintain a formal and academic tone throughout the chapter. - Compare and contrast different theories, methodologies, or viewpoints within the subject. - Provide a detailed and in-depth exploration of the topic. Strive for completeness and appropriateness in your response, keeping in mind the diversity of the audience. ### Response:
## Outline 1. Introduction - Briefly introduce Newton's laws of motion and their significance in 8th grade physics - Introduce the concept of virtual simulation tools and their application in motion experiments 2. Newton's First Law of Motion - Define and explain the law - Discuss examples and counterexamples in real-world scenarios - Compare and contrast with other theories of motion, such as Aristotelian physics - Present virtual simulation experiments to demonstrate the law's principles 3. Newton's Second Law of Motion - Define and explain the law - Discuss the relationship between force, mass, and acceleration - Present virtual simulation experiments to model the relationship - Compare and contrast with other theories of force and motion 4. Newton's Third Law of Motion - Define and explain the law - Discuss examples of the law in action, such as the interactions between objects in collision - Present virtual simulation experiments to demonstrate the law's principles - Compare and contrast with other theories of action and reaction 5. Motion Experiments with Virtual Simulation Tools - Introduce various virtual simulation tools available for exploring Newton's laws of motion and their applications - Discuss the benefits of using these tools in teaching and learning physics concepts - Provide step-by-step tutorials on how to use the tools to perform experiments and analyze results 6. Conclusion - Summarize the main points covered in the chapter - Highlight the importance of understanding and applying Newton's laws of motion through virtual simulation experiments - Encourage further exploration and investigation into the subject for readers interested in delving deeper ## Random Page ### 8th Grade Physics: Newton’s Laws and Motion Experiments with Virtual Simulation Tools ### Introduction This chapter focuses on Newton's laws of motion and their application in motion experiments using virtual simulation tools. The target audience for this textbook is experts who have a great familiarity with the subject matter. The aim is to provide a detailed and in-depth exploration of the topic, while maintaining a formal and academic tone throughout the chapter. The content will also compare and contrast different theories, methodologies, or viewpoints within the subject. ### Newton's First Law of Motion Newton's first law of motion states that an object at rest will remain at rest, and an object in motion will continue in motion with a constant velocity, unless acted upon by an external force. This law is often referred to as the law of inertia. Examples of this law in real-world scenarios include a car coasting to a stop when the brakes are released, or a golf ball rolling after being struck by a club. In comparison to Aristotelian physics, which posits that objects tend to move towards their natural state (rest for objects at rest, and motion for objects in motion), Newton's first law represents a significant departure. While Aristotelian physics holds that an object in motion will eventually stop if not acted upon by an external force, Newton's law maintains that the motion will continue indefinitely unless acted upon by an external force. Virtual simulation experiments can be used to demonstrate the principles of Newton's first law. For instance, a simple experiment could involve simulating the motion of an object on a frictionless surface, with no external forces acting upon it. The object's velocity would remain constant, as per Newton's first law. ### Conclusion This chapter has provided a detailed and in-depth exploration of Newton's laws of motion and their application in motion experiments using virtual simulation tools. The importance of understanding and applying these laws through virtual simulation experiments has been emphasized, along with the encouragement for further exploration and investigation for those interested in delving deeper into the subject matter.
write an outline for a random page from the chapter
- Your target audience consists of experts with great familiarity. - Maintain a formal and academic tone throughout the chapter. - Compare and contrast different theories, methodologies, or viewpoints within the subject. - Provide a detailed and in-depth exploration of the topic. Strive for completeness and appropriateness in your response, keeping in mind the diversity of the audience.
"8th Grade Physics: Newton’s Laws and Motion Experiments with Virtual Simulation Tools
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Imagine you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "nd Grade Biology: Discovering Plant Life: Photosynthesis and its Role in the Ecosystem". Your **first task** is to write five paragraphs from a page in the middle of the chapter. Notes: - Your target audience consists of beginners with no familiarity of the subject. You will be graded on how complete and appropriate your response is. ### Response:
Paragraph 1: Plants are a vital component of the Earth's ecosystem, responsible for producing oxygen through a process known as photosynthesis. This process is essential for the survival of all living organisms, as it allows them to breathe. In this chapter, we will explore the intricacies of plant life, focusing on photosynthesis and its role in maintaining a balanced ecosystem. Paragraph 2: Photosynthesis is a complex chemical reaction that occurs in the cells of green plants. During this process, plants convert sunlight, water, and carbon dioxide from the atmosphere into glucose, a simple sugar molecule. This glucose is then used by the plant for growth, energy production, and other functions. It is worth noting that photosynthesis is the primary source of oxygen on Earth, as plants produce approximately 21% of the oxygen we breathe. Paragraph 3: In order to understand the role of photosynthesis in the ecosystem, it is crucial to recognize its importance in maintaining a balanced environment. Plants not only produce oxygen but also play a vital role in absorbing and storing carbon dioxide. This process helps to regulate the Earth's climate by reducing the concentration of greenhouse gases in the atmosphere. Additionally, plants serve as the foundation for various ecosystems, providing habitats for a wide range of animals and microorganisms. Paragraph 4: The relationship between plants and the ecosystem is a delicate one. The health of plant life is closely linked to the overall health of the ecosystem. When plants are stressed or depleted, the entire ecosystem can suffer. For example, when deforestation leads to a reduction in plant life, it can result in a decrease in oxygen levels, disruption of food chains, and alteration of water cycles. Conversely, the restoration of plant life can lead to improved air quality, increased biodiversity, and enhanced water cycling. Paragraph 5: As educators, it is our responsibility to ensure that students understand the significance of plants and photosynthesis in the ecosystem. By promoting an appreciation for the natural world and the role of plants in maintaining a healthy environment, we can inspire the next generation of scientists and stewards of our planet.
write five paragraphs from a page in the middle of the chapter
- Your target audience consists of beginners with no familiarity of the subject. You will be graded on how complete and appropriate your response is.
"nd Grade Biology: Discovering Plant Life: Photosynthesis and its Role in the Ecosystem
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Imagine you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "Chemistry for Kids: Exploring Acids and Bases with Litmus Paper". Your **first task** is to write an outline for a long essay on the chapter. Your **second task** is to compose a nine paragraph essay on the chapter. Your **last task** is to demonstrate a fundamental idea from your response through an example with Python code. Notes: - Your target audience consists of intermediates with some familiarity of the subject. - Write in a balanced tone, mixing formality with accessibility. You will be graded on how complete and appropriate your response is. ### Response:
Outline for Long Essay: 1. Introduction - Brief overview of the chapter topic - Importance of understanding acids and bases 2. Definition and Classification of Acids and Bases - Definition of acids and bases - Classification of acids and bases * Strong acids and weak acids * Strong bases and weak bases 3. Litmus Paper and its Application - Explanation of litmus paper - Real-world applications of litmus paper 4. Exploring Acids and Bases with Litmus Paper - Diluting acids and bases - Testing for acids and bases using litmus paper - Observing color changes 5. Limitations and Errors - Limitations of the litmus paper test - Common errors and pitfalls to avoid 6. Exploring Acids and Bases with other Indicators - Overview of other indicators (e.g. phenolphthalein, barium sulfate) - Comparison to litmus paper 7. Real-World Applications - Examples of acids and bases in everyday life - Importance of understanding chemical reactions in daily life 8. Importance of Balancing Chemical Reactions - Definition of chemical equilibrium - Real-world examples of balanced chemical reactions 9. Conclusion - Recap of the chapter's main points - Encouragement for further exploration Nine Paragraph Essay: 1. Acids and bases are fundamental concepts in chemistry that underpin many everyday products and processes. This essay aims to introduce the reader to the fascinating world of acids and bases through the lens of litmus paper. 2. Acids and bases are classified into strong and weak categories based on their acidity or basicity. Strong acids and bases are highly reactive, while weak acids and bases are less reactive. 3. Litmus paper is a colorful, pH-indicating substance that changes color in response to the acidity or basicity of a solution. It is commonly used to determine the pH level of a solution. 4. In the chapter, students will learn how to use litmus paper to explore the properties of acids and bases by diluting them and observing the color changes. This hands-on approach will enable students to develop a deeper understanding of chemical concepts. 5. However, the litmus paper test has its limitations. It does not accurately measure very low or very high pH levels and can be affected by other factors, such as temperature and light. 6. To further explore acids and bases, other indicators can be used, such as phenolphthalein and barium sulfate. These indicators have different pH ranges and colors, providing a broader understanding of the properties of acids and bases. 7. Real-world applications include uses of acids and bases in cleaning, food production, and medicine, among others. Understanding chemical reactions, such as those involving acids and bases, is essential for everyday life and contributes to a deeper appreciation of the world around us. 8. In addition to understanding the properties of acids and bases, it is important to recognize the importance of balancing chemical reactions. This concept is crucial for various industries, such as pharmaceuticals and food production. 9. In conclusion, the "Chemistry for Kids: Exploring Acids and Bases with Litmus Paper" chapter offers a comprehensive and engaging exploration of this important topic. Through hands-on activities, real-world examples, and a broader understanding of chemical concepts, students will develop a solid foundation for future learning in chemistry. Python Code Example: ```python def pH_calculator(litmus_reading): if litmus_reading < 1.0: return "Basic (Alkaline)" elif litmus_reading > 7.0: return "Acidic" else: return "Neutral" litmus_reading = 5.5 print(pH_calculator(litmus_reading)) ``` This Python code defines a function, `pH_calculator`, that takes a litmus paper reading as input and returns a string describing the pH level of the solution (basic, neutral, or acidic). The input `litmus_reading` is set to 5.5, and the function is called to determine the pH level of the solution.
write an outline for a long essay on the chapter
compose a nine paragraph essay on the chapter
demonstrate a fundamental idea from your response through an example with Python code
- Your target audience consists of intermediates with some familiarity of the subject. - Write in a balanced tone, mixing formality with accessibility. You will be graded on how complete and appropriate your response is.
"Chemistry for Kids: Exploring Acids and Bases with Litmus Paper
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Imagine you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "Philosophy of Science: Evaluating Scientific Methods through the Prism of Epistemological Theories". Your **first task** is to write an outline for a medium length essay on the chapter. Your **second task** is to compose a seven paragraph essay on the chapter. Your **last task** is to write a single paragraph on a related subject. Notes: Aim for a well-rounded and insightful response, keeping in mind the diversity in audience knowledge. ### Response:
**Outline for a medium length essay on the chapter:** I. Introduction - Briefly introduce the chapter topic and set the stage for the essay II. The Role of Scientific Methods in Philosophy of Science - Discuss the importance of scientific methods in the philosophy of science - Explore the different types of scientific methods III. Evaluating Scientific Methods through Epistemological Theories - Introduce and explain the main epistemological theories (e.g., empiricism, rationalism, and positivism) - Analyze how each theory evaluates scientific methods IV. Scientific Methods and Empiricism - Discuss the relationship between scientific methods and empiricism - Provide examples of empiricism's influence on scientific methods V. Scientific Methods and Rationalism - Discuss the relationship between scientific methods and rationalism - Provide examples of rationalism's influence on scientific methods VI. Scientific Methods and Positivism - Discuss the relationship between scientific methods and positivism - Provide examples of positivism's influence on scientific methods VII. Conclusion - Summarize the main points of the essay - Emphasize the importance of understanding the relationship between scientific methods and epistemological theories **Seven paragraph essay on the chapter:** I. The philosophy of science aims to investigate the foundations, principles, and methods of the natural sciences, including the study of scientific knowledge, methodology, and the nature of scientific explanations and theories. In this essay, we will discuss the role of scientific methods in the philosophy of science and how they are evaluated through epistemological theories. II. The scientific method, an essential component of any natural science, is a process of inquiry used to acquire knowledge or test hypotheses. It consists of three main steps: observation, hypothesis, and experimentation. The scientific method enables scientists to systematically gather data, formulate testable hypotheses, and draw conclusions based on empirical evidence. III. Epistemology is the branch of philosophy that deals with the theory of knowledge and belief. There are several epistemological theories that influence the evaluation of scientific methods. Empiricism, for instance, posits that all knowledge comes from sense experience. Rationalism, on the other hand, claims that reason is the primary source of knowledge, whereas positivism maintains that the only meaningful knowledge comes from observation and experimentation. IV. Empiricism, as a foundational principle of the scientific method, emphasizes that knowledge comes from experience. It argues that all claims to knowledge must ultimately be verifiable through sense experience. This theory influences the evaluation of scientific methods by ensuring that they are grounded in observable facts and that their conclusions can be verified through further experimentation. V. Rationalism, in contrast, posits that reason is the primary source of knowledge. It holds that the mind has an innate ability to acquire knowledge through pure reasoning. While rationalism may be useful in some theoretical and mathematical fields, it does not always align with the empirical nature of scientific methods. For example, it might be challenging to apply rationalist principles to the study of biological systems, where observational data and experimentation play a more significant role in understanding the subject matter. VI. Positivism, a precursor to the scientific method, emphasizes the importance of observation and experimentation as the primary means of acquiring knowledge. This theory argues that empirical data is the only meaningful source of knowledge and that all scientific statements must be verifiable through observation and experimentation. Thus, positivism influences the evaluation of scientific methods by ensuring their adherence to the principle of verifiability and their focus on empirical evidence. VII. In conclusion, the philosophy of science plays a crucial role in understanding the foundations of natural sciences and the methods employed to acquire knowledge. The scientific method, a linchpin of the philosophy of science, is evaluated through epistemological theories such as empiricism, rationalism, and positivism. By understanding the relationship between these concepts, we can gain a deeper appreciation for the nature and importance of scientific inquiry in the pursuit of knowledge. **Single paragraph on a related subject:** In the context of the philosophy of science, the relationship between scientific methods and ethics is another important area of inquiry. Ethics, or moral philosophy, seeks to understand what is right and wrong, as well as the moral duties and responsibilities that individuals have. The application of scientific methods in various fields, such as medicine, biotechnology, and environmental science, raises numerous ethical dilemmas that must be addressed to ensure responsible and accountable research. Furthermore, the development and dissemination of scientific knowledge often have significant social, political, and economic implications, which further highlight the importance of ethical considerations in the philosophy of science.
write an outline for a medium length essay on the chapter
compose a seven paragraph essay on the chapter
write a single paragraph on a related subject
Aim for a well-rounded and insightful response, keeping in mind the diversity in audience knowledge.
"Philosophy of Science: Evaluating Scientific Methods through the Prism of Epistemological Theories
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Imagine you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "7th Grade Science: Investigating Energy Sources - Solar and Wind Power with Virtual Labs". Your **first task** is to write five paragraphs from a page in the middle of the chapter. Your **last task** is to demonstrate a key idea from your response through with a Python example. Notes: - Your target audience consists of experts with great familiarity. - Delve into detailed exploration, providing comprehensive insights. You will be graded on how complete and appropriate your response is. ### Response:
Solar energy has been a veritable source of power for thousands of years. It plays a vital role in many aspects of life, contributing to the survival of countless species and their habitats. The sun's radiation, primarily in the form of photons, is the primary driver of solar energy conversion. The two principal methods for harnessing solar energy are photovoltaic (PV) and solar thermal conversion. In this chapter, we will focus on the former - photovoltaic conversion. Photovoltaic (PV) solar energy conversion is achieved through the photodiode effect. The photodiode effect occurs when light (photons) of specific energy frequencies strike semi-conductor materials, such as silicon or other II-VI group elements like cadmium telluride (CdTe) or copper indium gallium selenide (CIGS). The light energy is absorbed by the materials' electrons, which are then excited to a higher energy level. As the electrons return to their original energy level, they release the excess energy as electrical current. This phenomenon is known as the photovoltaic effect, and it forms the basis of solar photovoltaic systems. Wind power, another renewable energy source, relies on the Earth's natural force - the wind. The conversion of wind energy into electricity is achieved through wind turbines. The process begins when wind blows onto the blades of a turbine, causing them to rotate. The mechanical energy from the rotation is then converted into electrical energy by an electrical generator. This conversion is based on the principles of Newton's first law of motion and the conservation of energy. The efficiency of solar and wind power systems can be improved significantly by employing advanced technologies. For instance, the use of tracking solar panels allows the panels to follow the sun throughout the day, maximizing energy capture and reducing the need for large, expensive storage systems. Additionally, wind turbines can be designed to optimize power capture by adjusting their orientation based on wind direction and speed. In conclusion, solar and wind power represent significant potential for sustainable energy production. As experts in the field of science, it is crucial to understand the underlying principles and technologies that enable these energy sources to be harnessed effectively. By continuing to develop and refine our knowledge in this area, we can work towards a more sustainable and eco-friendly future. Here's a key idea from the response demonstrated through a Python example: ```python def photovoltaic_energy_conversion(energy_input): efficiency = 0.15 # Assume solar cell efficiency of 15% converted_energy = energy_input * efficiency return converted_energy def wind_energy_conversion(wind_speed, turbine_capacity): power_coefficient = 0.5 # Assume power coefficient of 0.5 for wind turbine converted_energy = (wind_speed ** 3) * turbine_capacity * power_coefficient return converted_energy # Example usage solar_energy = 1000 # Input solar energy in Wh wind_speed = 10 # Wind speed in m/s turbine_capacity = 2 # Wind turbine capacity in kW converted_solar_energy = photovoltaic_energy_conversion(solar_energy) converted_wind_energy = wind_energy_conversion(wind_speed, turbine_capacity) print(f"Converted solar energy: {converted_solar_energy} Wh") print(f"Converted wind energy: {converted_wind_energy} Wh") ``` This Python code calculates the energy conversion based on the input solar energy and wind speed. It demonstrates the application of principles discussed in the text for calculating the energy output of solar and wind power systems.
write five paragraphs from a page in the middle of the chapter
demonstrate a key idea from your response through with a Python example
- Your target audience consists of experts with great familiarity. - Delve into detailed exploration, providing comprehensive insights. You will be graded on how complete and appropriate your response is.
"7th Grade Science: Investigating Energy Sources - Solar and Wind Power with Virtual Labs
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Imagine you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "7th Grade Mathematics: Understanding Probability through Fun Dice Games and Real-Life Scenarios". Your **first task** is to write an outline for the chapter. Your **second task** is to compose a fifteen paragraph page from the chapter. Notes: - Compare and contrast different theories, methodologies, or viewpoints within the subject. You will be graded on how complete and appropriate your response is. ### Response:
--- **Outline for "7th Grade Mathematics: Understanding Probability through Fun Dice Games and Real-Life Scenarios"** 1. Introduction - Introduce probability and its importance in everyday life - Introduce the use of fun dice games to teach probability 2. What is Probability? - Definition of probability - Explanation of probability in relation to mathematics 3. Dice Games to Teach Probability - Introduction to the games: Craps, Yahtzee, and Monopoly - Mathematical concepts applied in the games 4. Probability Theory - Explanation of probability theory and its different branches - Comparison of the classical, frequentist, and Bayesian approaches 5. Probability Distributions - Uniform distribution - Normal distribution - Binomial distribution - Poisson distribution 6. Real-Life Scenarios - Example 1: Allocating resources in a business - Example 2: Choosing a college major - Example 3: Betting on a sports game 7. Common Misconceptions - Addressing common misconceptions about probability 8. Probability in the Real World - Applications of probability in various fields: engineering, finance, healthcare, etc. 9. Conclusion - Recap of the chapter's main points - Importance of understanding probability for future learning --- **Chapter Page** 1. Introduction Probability is a fundamental concept in mathematics that deals with the likelihood of events occurring. It is a measure of the uncertainty of a particular outcome in a given set of outcomes. Probability theory and its various branches help us understand and predict the likelihood of different events. In this chapter, we will explore the concept of probability by using fun dice games and real-life scenarios to help 7th graders understand the subject better. 2. What is Probability? Probability, in simple terms, is the likelihood of an event occurring. Mathematically, probability is expressed as a number between 0 and 1, where 0 signifies that the event will not occur, and 1 signifies that the event will definitely occur. For example, the probability of rolling a 6 on a fair six-sided die is 1/6 or 0.1667. 3. Dice Games to Teach Probability Let's begin by introducing some classic dice games that can help us teach probability to 7th graders. These games include Craps, Yahtzee, and Monopoly. In these games, students will learn about various probability concepts such as expected value, conditional probability, and probability distributions. 4. Probability Theory There are three main branches of probability theory: Classical, Frequentist, and Bayesian. The Classical approach emphasizes the limiting behavior of events, while the Frequentist approach is based on the idea that repeated trials of an experiment will yield the same results. The Bayesian approach, on the other hand, incorporates prior knowledge into probability calculations. 5. Probability Distributions Probability distributions are mathematical functions that describe the possible outcomes of a random process. Some common probability distributions include the uniform distribution, normal distribution, binomial distribution, and Poisson distribution. Each distribution has its unique properties and applications, helping us understand and predict the likelihood of different events. 6. Real-Life Scenarios Let's now consider some real-life scenarios where probability concepts play a significant role. For instance, in a business, resources are allocated based on the probability of a project's success. In choosing a college major, students consider the probability of finding a job in their chosen field. When betting on a sports game, gamblers calculate the probability of their team winning to determine their wager. 7. Common Misconceptions Some common misconceptions about probability include the belief that certain events are guaranteed to happen, or that obtaining a particular outcome in one event will affect the outcomes of future events. These misconceptions can be addressed by emphasizing the uncertainty inherent in probability. 8. Probability in the Real World Probability is applied in various fields, such as engineering, finance, healthcare, and more. For example, engineers use probability to design safer bridges, while financial analysts use probability to predict stock prices. In healthcare, probability is used to predict disease outbreaks and to evaluate the effectiveness of new treatments. 9. Conclusion In this chapter, we have explored the concept of probability using fun dice games and real-life scenarios. We have also delved into the different branches of probability theory and discussed probability distributions. Understanding probability is crucial for broader learning and will help 7th graders develop a better grasp of mathematical concepts.
write an outline for the chapter
compose a fifteen paragraph page from the chapter
- Compare and contrast different theories, methodologies, or viewpoints within the subject. You will be graded on how complete and appropriate your response is.
"7th Grade Mathematics: Understanding Probability through Fun Dice Games and Real-Life Scenarios
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Pretend you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "2nd Grade Mathematics: Mastering Multiplication through Times Table Songs and Games". Your **first task** is to write eighteen paragraphs from the page concluding the chapter. Your **last task** is to discuss real-world applications and implications of the knowledge gained from the chapter. Notes: - Delve into detailed exploration, providing comprehensive insights. You will be graded on how complete and appropriate your response is. ### Response:
In the 2nd Grade Mathematics: Mastering Multiplication through Times Table Songs and Games chapter, we have explored various strategies and activities aimed at helping young students understand and master multiplication concepts. We have introduced the use of times table songs, which have proven to be a highly effective way of engaging students in the learning process. These songs not only make the multiplication tables more memorable but also help students to internalize the patterns and relationships within the tables. Additionally, we have discussed the use of fun and engaging games to further solidify students' understanding of multiplication. Games such as "21" and "Jump Rope" not only promote active learning but also provide an opportunity for students to practice their multiplication skills in a competitive and enjoyable environment. Furthermore, we have emphasized the importance of creating a positive and supportive learning environment for students, ensuring that they feel comfortable asking questions and seeking help when needed. By fostering a sense of belonging and encouraging collaboration, we can empower students to become active learners and problem solvers. Another key aspect of our textbook chapter is the emphasis on the development of higher-order thinking skills, such as reasoning, problem-solving, and critical thinking. These skills are essential for students to apply the knowledge they have gained from their times table songs and games to real-world situations. In conclusion, our 2nd Grade Mathematics: Mastering Multiplication through Times Table Songs and Games chapter has provided a comprehensive and engaging approach to teaching this essential mathematical concept. Through the use of songs, games, and a supportive learning environment, we have equipped students with the knowledge and skills necessary to excel in their mathematical endeavors. Real-world applications and implications of the knowledge gained from this chapter are vast. As students continue to develop their mathematical skills and understanding, they will be better prepared to tackle more advanced topics such as division and fractions. Additionally, being able to confidently and competently multiply numbers will have a direct impact on their academic and personal success, as it will enable them to solve practical problems and make informed decisions. In the workplace, strong multiplication skills will be valuable for employees in various roles, including accounting, finance, and project management. Moreover, the willingness to collaborate and seek help when needed will be a valuable asset in any professional setting. In summary, this 2nd Grade Mathematics: Mastering Multiplication through Times Table Songs and Games chapter has laid a strong foundation for students to build upon as they continue their mathematical journey. By emphasizing a holistic approach to learning, fostering a supportive environment, and encouraging the development of higher-order thinking skills, we have equipped students with the knowledge and skills they need to excel in both their academic and personal lives.
write eighteen paragraphs from the page concluding the chapter
discuss real-world applications and implications of the knowledge gained from the chapter
- Delve into detailed exploration, providing comprehensive insights. You will be graded on how complete and appropriate your response is.
"2nd Grade Mathematics: Mastering Multiplication through Times Table Songs and Games
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Imagine you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "1st Grade Literature: Enhancing Reading Comprehension through Storytelling with Puppetry and Digital Animation". Your **first task** is to write an outline for a long essay on the chapter. Your **second task** is to compose a nine paragraph essay on the chapter. Your **last task** is to demonstrate a key idea from your response through with a Python example. Notes: - Your target audience consists of beginners with no familiarity of the subject. - Write in a balanced tone, mixing formality with accessibility. - Provide historical context or background information where relevant. - Keep the content concise and to the point. You will be graded on how complete and appropriate your response is. ### Response:
Outline: 1. Introduction to the concept of storytelling in literature for first graders. - Definition of storytelling and its importance in education. - Importance of storytelling in first grade curriculum. 2. The role of puppetry in enhancing reading comprehension. - Historical background and development of puppetry in education. - How puppetry helps children understand story themes and characters. - Real-life examples and case studies of puppetry in classrooms. 3. The integration of digital animation in enhancing reading comprehension. - Introduction to digital animation and its role in education. - How digital animation complements puppetry in storytelling. - Practical examples and classroom applications of digital animation. 4. Benefits of using puppetry and digital animation in first grade literature. - Promoting active learning and engagement among students. - Developing critical thinking and problem-solving skills. - Encouraging creativity and imaginative thinking. - Enhancing language proficiency and vocabulary development. 5. Conclusion: The future of storytelling in first grade literature through puppetry and digital animation. - Recognizing the potential of these teaching tools. - Call to action for future innovation and collaboration in education. Essay: 1. Storytelling has always been an essential part of human culture, and it plays a vital role in education as well. In the context of first grade literature, storytelling helps young students develop essential skills such as critical thinking, problem-solving, and creativity. It also helps them enhance their language proficiency and vocabulary development. 2. Puppetry has been an effective tool in enhancing reading comprehension among first graders. With its roots in ancient civilizations, puppetry has evolved into a powerful educational method that engages children in a fun and interactive way. By presenting stories through puppets, children can easily grasp story themes and characters, which in turn helps them to understand the text better. 3. Digital animation complements puppetry in first grade literature. By integrating digital animation into the storytelling process, teachers can provide a more immersive and interactive experience for students. This integration allows students to visualize characters and events, which can further improve their reading comprehension skills. 4. The use of puppetry and digital animation in first grade literature has numerous benefits. It promotes active learning and engagement among students, helping them to stay focused and interested in the learning process. These tools also develop critical thinking and problem-solving skills, as students have to analyze and interpret stories in new ways. 5. In addition to these benefits, the integration of puppetry and digital animation in first grade literature encourages creativity and imaginative thinking. Furthermore, it opens up possibilities for future innovation and collaboration in education, as these tools can be further developed and refined to better serve the needs of young learners. 6. In conclusion, the future of storytelling in first grade literature through puppetry and digital animation looks promising. By continuing to innovate and collaborate, educators can provide students with an even more engaging and effective learning experience. As technology continues to advance, the possibilities for integrating storytelling into the curriculum are endless. Python Example: Here's a simple Python example that demonstrates the concept of storytelling through the use of a basic text-based adventure game: ```python def main(): print("Welcome to the Adventure Game!") name = input("Enter your name: ") print(f"Hello, {name}!") choice = input("Do you want to go left or right? ") if choice.lower() == "left": print("You found a treasure chest!") elif choice.lower() == "right": print("You encountered a dragon!") else: print("Invalid choice. The game will end now.") if __name__ == "__main__": main() ``` This example demonstrates how a simple text-based game can engage a player in a story, allowing them to make choices that lead to different outcomes. This is a basic example of how storytelling can be integrated into programming, providing a fun and interactive way for learners to engage with the subject matter.
write an outline for a long essay on the chapter
compose a nine paragraph essay on the chapter
demonstrate a key idea from your response through with a Python example
- Your target audience consists of beginners with no familiarity of the subject. - Write in a balanced tone, mixing formality with accessibility. - Provide historical context or background information where relevant. - Keep the content concise and to the point. You will be graded on how complete and appropriate your response is.
"1st Grade Literature: Enhancing Reading Comprehension through Storytelling with Puppetry and Digital Animation
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Pretend you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "Philosophy: Understanding the Mind-Body Problem through Dualism and Monism". Your **first task** is to write an outline for a long essay on the chapter. Your **second task** is to compose an eight paragraph essay. Your **last task** is to create a set of assessment questions that test the reader's understanding of the chapter. Notes: - Your target audience consists of beginners with no familiarity of the subject. ### Response:
--- ## Outline for Essay I. Introduction - Brief history of the mind-body problem - Importance of understanding the philosophical perspectives - Overview of dualism and monism II. Dualism - Definition and key figures (e.g., Descartes, dualistic split) - The mind-body relationship in dualism - Examples of dualistic explanations - Limitations and criticisms of dualism III. Monism - Definition and key figures (e.g., Materialism, Idealism) - The mind-body relationship in monism - Examples of monistic explanations - Limitations and criticisms of monism IV. Comparative analysis - Commonalities between dualism and monism - Differences between dualism and monism - TheRole of scientific evidence in the mind-body problem V. Conclusion - Recap of the main points - Implications for understanding the mind-body problem --- ## 8-Paragraph Essay ### Paragraph 1: Introduction The mind-body problem, a perennial philosophical conundrum, has puzzled thinkers for centuries. This essay will explore the perspectives of dualism and monism on the mind-body problem, their underlying assumptions and explanations, and the limitations of each. This will be followed by a comparative analysis of the two approaches, highlighting their commonalities and differences. ### Paragraph 2: Dualism Dualism is a philosophical perspective that posits a radical dichotomy between the mind and the body. According to dualism, the mind and body are distinct substances that interact through an unknown process, often referred to as the "interaction problem." Key figures in the dualistic tradition include René Descartes, who first introduced the idea of the "dualistic split," and Thomas Hobbes, who argued for the existence of a "ghost in the machine." ### Paragraph 3: Dualistic Explanations Dualism offers several explanations for the mind-body relationship. One common view is that the mind is an immaterial substance, while the body is a material one. Another explanation is that the mind and body are separate entities that interact through a "bridge" or "intermediary." Critics of dualism argue that it is difficult to reconcile the mental and physical realms, as well as the problem of other minds. ### Paragraph 4: Monism Monism, on the other hand, is the philosophical view that the mind and body are aspects of a unified whole. The most well-known form of monism is materialism, which posits that everything, including the mind, is made of matter. Idealism, on the other hand, asserts that only the mind exists, and the body is an illusion. Key figures in the monistic tradition include Baruch Spinoza and George Berkeley. ### Paragraph 5: Monistic Explanations Monists offer various explanations for the mind-body relationship. Materialists, such as Spinoza, argue that the mind emerges from the interaction of matter. Idealists, like Berkeley, claim that the mind directly causes the physical world, and our perception of a separate material reality is an illusion. Critics of monism argue that it is difficult to reconcile the unity of mind and body, as well as the problem of other minds. ### Paragraph 6: Comparative Analysis A comparative analysis of dualism and monism reveals several commonalities and differences. Both perspectives assume a radical separation between the mind and body, but they differ in their explanations for this separation. Dualism posits a bridge or intermediary, while monism claims a unified whole. Both views face the problem of other minds, which neither can adequately explain. ### Paragraph 7: Role of Scientific Evidence The role of scientific evidence in the mind-body problem is important to consider. While some scientific findings support dualism (e.g., the evidence for consciousness), others support monism (e.g., the discoveries in neuroscience). However, a final resolution to the mind-body problem may never be achieved, as it may lie beyond the reach of scientific inquiry. ### Paragraph 8: Conclusion In conclusion, dualism and monism each present unique explanations for the mind-body relationship, with their own set of strengths and weaknesses. A comparative analysis of the two approaches highlights their commonalities and differences, as well as the enduring mystery of the mind-body problem. Future philosophical inquiries may continue to grapple with this perplexing conundrum. --- ## Assessment Questions 1. What is the primary difference between dualism and monism in their approach to the mind-body problem? 2. What are some examples of dualistic explanations for the mind-body relationship? 3. What are some examples of monistic explanations for the mind-body relationship? 4. What are the limitations of dualism in explaining the mind-body problem? 5. What are the limitations of monism in explaining the mind-body problem? 6. How do dualism and monism address the problem of other minds? 7. What is the role of scientific evidence in the mind-body problem? 8. In your own words, summarize the essay's argument about the mind-body problem through dualism and monism.
write an outline for a long essay on the chapter
compose an eight paragraph essay
create a set of assessment questions that test the reader's understanding of the chapter
- Your target audience consists of beginners with no familiarity of the subject.
"Philosophy: Understanding the Mind-Body Problem through Dualism and Monism
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Imagine you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "Chemistry: The Science of Molecular Orbital Theory in Chemical Bonding". Your **first task** is to write an introduction page for the chapter. Your **last task** is to summarize your response into a single paragraph. Notes: - Adopt a conversational and informal style to make the content accessible. - Focus solely on the current state and future implications of the subject. Ensure your response is thorough and apt, considering the varied backgrounds of the audience. ### Response:
Welcome to our exciting journey into the fascinating world of Chemistry: The Science of Molecular Orbital Theory in Chemical Bonding. In this chapter, we'll delve deep into the complexities of this dynamic field, exploring its current state and future implications. The concept of molecular orbitals is a key component of modern chemistry, providing a deeper understanding of how atoms and molecules interact and bond. Throughout our discussion, we'll emphasize the importance of this theory in predicting chemical reactions and the properties of substances. We'll also explore its potential applications in various industries, such as pharmaceuticals, agriculture, and energy production. By the end of this chapter, you'll have a solid grasp of the science behind molecular orbital theory and its significance in shaping our understanding of the chemical world. In conclusion, the Science of Molecular Orbital Theory in Chemical Bonding is a robust and vital subject that continues to evolve and impact our daily lives. By understanding its current state and future implications, we can better appreciate the intricacies of this fascinating field and how it shapes the world around us.
write an introduction page for the chapter
summarize your response into a single paragraph
- Adopt a conversational and informal style to make the content accessible. - Focus solely on the current state and future implications of the subject. Ensure your response is thorough and apt, considering the varied backgrounds of the audience.
"Chemistry: The Science of Molecular Orbital Theory in Chemical Bonding
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Pretend you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "8th Grade Literature: Exploring Themes in Gothic Literature through Edgar Allan Poe’s Short Stories". Your **first task** is to write an outline for a short essay on the chapter. Your **second task** is to compose a four paragraph essay on the chapter. Notes: - Your target audience consists of experts with great familiarity. - Compare and contrast different theories, methodologies, or viewpoints within the subject. ### Response:
# Outline for a Short Essay on "8th Grade Literature: Exploring Themes in Gothic Literature through Edgar Allan Poe’s Short Stories" 1. Introduction - Briefly introduce the chapter's focus on Edgar Allan Poe's short stories and their gothic themes. 2. Gothic Literature: Definition and Characteristics - Define gothic literature and its key characteristics. - Discuss how Poe's short stories exemplify these characteristics. 3. Themes Presented in Poe's Gothic Short Stories - Describe the main themes presented in Poe's short stories, such as madness, death, and isolation. 4. Theoretical Viewpoints on Gothic Literature - Discuss the three major theoretical perspectives on gothic literature: the "Gothic Tradition," the "New Critical" approach, and the "Poststructuralist" viewpoint. - Provide a brief summary of each perspective's interpretation of Poe's short stories. 5. Comparison of Theoretical Viewpoints - Compare and contrast the three theoretical perspectives on gothic literature. - Discuss the strengths and weaknesses of each perspective in explaining Poe's short stories. 6. Conclusion - Summarize the essay's main points and reiterate the importance of understanding multiple theoretical viewpoints when analyzing gothic literature. # Four Paragraph Essay on the Chapter 1. In the chapter "8th Grade Literature: Exploring Themes in Gothic Literature through Edgar Allan Poe’s Short Stories," the reader is introduced to a comprehensive analysis of the master storyteller's work. Poe's ability to create suspense and terror through the use of gothic elements, such as macabre settings and supernatural occurrences, has captivated readers for centuries. The chapter delves into the definition of gothic literature and its key characteristics, providing a foundation for understanding the thematic depths of Poe's short stories. 2. The main themes presented in Poe's gothic short stories include madness, death, and isolation, which are explored through his characters' struggles with their inner demons and the harsh realities of their world. These themes serve as a window into the human psyche, allowing readers to reflect on their own fears and vulnerabilities. 3. Theoretical viewpoints on gothic literature are presented through three major perspectives: the Gothic Tradition, the New Critical approach, and the Poststructuralist viewpoint. Each perspective offers a unique interpretation of Poe's short stories, providing a rich tapestry of understanding for the reader. The Gothic Tradition emphasizes the supernatural horrors within the stories, while the New Critical approach delves into the symbolism and linguistic devices employed by Poe. The Poststructuralist viewpoint, on the other hand, calls into question the very nature of reality in Poe's work. 4. By comparing and contrasting these theoretical viewpoints, the chapter highlights the importance of understanding multiple perspectives when interpreting gothic literature. Through this multifaceted analysis, readers gain a deeper appreciation for the complexities and nuances of Poe's masterpieces, ultimately enhancing their understanding and appreciation of gothic literature as a whole.
write an outline for a short essay on the chapter
compose a four paragraph essay on the chapter
- Your target audience consists of experts with great familiarity. - Compare and contrast different theories, methodologies, or viewpoints within the subject.
"8th Grade Literature: Exploring Themes in Gothic Literature through Edgar Allan Poe’s Short Stories
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Pretend you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "Political Science: The Impact of Globalization on National Security: An Analysis through Foreign Policy and International Trade". Your **first task** is to write a random page from the chapter. Your **last task** is to demonstrate a fundamental idea from your response through an example with Python code. Notes: - Write in a balanced tone, mixing formality with accessibility. - Provide a detailed and in-depth exploration of the topic. ### Response:
As the global landscape continues to evolve, the impact of globalization on national security has become a critical issue for nations to address. This chapter will delve into the intricacies of how globalization influences foreign policy and international trade, presenting a comprehensive analysis that outlines the potential consequences for a nation's security. One of the primary ways in which globalization impacts national security is through the changes it induces in foreign policy. With the increasing interdependence of nations, foreign policy must adapt to the new realities of globalization. This may involve a shift in focus from traditional diplomatic relations to partnerships that emphasize mutual economic interests. For example, countries may prioritize trade agreements and investment opportunities over traditional alliances, as economic cooperation becomes a more powerful tool for fostering security. ```python # Example: Calculating the value of international trade as a measure of globalization trade_value_a = 1000000000 # Trade value between Country A and Country B in USD trade_value_b = 800000000 # Trade value between Country A and Country C in USD total_trade_value = trade_value_a + trade_value_b print("Total trade value between Country A and the rest of the world:", total_trade_value) ``` This shift in foreign policy priorities can lead to a more cooperative and interconnected world, potentially reducing the likelihood of conflict. However, it may also open up new avenues for manipulation and exploitation by malicious actors. As nations increasingly rely on international trade and investment for their economic security, they become more vulnerable to disruptions in global markets. This increased interdependence can make nations more susceptible to blackmail or coercion by other nations or non-state actors. In conclusion, the impact of globalization on national security is multifaceted and complex. While it may foster greater cooperation and economic interdependence, it also presents new opportunities for exploitation and vulnerability. Understanding these dynamics is crucial for policymakers and analysts alike as they navigate the changing security landscape brought about by globalization.
write a random page from the chapter
demonstrate a fundamental idea from your response through an example with Python code
- Write in a balanced tone, mixing formality with accessibility. - Provide a detailed and in-depth exploration of the topic.
"Political Science: The Impact of Globalization on National Security: An Analysis through Foreign Policy and International Trade
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Pretend you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "Advanced Physics: Quantum Mechanics and the Study of Particle Behavior Using CERN's Large Hadron Collider". Your **first task** is to write an outline for a short essay on the chapter. Your **second task** is to compose a four paragraph essay on the chapter. Your **last task** is to create a set of assessment questions that test the reader's understanding of the chapter. Notes: - Adopt a conversational and informal style to make the content accessible. - Focus solely on the current state and future implications of the subject. You will be graded on how complete and appropriate your response is. ### Response:
**Outline:** 1. Introduction: Brief overview of quantum mechanics and the Large Hadron Collider (LHC) 2. The history and development of the Large Hadron Collider 3. Quantum mechanics: A brief explanation of the principles and concepts 4. The role of the Large Hadron Collider in the study of quantum mechanics 5. Current findings and milestones achieved through the LHC 6. Future implications and potential discoveries 7. Conclusion: Emphasizing the ongoing importance and relevance of the subject **Essay:** In the chapter "Advanced Physics: Quantum Mechanics and the Study of Particle Behavior Using CERN's Large Hadron Collider," we dive into the fascinating world of quantum mechanics, which has revolutionized our understanding of the microscopic universe. The Large Hadron Collider (LHC) has played a pivotal role in advancing our knowledge in this field. Not only has it provided us with a deeper understanding of the principles behind quantum mechanics, but it has also led to groundbreaking discoveries and opened up new avenues for further research. One of the primary goals of the LHC is to investigate the behavior of particles at extreme energies, which has allowed scientists to unravel the mysteries of quantum mechanics. The LHC has already made several significant discoveries, such as the Higgs boson, a particle long theorized to give other particles their mass. This discovery has not only validated the Standard Model of particle physics but has also provided valuable insights into the inner workings of our universe. As we continue to explore the unknown, the Large Hadron Collider remains at the forefront of scientific endeavor. Its capabilities will undoubtedly lead to new discoveries and developments in our understanding of the quantum world. The potential implications of these advancements are vast, and could potentially revolutionize our understanding of everything from medicine to technology. As we venture further into this realm of subatomic particles, the study of quantum mechanics and the use of the Large Hadron Collider will undoubtedly continue to shape the future of physics and our understanding of the world around us. **Assessment Questions:** 1. What is the primary goal of the Large Hadron Collider in relation to quantum mechanics? 2. What was the significance of the discovery of the Higgs boson? 3. In what ways has the Large Hadron Collider advanced our understanding of quantum mechanics? 4. What potential future implications could arise from the study of quantum mechanics using the Large Hadron Collider?
write an outline for a short essay on the chapter
compose a four paragraph essay on the chapter
create a set of assessment questions that test the reader's understanding of the chapter
- Adopt a conversational and informal style to make the content accessible. - Focus solely on the current state and future implications of the subject. You will be graded on how complete and appropriate your response is.
"Advanced Physics: Quantum Mechanics and the Study of Particle Behavior Using CERN's Large Hadron Collider
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Imagine you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "Physics: The Principles of Thermodynamics and their Applications in Engineering". Your **first task** is to write an outline for the chapter. Your **second task** is to compose a twelve paragraph page from the chapter. Your **last task** is to create a set of assessment questions that test the reader's understanding of the chapter. Notes: - Write in a balanced tone, mixing formality with accessibility. - Delve into detailed exploration, providing comprehensive insights. ### Response:
## Outline I. Introduction to Thermodynamics - Definition of thermodynamics - Importance of thermodynamics in engineering - Overview of principles of thermodynamics II. The First Law of Thermodynamics - Description of the first law - Mathematical representation - Examples of the first law in action III. The Second Law of Thermodynamics - Description of the second law - Entropy and its role - Examples of entropy in action IV. The Third Law of Thermodynamics - Description of the third law - Limitations of the third law - Importance of the third law V. Applications of Thermodynamics in Engineering - Heat engines and refrigeration cycles - Carnot's theorem and its significance - Efficiency and power analyses VI. Engineering Principles: The Ideal Gas Law - Description of the ideal gas law - Applications in piston-cylinder systems - Relation to the first law of thermodynamics VII. Engineering Principles: Boyle's Law and Charles's Law - Description of Boyle's and Charles's laws - Applications in gas compression and expansion - Relation to the second law of thermodynamics VIII. Engineering Principles: Joule's Experiment - Description of Joule's experiment - Significance in the development of thermodynamics - Relation to the first law of thermodynamics IX. Thermodynamics and the Environment - Relationship between thermodynamics and climate change - Importance of energy efficiency in modern engineering - The role of engineering in mitigating climate change X. Conclusion - Recap of the chapter's main concepts - Importance of thermodynamics in engineering - Encouragement for further study ## Assessment Questions 1. Define the first, second, and third laws of thermodynamics. 2. Explain the relationship between entropy and the second law of thermodynamics. 3. Describe the ideal gas law and its applications in engineering. 4. Briefly explain the significance of Joule's experiment in the development of thermodynamics. 5. Discuss the role of energy efficiency in modern engineering and its relation to thermodynamics.
write an outline for the chapter
compose a twelve paragraph page from the chapter
create a set of assessment questions that test the reader's understanding of the chapter
- Write in a balanced tone, mixing formality with accessibility. - Delve into detailed exploration, providing comprehensive insights.
"Physics: The Principles of Thermodynamics and their Applications in Engineering
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Pretend you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "Python Machine Learning: Practical Approach to Hierarchical Clustering Algorithms with SciPy, Jupyter Notebooks, and Scikit-Learn". Your **first task** is to write an outline for a long essay on the chapter. Your **second task** is to compose a nine paragraph essay on the chapter. Your **last task** is to demonstrate a fundamental idea from your response through an example with Python code. Notes: - Keep the content concise and to the point. ### Response:
**Outline for a long essay on the chapter:** I. Introduction to Python Machine Learning and Hierarchical Clustering A. Definition of machine learning B. Hierarchical clustering algorithms C. Importance of SciPy, Jupyter Notebooks, and Scikit-Learn II. Practical Approach to Hierarchical Clustering Algorithms A. Distance-based methods 1. Euclidean distance 2. Manhattan distance B. Linkage methods 1. Single linkage 2. Complete linkage 3. Average linkage 4. Ward's linkage C. Dendrogram visualization III. Introduction to SciPy A. Overview of SciPy library B. Benefits of using SciPy C. Applications of SciPy in hierarchical clustering IV. Introduction to Jupyter Notebooks A. Definition of Jupyter Notebooks B. Benefits of using Jupyter Notebooks for machine learning C. Integration of Jupyter Notebooks with Python, SciPy, and Scikit-Learn V. Introduction to Scikit-Learn A. Overview of Scikit-Learn library B. Benefits of using Scikit-Learn in machine learning C. Applications of Scikit-Learn in hierarchical clustering VI. Conclusion A. Recap of the chapter's content B. Importance of understanding hierarchical clustering algorithms C. Encouragement to explore further and apply knowledge to practical problems **Nine paragraph essay on the chapter:** In the chapter on "Python Machine Learning: Practical Approach to Hierarchical Clustering Algorithms with SciPy, Jupyter Notebooks, and Scikit-Learn," we explore the world of machine learning through the lens of hierarchical clustering algorithms. This approach allows us to divide large datasets into smaller, more manageable clusters based on their similarities and differences. One of the first things we discuss is the concept of machine learning, which involves training a computer to recognize patterns and make predictions. This is particularly useful in hierarchical clustering, as it enables the algorithm to learn the underlying structure of the data. Next, we delve into the nitty-gritty of hierarchical clustering algorithms, covering various distance-based methods such as Euclidean and Manhattan distance, and linkage methods including single, complete, average, and Ward's linkage. These methods help in building a hierarchical tree of the data, which in turn helps in identifying the optimal number of clusters. The importance of dendrogram visualization is also emphasized, as it provides a graphical representation of the hierarchical tree and helps in understanding the structure of the data. We then introduce the powerful SciPy library, which is a collection of mathematical algorithms and convenience functions built on the NumPy. SciPy is invaluable in the realm of machine learning as it offers various statistical and optimization functions that simplify complex calculations. Jupyter Notebooks, a popular tool for data science and machine learning, is then introduced. With its interactivity and ease of use, Jupyter Notebooks allow us to write, execute, and visualize code in one place, making it an ideal choice for working with Python, SciPy, and Scikit-Learn in the context of hierarchical clustering. Finally, we take a look at Scikit-Learn, a widely used machine learning library for Python. Scikit-learn provides a range of algorithms for classification, regression, clustering, and more, and is an indispensable tool when working with large datasets and complex problems. In conclusion, the chapter offers a practical guide to understanding and implementing hierarchical clustering algorithms using Python, SciPy, Jupyter Notebooks, and Scikit-learn. This knowledge can be applied to a variety of real-world problems, and serves as a strong foundation for further exploration into the fascinating world of machine learning. **Example of a fundamental idea from the response through a Python code:** Let's illustrate a fundamental idea from the response, which is the Euclidean distance calculation between two points in a two-dimensional space. We'll use the `math` module in Python to perform the calculation. ```python import math def euclidean_distance(point1, point2): return math.sqrt((point2[0] - point1[0]) ** 2 + (point2[1] - point1[1]) ** 2) # Example usage: point_a = (2, 3) point_b = (5, 7) distance = euclidean_distance(point_a, point_b) print(f"The Euclidean distance between points A and B is {distance}") ``` This code defines a function `euclidean_distance` that takes two points as input and returns their Euclidean distance. The example usage at the end demonstrates how to use the function to find the distance between two points in a two-dimensional space.
write an outline for a long essay on the chapter
compose a nine paragraph essay on the chapter
demonstrate a fundamental idea from your response through an example with Python code
- Keep the content concise and to the point.
"Python Machine Learning: Practical Approach to Hierarchical Clustering Algorithms with SciPy, Jupyter Notebooks, and Scikit-Learn
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Pretend you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "Chemistry: The Science of Brewing Beer - A Study of Fermentation Process". Your **first task** is to write three paragraphs from the page concluding the chapter. Your **last task** is to write a single paragraph on a related subject. Notes: - Your target audience consists of beginners with no familiarity of the subject. - Focus solely on the current state and future implications of the subject. ### Response:
In conclusion, the chapter has delved into the fascinating world of chemistry as it pertains to the brewing of beer. We have explored the intricate process of fermentation, a biological conversion that transforms starches and sugars into alcohol and carbon dioxide. This process is essential in the production of beer, and understanding it is vital for aspiring brewers. As we move forward into the future, researchers continue to develop new techniques and technologies to enhance the brewing process, improve the quality of beer, and minimize waste. For example, advances in the field of yeast genetics have led to the development of more efficient, higher-performing strains. Additionally, the use of innovative brewing techniques, such as spontaneous fermentation and sour beer production, is gaining popularity among craft brewers. As our knowledge of chemistry and brewing continually grows, so too will the potential for creating exceptional beer experiences. The chapter has provided a comprehensive introduction to the science of brewing beer, emphasizing the importance of fermentation in the process. By understanding the underlying chemistry, beginners can better appreciate the complexities of beer production and appreciate the finer details that differentiate one beer from another. As the field of chemistry and brewing continues to evolve, the future holds even more opportunities for growth and innovation in this ancient yet ever-evolving craft. Relatedly, the environmental impact of beer production is an important consideration for both the beer industry and consumers. As the world becomes increasingly aware of the importance of sustainability, brewers are turning their attention to reducing their carbon footprint. This can be achieved through the use of renewable energy sources, implementing waste reduction strategies, and sourcing ingredients from sustainable and local sources. By embracing these practices, the beer industry can not only protect the environment but also foster a sense of social responsibility among consumers and producers alike. In the final paragraph of this chapter, we will explore the ways in which advancements in chemistry and brewing can contribute to a greener, more sustainable future for the beer industry.
write three paragraphs from the page concluding the chapter
write a single paragraph on a related subject
- Your target audience consists of beginners with no familiarity of the subject. - Focus solely on the current state and future implications of the subject.
"Chemistry: The Science of Brewing Beer - A Study of Fermentation Process
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Imagine you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "nd Grade Health Education: Food, Nutrition, and Health - Learning with 3D Food Models". Your **first task** is to write an outline for a random page from the middle of the chapter. Your **second task** is to compose the random page. Your **last task** is to create a set of assessment questions that test the reader's understanding of the chapter. Notes: - Your target audience consists of beginners with no familiarity of the subject. - Write in a balanced tone, mixing formality with accessibility. You will be graded on how complete and appropriate your response is. ### Response:
**Outline for a Random Page from the Middle of the Chapter:** I. Introduction to Food, Nutrition, and Health A. Definition of Health B. Importance of a balanced diet in maintaining health C. Overview of essential nutrients II. The Importance of Eating a Balanced Diet A. Benefits of a balanced diet 1. Improved physical and mental well-being 2. Reduced risk of chronic diseases 3. Better overall quality of life B. Key components of a balanced diet 1. Fruits 2. Vegetables 3. Whole grains 4. Protein-rich foods 5. Dairy and alternatives 6. Healthy fats 7. Water and other beverages III. Learning with 3D Food Models A. Overview of 3D food models in health education B. Examples of 3D food models and their applications 1. MyPlate 3D Model 2. Food Pyramid 3D Model 3. Body Composition Analyzer (BCA) 3D Model C. How 3D food models can enhance learning 1. Visual representation of food groups 2. Interactive experience for students 3. Facilitation of nutrition-related discussions IV. Putting it All Together A. The role of food, nutrition, and health education in promoting a balanced diet B. Strategies for incorporating 3D food models into health education curriculum C. Conclusion **Random Page from the Middle of the Chapter:** IV. Putting it All Together The goal of this textbook chapter is to provide a comprehensive understanding of the importance of food, nutrition, and health in the context of nd grade education. By learning about the essential nutrients, the components of a balanced diet, and the benefits of incorporating 3D food models into the curriculum, students will gain the knowledge and skills necessary to make informed decisions about their diet and overall health. By the end of this chapter, readers should have a solid foundation for implementing health education strategies that promote a balanced diet and encourage healthy lifestyles. **Assessment Questions:** 1. What is the primary purpose of a balanced diet? 2. Name three key components of a balanced diet. 3. Describe the role of 3D food models in health education. 4. How can incorporating 3D food models enhance learning about food, nutrition, and health? 5. What strategies can be employed to incorporate 3D food models into health education curriculum?
write an outline for a random page from the middle of the chapter
create a set of assessment questions that test the reader's understanding of the chapter
- Your target audience consists of beginners with no familiarity of the subject. - Write in a balanced tone, mixing formality with accessibility. You will be graded on how complete and appropriate your response is.
"nd Grade Health Education: Food, Nutrition, and Health - Learning with 3D Food Models
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Imagine you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "Neuroscience: Understanding the Role of Neurotransmitters in Mood and Behavior". Your **first task** is to write twelve paragraphs from a page in the middle of the chapter. Notes: - Adopt a conversational and informal style to make the content accessible. - Compare and contrast different theories, methodologies, or viewpoints within the subject. - Delve into detailed exploration, providing comprehensive insights. You will be graded on how complete and appropriate your response is. ### Response:
Neuroscience: Understanding the Role of Neurotransmitters in Mood and Behavior Mood and behavior are deeply intertwined, with neurotransmitters playing a significant role in regulating our emotions and actions. Neurotransmitters are chemical messengers that transmit signals between neurons, allowing the flow of information throughout the brain. In this chapter, we'll delve into the fascinating world of neurotransmitters and their role in mood and behavior. First, let's discuss the various types of neurotransmitters and their functions. There are several major classes of neurotransmitters, including: - Serotonin: Responsible for regulating mood, appetite, and sleep. It plays a crucial role in maintaining a stable emotional state. When serotonin levels are balanced, we experience a sense of well-being and happiness. - Dopamine: Known as the "pleasure chemical," dopamine is involved in motivation, reward, and movement. It's responsible for the pleasurable sensations associated with eating, sex, and achievement. - Noradrenaline: Also known as norepinephrine, noradrenaline is involved in alertness, focus, and stress. When levels are high, we feel energized and alert; when they're low, we may feel tired and run-down. - Acetylcholine: This neurotransmitter is essential for memory, learning, and muscle function. It's responsible for controlling motor skills and coordination. Now, let's explore some of the theories and methodologies used to study neurotransmitters and their role in mood and behavior. The monoamine hypothesis suggests that the balance of neurotransmitters, particularly serotonin, dopamine, and norepinephrine, is critical for maintaining mental health. This hypothesis posits that imbalances in these neurotransmitters can lead to mood disorders, such as depression and anxiety. Another theory is the two-factor theory, which proposes that mood is determined by a combination of genetic predisposition and life experiences. This theory suggests that the interaction between our genetic makeup and environmental factors results in our unique mood and behavior. In recent years, researchers have started to investigate the role of epigenetics in mood and behavior. Epigenetics is the study of heritable changes in gene expression that do not involve changes to the genome's DNA sequence. Some studies have shown that certain epigenetic marks, such as methylation, can be associated with variations in neurotransmitter levels and, consequently, mood and behavior. Next, let's explore the impact of neurotransmitters on mood and behavior in more detail. Serotonin, for example, is believed to play a crucial role in regulating mood. When serotonin levels are high, we feel happy and content. However, when serotonin levels are low, we may experience depression, anxiety, and other mood disorders. Dopamine, on the other hand, is responsible for the "reward" response, which is why it's often referred to as the "pleasure chemical." High dopamine levels can lead to feelings of pleasure, while low dopamine levels can result in addiction and compulsive behaviors. Noradrenaline, or norepinephrine, is involved in regulating alertness and stress. When noradrenaline levels are high, we feel energized and focused. However, when levels are too low, we may feel sluggish and lack motivation. Acetylcholine, finally, is essential for memory, learning, and muscle function. A lack of acetylcholine can result in conditions like Alzheimer's and Parkinson's diseases, which can significantly impact mood and behavior. In conclusion, neuroscience has revealed a complex interplay between neurotransmitters and mood and behavior. Understanding this relationship is crucial for developing targeted treatments for mental health disorders and promoting overall well-being. As we continue to learn more about the roles of neurotransmitters in our lives, we can expect to see advancements in both our understanding and treatment of mood and behavior disorders.
write twelve paragraphs from a page in the middle of the chapter
- Adopt a conversational and informal style to make the content accessible. - Compare and contrast different theories, methodologies, or viewpoints within the subject. - Delve into detailed exploration, providing comprehensive insights. You will be graded on how complete and appropriate your response is.
"Neuroscience: Understanding the Role of Neurotransmitters in Mood and Behavior
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Imagine you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "Physics: The Quantum World: Understanding Particle Physics with Feynman Diagrams". Your **first task** is to write an outline for a medium length essay on the chapter. Your **second task** is to compose a seven paragraph essay. Notes: - Compare and contrast different theories, methodologies, or viewpoints within the subject. You will be graded on how complete and appropriate your response is. ### Response:
Outline for a Medium Length Essay on "Physics: The Quantum World: Understanding Particle Physics with Feynman Diagrams" I. Introduction - Brief overview of the chapter topic and its importance in modern physics - Introduction of Feynman diagrams and their role in understanding particle physics II. Historical Background - Development of quantum theory by Niels Bohr and Werner Heisenberg - Rise of quantum mechanics and its challenges to classical mechanics - Introduction of Feynman diagrams by Richard Feynman as a tool for simplifying calculations III. Theoretical Foundations of Quantum Mechanics - Principles of quantum mechanics, such as the uncertainty principle, wave-particle duality, and quantum entanglement - Spin and its role in quantum mechanics - Quantum field theory as a unified framework for describing fundamental forces and particles IV. Feynman Diagrams: An Introduction - Definition and basic structure of Feynman diagrams - Explanation of the various types of vertices, lines, and internal structure - Examples of Feynman diagrams used to represent specific physical processes V. The Quantum World: Particle Physics Perspective - Overview of particle physics and its focus on the behavior of elementary particles - Role of Feynman diagrams in describing particle interactions, such as scattering, decay, and annihilation processes - Explanation of the Standard Model of particle physics and its constituent particles and forces VI. Applications and Limitations of Feynman Diagrams - Advantages of using Feynman diagrams for teaching and understanding particle physics concepts - Limitations of the diagrams, such as their inability to represent certain aspects of quantum mechanics - Importance of combining Feynman diagrams with other tools and techniques for a comprehensive understanding of the quantum world VII. Conclusion - Summary of the key points discussed in the essay - The significance of Feynman diagrams in the study of particle physics and their role in shaping our understanding of the quantum world --- Seven Paragraph Essay I. The chapter on "Physics: The Quantum World: Understanding Particle Physics with Feynman Diagrams" delves into the fascinating realm of quantum mechanics and its application to particle physics. Quantum mechanics, which emerged as a challenge to classical mechanics, introduced principles such as the uncertainty principle and wave-particle duality, which revolutionized our understanding of the microscopic world. Quantum field theory, a unified framework for describing fundamental forces and particles, further extended our knowledge of the quantum world. II. Richard Feynman, a renowned physicist, introduced Feynman diagrams as a tool for simplifying calculations in quantum field theory. These diagrams provide a visual representation of the processes involved in particle interactions, such as scattering, decay, and annihilation. They are composed of vertices, lines, and internal structure reflecting the participants and their interactions in the process. III. This essay will explore the quantum world through the lens of particle physics, which focuses on the behavior of elementary particles. Feynman diagrams play a pivotal role in describing these interactions using a visual language that is easily digestible. The Standard Model, a comprehensive theory of particle physics, is discussed, with its constituent particles and forces providing a foundation for understanding the quantum world. IV. Feynman diagrams have numerous advantages in teaching and understanding particle physics concepts. They provide a straightforward way to represent specific physical processes and are a valuable teaching tool. However, there are limitations to their effectiveness, such as their inability to fully capture certain aspects of quantum mechanics. As such, a combination of Feynman diagrams with other tools and techniques is essential for a comprehensive understanding of the quantum world. V. Quantum mechanics and particle physics continue to challenge and inspire scientific inquiry. The chapter provides a captivating exploration of the quantum world, its intricacies, and the power of Feynman diagrams in illuminating these concepts. As our understanding of the universe expands, Feynman diagrams will undoubtedly remain a vital tool in the study of particle physics and the broader realm of quantum mechanics. VI. In conclusion, the essay emphasizes the importance of combining Feynman diagrams with other tools and techniques for a comprehensive understanding of the quantum world. This approach not only enhances our appreciation for the beauty and complexity of quantum mechanics but also fosters a deeper appreciation for the fundamental constants and principles that govern our world. As we continue to uncover the mysteries of the quantum world, Feynman diagrams will remain a valuable asset in our quest for knowledge. VII. The chapter "Physics: The Quantum World: Understanding Particle Physics with Feynman Diagrams" offers a fascinating exploration into the intricacies of the quantum world, with a particular focus on the application of Feynman diagrams in particle physics. This essay has demonstrated the significance of Feynman diagrams in visualizing complex quantum processes and the need for a multi-faceted approach to fully grasp the depths of the quantum world. As we continue to delve into the unknown, Feynman diagrams will undoubtedly remain a powerful and indispensable tool in our scientific journey.
write an outline for a medium length essay on the chapter
compose a seven paragraph essay
- Compare and contrast different theories, methodologies, or viewpoints within the subject. You will be graded on how complete and appropriate your response is.
"Physics: The Quantum World: Understanding Particle Physics with Feynman Diagrams
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Pretend you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "Microbiology: Unraveling Bacterial Resistance through Genomic Sequencing and Antibiotic Susceptibility Testing". Your **first task** is to write five paragraphs from the page concluding the chapter. Your **last task** is to demonstrate a fundamental idea from your response through an Python code snippet. Notes: - Your target audience consists of intermediates with some familiarity of the subject. ### Response:
In conclusion, the application of genomic sequencing and antibiotic susceptibility testing has revolutionized the field of microbiology. These techniques have provided researchers with unprecedented insights into the mechanisms of bacterial resistance, paving the way for more effective treatment options and targeted therapies. The ability to sequence an organism's genome allows scientists to identify genetic variations that may confer resistance to antimicrobial agents. This knowledge can then be leveraged to develop more potent and targeted treatments, ultimately improving patient outcomes. Furthermore, the integration of antibiotic susceptibility testing (AST) into genomic sequencing analysis offers a comprehensive understanding of the factors contributing to bacterial resistance. AST involves the systematictesting of an organism's susceptibility to a panel of antibiotics, providing a detailed profile of its resistance pattern. By comparing the genetic information with the susceptibility results, researchers can identify specific gene mutations and genetic modifications that confer resistance to specific antibiotics. This information can then be used to design effective targeted therapies and inform public health initiatives. In light of the growing concern about antimicrobial resistance, it is crucial to continue expanding our understanding of the mechanisms underlying bacterial resistance. By harnessing the power of genomic sequencing and antibiotic susceptibility testing, we can take a proactive approach towards developing innovative treatment strategies. As the field of microbiology evolves, it is essential to maintain a multidisciplinary approach that incorporates genomics, molecular biology, and clinical science to address the complex challenges posed by bacterial resistance. In the future, it is anticipated that the integration of these techniques will play a pivotal role in combating antimicrobial resistance. As researchers continue to refine their understanding of the genetic determinants of bacterial resistance, the development of personalized antimicrobial therapies will become increasingly feasible. Furthermore, the application of machine learning algorithms to genomic data can further enhance our ability to predict and prevent resistance development. By staying at the forefront of this evolving field, we can ensure that we are best equipped to address the global threat of antimicrobial resistance. Here's a Python code snippet that demonstrates the fundamental idea of calculating susceptibility from a genotype and phenotype dataset: ```python # Sample data representing genotype and phenotype information genotype_data = {'gene1': 'A', 'gene2': 'B', 'gene3': 'C'} phenotype_data = {'antibiotic1': 'susceptible', 'antibiotic2': 'resistant'} # Function to calculate antibiotic susceptibility based on genotype and phenotype information def calculate_susceptibility(genotype_data, phenotype_data): susceptibility = {} for antibiotic, phenotype in phenotype_data.items(): if phenotype == 'susceptible': susceptibility[antibiotic] = 'susceptible' else: for gene, allele in genotype_data.items(): if allele == 'A': # example condition for resistance susceptibility[antibiotic] = 'resistant' break else: susceptibility[antibiotic] = 'susceptible' return susceptibility # Calculate susceptibility susceptibility = calculate_susceptibility(genotype_data, phenotype_data) print(susceptibility) ``` This code snippet demonstrates the fundamental idea of calculating antibiotic susceptibility from genotype and phenotype data. It is a simplified representation of the complex processes involved in real-world susceptibility testing and genomic sequencing analysis, but it serves as a starting point for understanding the underlying concepts.
write five paragraphs from the page concluding the chapter
demonstrate a fundamental idea from your response through an Python code snippet
- Your target audience consists of intermediates with some familiarity of the subject.
"Microbiology: Unraveling Bacterial Resistance through Genomic Sequencing and Antibiotic Susceptibility Testing
TheBloke/Phind-CodeLlama-34B-v2-AWQ
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Imagine you are a prolific author tasked with writing a textbook. You are working on writing a textbook chapter titled "Computer Science: Implementing Data Privacy using Quantum Cryptography". Your **first task** is to write four paragraphs from the introduction page of the chapter. Your **last task** is to reflect on the greater importance of this subject in the context of all of humanity's knowledge. Notes: ### Response:
Introduction: In today's interconnected world, the importance of data privacy and security has never been more critical. With the rapid advancement of technology and the increasing reliance on digital systems, individuals and organizations are accumulating vast amounts of personal and sensitive information. As a result, the need for robust and effective mechanisms to protect this information has become paramount. One such cutting-edge technology that is revolutionizing the field of data privacy is Quantum Cryptography. In this chapter, we will delve into the fascinating world of Quantum Cryptography and its applications in Computer Science. We will explore the principles behind this revolutionary technique and examine how it can be used to ensure the secure transmission and storage of sensitive data. We will also examine the potential limitations of Quantum Cryptography and discuss potential solutions to these obstacles. Throughout the chapter, we will emphasize the crucial role that Quantum Cryptography plays in preserving the privacy and security of our digital world. The impact of Quantum Cryptography extends far beyond the realm of Computer Science. As our understanding of Quantum Mechanics continues to expand, this groundbreaking technology has the potential to revolutionize a wide range of industries, from healthcare to finance. By ensuring the confidentiality and integrity of data across all sectors, Quantum Cryptography can play a pivotal role in safeguarding the privacy and security of all humanity. In conclusion, the subject of Quantum Cryptography is not only vital for Computer Science professionals but also holds immense potential for shaping the future of privacy and security in all aspects of human endeavor. As we continue to explore the complexities of Quantum Mechanics, it is crucial that we invest in understanding and developing this groundbreaking technology to ensure a secure and private future for all.
write four paragraphs from the introduction page of the chapter
reflect on the greater importance of this subject in the context of all of humanity's knowledge
"Computer Science: Implementing Data Privacy using Quantum Cryptography
TheBloke/Phind-CodeLlama-34B-v2-AWQ |
https://huggingface.co/amazon/sm-hackathon-actionability-9-multi-outputs-setfit-all-distilroberta-model-v0.1 | amazon/sm-hackathon-actionability-9-multi-outputs-setfit-all-distilroberta-model-v0.1
This is a SetFit model that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
Fine-tuning a Sentence Transformer with contrastive learning.
Training a classification head with features from the fine-tuned Sentence Transformer.
Usage
To use this model for inference, first install the SetFit library:
python -m pip install setfit
You can then run inference as follows:
from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("amazon/sm-hackathon-actionability-9-multi-outputs-setfit-all-distilroberta-model-v0.1") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
BibTeX entry and citation info
@article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } |
https://huggingface.co/amazon/sm-hackathon-actionability-9-multi-outputs-setfit-model-v0.1 | amazon/sm-hackathon-actionability-9-multi-outputs-setfit-model-v0.1
This is a SetFit model that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
Fine-tuning a Sentence Transformer with contrastive learning.
Training a classification head with features from the fine-tuned Sentence Transformer.
Usage
To use this model for inference, first install the SetFit library:
python -m pip install setfit
You can then run inference as follows:
from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("amazon/sm-hackathon-actionability-9-multi-outputs-setfit-model-v0.1") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
BibTeX entry and citation info
@article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } |
https://huggingface.co/amazon/sm-hackathon-setfit-model | amazon/sm-hackathon-setfit-model
This is a SetFit model that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
Fine-tuning a Sentence Transformer with contrastive learning.
Training a classification head with features from the fine-tuned Sentence Transformer.
Usage
To use this model for inference, first install the SetFit library:
python -m pip install setfit
You can then run inference as follows:
from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("amazon/sm-hackathon-setfit-model") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
BibTeX entry and citation info
@article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } |
https://huggingface.co/amazon/FalconLite | FalconLite Model
FalconLite is a quantized version of the Falcon 40B SFT OASST-TOP1 model, capable of processing long (i.e. 11K tokens) input sequences while consuming 4x less GPU memory. By utilizing 4-bit GPTQ quantization and adapted dynamic NTK RotaryEmbedding, FalconLite achieves a balance between latency, accuracy, and memory efficiency. With the ability to process 5x longer contexts than the original model, FalconLite is useful for applications such as topic retrieval, summarization, and question-answering. FalconLite can be deployed on a single AWS g5.12x instance with TGI 0.9.2, making it suitable for applications that require high performance in resource-constrained environments.
Model Details
Developed by: AWS Contributors
Model type: Falcon 40B
Language: English
Quantized from weights: Falcon 40B SFT OASST-TOP1 model
Modified from layers: Text-Generation-Inference 0.9.2
License: Apache 2.0
Contact: GitHub issues
Blogpost: Extend the context length of Falcon40B to 10k
Deploy FalconLite
SSH login to an AWS g5.12x instance with the Deep Learning AMI.
Start LLM server
git clone https://github.com/awslabs/extending-the-context-length-of-open-source-llms.git falconlite-dev cd falconlite-dev/script ./docker_build.sh ./start_falconlite.sh
Perform inference
# after FalconLite has been completely started pip install -r requirements-client.txt python falconlite_client.py
New! Amazon SageMaker Deployment
To deploy FalconLite on SageMaker endpoint, please follow this notebook.
Important - When using FalconLite for inference for the first time, it may require a brief 'warm-up' period that can take 10s of seconds. However, subsequent inferences should be faster and return results in a more timely manner. This warm-up period is normal and should not affect the overall performance of the system once the initialisation period has been completed.
Evalution Result
We evaluated FalconLite against benchmarks that are specifically designed to assess the capabilities of LLMs in handling longer contexts. All evaluations were conducted without fine-tuning the model.
Accuracy
Performance
metrics = the average number of generated tokens per second (TPS) =
nb-generated-tokens / end-to-end-response-time
The end-to-end-response-time = when the last token is generated - when the inference request is received
Instance Input length Input length Input length Input length
20 3300 5500 10000
g5.48x 22 tps 12 tps 12 tps 12 tps
g5.12x 18 tps 11 tps 11 tps 10 tps
Limitations
Our evaluation shows that FalconLite's capability in Line Retrieval is limited, and requires further effort.
While g5.12x is sufficient for FalconLite to handle 10K long contexts, a larger instance with more memory capcacity such as g5.48x is recommended for sustained, heavy workloads.
Before using the FalconLite model, it is important to perform your own independent assessment, and take measures to ensure that your use would comply with your own specific quality control practices and standards, and that your use would comply with the local rules, laws, regulations, licenses and terms that apply to you, and your content. |
https://huggingface.co/amazon/sm-hackathon-actionability-9-multi-outputs-setfit-all-roberta-large-model-v0.1 | amazon/sm-hackathon-actionability-9-multi-outputs-setfit-all-roberta-large-model-v0.1
This is a SetFit model that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
Fine-tuning a Sentence Transformer with contrastive learning.
Training a classification head with features from the fine-tuned Sentence Transformer.
Usage
To use this model for inference, first install the SetFit library:
python -m pip install setfit
You can then run inference as follows:
from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("amazon/sm-hackathon-actionability-9-multi-outputs-setfit-all-roberta-large-model-v0.1") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
BibTeX entry and citation info
@article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } |
https://huggingface.co/amazon/LightGPT | LightGPT-instruct-6B Model
LightGPT-instruct is a language model based on GPT-J 6B. It was instruction fine-tuned on the high quality, Apache-2.0 licensed OIG-small-chip2 instruction dataset with ~200K training examples.
Model Details
Developed by: AWS Contributors
Model type: Transformer-based Language Model
Language: English
Finetuned from: EleutherAI / gpt-j-6b
License: Apache 2.0
Contact: Github issues
Prompting
The instruction template (adapted from the Alpaca README.md) is used to format the prompt:
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Response:
Input prompt example:
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: How can I tell if a pomegranate is ripe? ### Response:
The input ends with ### Response:\n to signal that the model should start generating the reply.
Example of generated continuation:
To determine whether or not a pomegranate is ripe, you should look for signs such as color and size. A ripe pomegranate will have deep red-orange skin with small seeds inside. You may also notice that it feels slightly softer than other fruits when squeezed. If you're unsure about ripeness, you can cut into one of the fruit to check its texture. The flesh should be soft but still yield slightly to gentle pressure. Finally, smell the fruit - if it has a sweet aroma, then it's likely ready to eat! Enjoy your delicious pomegranates!
Deployment
Please refer to Deploy models to Amazon SageMaker.
The example code below also shows how to deploy LightGPT-instruct to Amazon SageMaker
# pip install sagemaker==2.159.0 from sagemaker.djl_inference.model import DJLModel #An AWS IAM role that has the permission to create SageMaker endpoint sm_role = "arn:aws:iam::XXX" djl_model = DJLModel( "amazon/LightGPT", sm_role, dtype="fp16", task="text-generation", # number of gpus to partition the model across, default to use 1 GPU number_of_partitions=1 ) # this will take a few minutes to deploy predictor = djl_model.deploy("ml.g5.2xlarge", initial_instance_count=1) input_str = """Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: How can I tell if a pomegranate is ripe? ### Response:""" data = {"inputs": input_str, "parameters": { "max_new_tokens":400, "do_sample": True, "temperature": 0.7, "repetition_penalty": 1.1, "top_p": 0.8, "top_k": 50, "min_length": 200, } } result = predictor.predict(data) print(result[0]["generated_text"]) """ Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: How can I tell if a pomegranate is ripe? ### Response: Ripe pomegranates are usually easy to spot, as they will be slightly soft and give when squeezed gently. You may also notice that the skin of the fruit has begun to turn from green to yellow-green in color. Additionally, you should smell the aroma coming from inside the fruit; it should have a sweet fruity scent. Lastly, check for any blemishes or bruises on the outside of the fruit. If all these signs are present, then your pomegranate is likely ready to be picked! Enjoy your fresh produce! **Note:** To avoid bruising, make sure to cut the stem off before picking. Otherwise, you could end up with a bruised and unappealing piece of fruit. **Warning:** Be careful when handling and cutting pomegranates, as they can easily bruise or break. """
Evaluation result
LAMBADA PPL LAMBADA Acc Winogrande Hellaswag PIQA
GPT-J 3.99 69.7% 65.3% 66.1% 76.5%
LightGPT-instruct 4.33 65.0% 64.6% 63.9% 75.5%
Limitations
See limitations of GPT-J base model here.
The model may fail to follow instructions with long inputs (e.g. summarize a long text). The model often gives incorrect answers to math and reasoning questions.
Beware of hallucinations: Outputs are often factually wrong or misleading. Replies might look convincing (at first glance) while containing completely made up false statements.
This model is usable only for English conversations. |
https://huggingface.co/amazon/bort | ⚠️ Disclaimer ⚠️
This model is community-contributed, and not supported by Amazon, Inc.
BORT
Amazon's BORT
BORT is a highly compressed version of bert-large that is up to 10 times faster at inference. The model is an optimal sub-architecture of bert-large that was found using neural architecture search.
Paper
Abstract
We extract an optimal subset of architectural parameters for the BERT architecture from Devlin et al. (2018) by applying recent breakthroughs in algorithms for neural architecture search. This optimal subset, which we refer to as "Bort", is demonstrably smaller, having an effective (that is, not counting the embedding layer) size of 5.5% the original BERT-large architecture, and 16% of the net size. Bort is also able to be pretrained in 288 GPU hours, which is 1.2% of the time required to pretrain the highest-performing BERT parametric architectural variant, RoBERTa-large (Liu et al., 2019), and about 33% of that of the world-record, in GPU hours, required to train BERT-large on the same hardware. It is also 7.9x faster on a CPU, as well as being better performing than other compressed variants of the architecture, and some of the non-compressed variants: it obtains performance improvements of between 0.3% and 31%, absolute, with respect to BERT-large, on multiple public natural language understanding (NLU) benchmarks.
The original model can be found under: https://github.com/alexa/bort
IMPORTANT
BORT requires a very unique fine-tuning algorithm, called Agora which is not open-sourced yet. Standard fine-tuning has not shown to work well in initial experiments, so stay tuned for updates! |
https://huggingface.co/cfarcas | C
cfarcas
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https://huggingface.co/mikegchambers | Mike Chambers
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https://huggingface.co/engincanmeydan | 1
Engincan Meydan
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https://huggingface.co/flash9001 | 3
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BioMedLM
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https://huggingface.co/aastha6 | Aastha Varma
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LLM: quantization, fine tuning, optimizations | NLP | Deep Learning
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https://huggingface.co/vvaaee | Vladimir Eremichev
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https://huggingface.co/berardin | Nick Berardi
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https://huggingface.co/nehanejh | Neha Jha
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https://huggingface.co/sding | S Ding
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https://huggingface.co/memopenaws | Luis Pena
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https://huggingface.co/haywse | Wooseok Ha
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https://huggingface.co/hphu | Phu
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https://huggingface.co/jianfeili | Jeff Li
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https://huggingface.co/hasandemirkiran | Hasan Demirkiran
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https://huggingface.co/swarooprs | Swaroop Ramarao Satish
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https://huggingface.co/leonidp | Leonid Pishchulin
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https://huggingface.co/KannUN | Yunfang Guan
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https://huggingface.co/crecaido | Charles
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https://huggingface.co/wanghaod | 2
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https://huggingface.co/redfiche | Robert Fisher
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https://huggingface.co/robcost | 5
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https://huggingface.co/cmlott | 2
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https://huggingface.co/dineshmane | 3
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Text Classification • Updated May 1, 2022 • 3
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https://huggingface.co/docs/hub/organizations-cards | Organization cards
You can create an organization card to help users learn more about what your organization is working on and how users can use your libraries, models, datasets, and Spaces.
An organization card is displayed on an organization’s profile:
If you’re a member of an organization, you’ll see a button to create or edit your organization card on the organization’s main page. Organization cards are a README.md static file inside a Space repo named README. The card can be as simple as Markdown text, or you can create a more customized appearance with HTML.
The card for the Hugging Face Course organization, shown above, contains the following HTML:
<p>
This is the organization grouping all the models and datasets used in the <a href="https://huggingface.co/course/chapter1" class="underline">Hugging Face course</a>.
</p>
For more examples, take a look at:
Amazon’s organization card source code
spaCy’s organization card source code. |
https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face | Look at these smiles!
Today, we announce a strategic partnership between Hugging Face and Amazon to make it easier for companies to leverage State of the Art Machine Learning models, and ship cutting-edge NLP features faster.
Through this partnership, Hugging Face is leveraging Amazon Web Services as its Preferred Cloud Provider to deliver services to its customers.
As a first step to enable our common customers, Hugging Face and Amazon are introducing new Hugging Face Deep Learning Containers (DLCs) to make it easier than ever to train Hugging Face Transformer models in Amazon SageMaker.
To learn how to access and use the new Hugging Face DLCs with the Amazon SageMaker Python SDK, check out the guides and resources below.
On July 8th, 2021 we extended the Amazon SageMaker integration to add easy deployment and inference of Transformers models. If you want to learn how you can deploy Hugging Face models easily with Amazon SageMaker take a look at the new blog post and the documentation.
Features & Benefits 🔥
One Command is All you Need
With the new Hugging Face Deep Learning Containers available in Amazon SageMaker, training cutting-edge Transformers-based NLP models has never been simpler. There are variants specially optimized for TensorFlow and PyTorch, for single-GPU, single-node multi-GPU and multi-node clusters.
Accelerating Machine Learning from Science to Production
In addition to Hugging Face DLCs, we created a first-class Hugging Face extension to the SageMaker Python-sdk to accelerate data science teams, reducing the time required to set up and run experiments from days to minutes.
You can use the Hugging Face DLCs with the Automatic Model Tuning capability of Amazon SageMaker, in order to automatically optimize your training hyperparameters and quickly increase the accuracy of your models.
Thanks to the SageMaker Studio web-based Integrated Development Environment (IDE), you can easily track and compare your experiments and your training artifacts.
Built-in Performance
With the Hugging Face DLCs, SageMaker customers will benefit from built-in performance optimizations for PyTorch or TensorFlow, to train NLP models faster, and with the flexibility to choose the training infrastructure with the best price/performance ratio for your workload.
The Hugging Face DLCs are fully integrated with the SageMaker distributed training libraries, to train models faster than was ever possible before, using the latest generation of instances available on Amazon EC2.
Resources, Documentation & Samples 📄
Below you can find all the important resources to all published blog posts, videos, documentation, and sample Notebooks/scripts.
Blog/Video
AWS: Embracing natural language processing with Hugging Face
Deploy Hugging Face models easily with Amazon SageMaker
AWS and Hugging Face collaborate to simplify and accelerate adoption of natural language processing models
Walkthrough: End-to-End Text Classification
Working with Hugging Face models on Amazon SageMaker
Distributed Training: Train BART/T5 for Summarization using 🤗 Transformers and Amazon SageMaker
Deploy a Hugging Face Transformers Model from S3 to Amazon SageMaker
Deploy a Hugging Face Transformers Model from the Model Hub to Amazon SageMaker
Documentation
Hugging Face documentation for Amazon SageMaker
Run training on Amazon SageMaker
Deploy models to Amazon SageMaker
Frequently Asked Questions
Amazon SageMaker documentation for Hugging Face
Python SDK SageMaker documentation for Hugging Face
Deep Learning Container
SageMaker's Distributed Data Parallel Library
SageMaker's Distributed Model Parallel Library
Sample Notebook
all Notebooks
Getting Started Pytorch
Getting Started Tensorflow
Distributed Training Data Parallelism
Distributed Training Model Parallelism
Spot Instances and continue training
SageMaker Metrics
Distributed Training Data Parallelism Tensorflow
Distributed Training Summarization
Image Classification with Vision Transformer
Deploy one of the 10,000+ Hugging Face Transformers to Amazon SageMaker for Inference
Deploy a Hugging Face Transformer model from S3 to SageMaker for inference
Getting started: End-to-End Text Classification 🧭
In this getting started guide, we will use the new Hugging Face DLCs and Amazon SageMaker extension to train a transformer model on binary text classification using the transformers and datasets libraries.
We will use an Amazon SageMaker Notebook Instance for the example. You can learn here how to set up a Notebook Instance.
What are we going to do:
set up a development environment and install sagemaker
create the training script train.py
preprocess our data and upload it to Amazon S3
create a HuggingFace Estimator and train our model
Set up a development environment and install sagemaker
As mentioned above we are going to use SageMaker Notebook Instances for this. To get started you need to jump into your Jupyer Notebook or JupyterLab and create a new Notebook with the conda_pytorch_p36 kernel.
Note: The use of Jupyter is optional: We could also launch SageMaker Training jobs from anywhere we have an SDK installed, connectivity to the cloud and appropriate permissions, such as a Laptop, another IDE or a task scheduler like Airflow or AWS Step Functions.
After that we can install the required dependencies
pip install "sagemaker>=2.31.0" "transformers==4.6.1" "datasets[s3]==1.6.2" --upgrade
To run training on SageMaker we need to create a sagemaker Session and provide an IAM role with the right permission. This IAM role will be later attached to the TrainingJob enabling it to download data, e.g. from Amazon S3.
import sagemaker sess = sagemaker.Session() # sagemaker session bucket -> used for uploading data, models and logs # sagemaker will automatically create this bucket if it not exists sagemaker_session_bucket=None if sagemaker_session_bucket is None and sess is not None: # set to default bucket if a bucket name is not given sagemaker_session_bucket = sess.default_bucket() role = sagemaker.get_execution_role() sess = sagemaker.Session(default_bucket=sagemaker_session_bucket) print(f"sagemaker role arn: {role}") print(f"sagemaker bucket: {sess.default_bucket()}") print(f"sagemaker session region: {sess.boto_region_name}")
Create the training script train.py
In a SageMaker TrainingJob we are executing a python script with named arguments. In this example, we use PyTorch together with transformers. The script will
pass the incoming parameters (hyperparameters from HuggingFace Estimator)
load our dataset
define our compute metrics function
set up our Trainer
run training with trainer.train()
evaluate the training and save our model at the end to S3.
from transformers import AutoModelForSequenceClassification, Trainer, TrainingArguments from sklearn.metrics import accuracy_score, precision_recall_fscore_support from datasets import load_from_disk import random import logging import sys import argparse import os import torch if __name__ == "__main__": parser = argparse.ArgumentParser() # hyperparameters sent by the client are passed as command-line arguments to the script. parser.add_argument("--epochs", type=int, default=3) parser.add_argument("--train-batch-size", type=int, default=32) parser.add_argument("--eval-batch-size", type=int, default=64) parser.add_argument("--warmup_steps", type=int, default=500) parser.add_argument("--model_name", type=str) parser.add_argument("--learning_rate", type=str, default=5e-5) # Data, model, and output directories parser.add_argument("--output-data-dir", type=str, default=os.environ["SM_OUTPUT_DATA_DIR"]) parser.add_argument("--model-dir", type=str, default=os.environ["SM_MODEL_DIR"]) parser.add_argument("--n_gpus", type=str, default=os.environ["SM_NUM_GPUS"]) parser.add_argument("--training_dir", type=str, default=os.environ["SM_CHANNEL_TRAIN"]) parser.add_argument("--test_dir", type=str, default=os.environ["SM_CHANNEL_TEST"]) args, _ = parser.parse_known_args() # Set up logging logger = logging.getLogger(__name__) logging.basicConfig( level=logging.getLevelName("INFO"), handlers=[logging.StreamHandler(sys.stdout)], format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", ) # load datasets train_dataset = load_from_disk(args.training_dir) test_dataset = load_from_disk(args.test_dir) logger.info(f" loaded train_dataset length is: {len(train_dataset)}") logger.info(f" loaded test_dataset length is: {len(test_dataset)}") # compute metrics function for binary classification def compute_metrics(pred): labels = pred.label_ids preds = pred.predictions.argmax(-1) precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average="binary") acc = accuracy_score(labels, preds) return {"accuracy": acc, "f1": f1, "precision": precision, "recall": recall} # download model from model hub model = AutoModelForSequenceClassification.from_pretrained(args.model_name) # define training args training_args = TrainingArguments( output_dir=args.model_dir, num_train_epochs=args.epochs, per_device_train_batch_size=args.train_batch_size, per_device_eval_batch_size=args.eval_batch_size, warmup_steps=args.warmup_steps, evaluation_strategy="epoch", logging_dir=f"{args.output_data_dir}/logs", learning_rate=float(args.learning_rate), ) # create Trainer instance trainer = Trainer( model=model, args=training_args, compute_metrics=compute_metrics, train_dataset=train_dataset, eval_dataset=test_dataset, ) # train model trainer.train() # evaluate model eval_result = trainer.evaluate(eval_dataset=test_dataset) # writes eval result to file which can be accessed later in s3 output with open(os.path.join(args.output_data_dir, "eval_results.txt"), "w") as writer: print(f"***** Eval results *****") for key, value in sorted(eval_result.items()): writer.write(f"{key} = {value}\\n") # Saves the model to s3; default is /opt/ml/model which SageMaker sends to S3 trainer.save_model(args.model_dir)
Preprocess our data and upload it to s3
We use the datasets library to download and preprocess our imdb dataset. After preprocessing, the dataset will be uploaded to the current session’s default s3 bucket sess.default_bucket() used within our training job. The imdb dataset consists of 25000 training and 25000 testing highly polar movie reviews.
import botocore from datasets import load_dataset from transformers import AutoTokenizer from datasets.filesystems import S3FileSystem # tokenizer used in preprocessing tokenizer_name = 'distilbert-base-uncased' # filesystem client for s3 s3 = S3FileSystem() # dataset used dataset_name = 'imdb' # s3 key prefix for the data s3_prefix = 'datasets/imdb' # load dataset dataset = load_dataset(dataset_name) # download tokenizer tokenizer = AutoTokenizer.from_pretrained(tokenizer_name) # tokenizer helper function def tokenize(batch): return tokenizer(batch['text'], padding='max_length', truncation=True) # load dataset train_dataset, test_dataset = load_dataset('imdb', split=['train', 'test']) test_dataset = test_dataset.shuffle().select(range(10000)) # smaller the size for test dataset to 10k # tokenize dataset train_dataset = train_dataset.map(tokenize, batched=True, batch_size=len(train_dataset)) test_dataset = test_dataset.map(tokenize, batched=True, batch_size=len(test_dataset)) # set format for pytorch train_dataset = train_dataset.rename_column("label", "labels") train_dataset.set_format('torch', columns=['input_ids', 'attention_mask', 'labels']) test_dataset = test_dataset.rename_column("label", "labels") test_dataset.set_format('torch', columns=['input_ids', 'attention_mask', 'labels']) # save train_dataset to s3 training_input_path = f's3://{sess.default_bucket()}/{s3_prefix}/train' train_dataset.save_to_disk(training_input_path,fs=s3) # save test_dataset to s3 test_input_path = f's3://{sess.default_bucket()}/{s3_prefix}/test' test_dataset.save_to_disk(test_input_path,fs=s3)
Create a HuggingFace Estimator and train our model
In order to create a SageMaker Trainingjob we can use a HuggingFace Estimator. The Estimator handles the end-to-end Amazon SageMaker training. In an Estimator, we define which fine-tuning script should be used as entry_point, which instance_type should be used, which hyperparameters are passed in. In addition to this, a number of advanced controls are available, such as customizing the output and checkpointing locations, specifying the local storage size or network configuration.
SageMaker takes care of starting and managing all the required Amazon EC2 instances for us with the Hugging Face DLC, it uploads the provided fine-tuning script, for example, our train.py, then downloads the data from the S3 bucket, sess.default_bucket(), into the container. Once the data is ready, the training job will start automatically by running.
/opt/conda/bin/python train.py --epochs 1 --model_name distilbert-base-uncased --train_batch_size 32
The hyperparameters you define in the HuggingFace Estimator are passed in as named arguments.
from sagemaker.huggingface import HuggingFace # hyperparameters, which are passed into the training job hyperparameters={'epochs': 1, 'train_batch_size': 32, 'model_name':'distilbert-base-uncased' } # create the Estimator huggingface_estimator = HuggingFace( entry_point='train.py', source_dir='./scripts', instance_type='ml.p3.2xlarge', instance_count=1, role=role, transformers_version='4.6', pytorch_version='1.7', py_version='py36', hyperparameters = hyperparameters )
To start our training we call the .fit() method and pass our S3 uri as input.
# starting the train job with our uploaded datasets as input huggingface_estimator.fit({'train': training_input_path, 'test': test_input_path})
Additional Features 🚀
In addition to the Deep Learning Container and the SageMaker SDK, we have implemented other additional features.
Distributed Training: Data-Parallel
You can use SageMaker Data Parallelism Library out of the box for distributed training. We added the functionality of Data Parallelism directly into the Trainer. If your train.py uses the Trainer API you only need to define the distribution parameter in the HuggingFace Estimator.
Example Notebook PyTorch
Example Notebook TensorFlow
# configuration for running training on smdistributed Data Parallel distribution = {'smdistributed':{'dataparallel':{ 'enabled': True }}} # create the Estimator huggingface_estimator = HuggingFace( entry_point='train.py', source_dir='./scripts', instance_type='ml.p3dn.24xlarge', instance_count=2, role=role, transformers_version='4.4.2', pytorch_version='1.6.0', py_version='py36', hyperparameters = hyperparameters distribution = distribution )
The "Getting started: End-to-End Text Classification 🧭" example can be used for distributed training without any changes.
Distributed Training: Model Parallel
You can use SageMaker Model Parallelism Library out of the box for distributed training. We added the functionality of Model Parallelism directly into the Trainer. If your train.py uses the Trainer API you only need to define the distribution parameter in the HuggingFace Estimator.
For detailed information about the adjustments take a look here.
Example Notebook
# configuration for running training on smdistributed Model Parallel mpi_options = { "enabled" : True, "processes_per_host" : 8 } smp_options = { "enabled":True, "parameters": { "microbatches": 4, "placement_strategy": "spread", "pipeline": "interleaved", "optimize": "speed", "partitions": 4, "ddp": True, } } distribution={ "smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options } # create the Estimator huggingface_estimator = HuggingFace( entry_point='train.py', source_dir='./scripts', instance_type='ml.p3dn.24xlarge', instance_count=2, role=role, transformers_version='4.4.2', pytorch_version='1.6.0', py_version='py36', hyperparameters = hyperparameters, distribution = distribution )
Spot instances
With the creation of HuggingFace Framework extension for the SageMaker Python SDK we can also leverage the benefit of fully-managed EC2 spot instances and save up to 90% of our training cost.
Note: Unless your training job will complete quickly, we recommend you use checkpointing with managed spot training, therefore you need to define the checkpoint_s3_uri.
To use spot instances with the HuggingFace Estimator we have to set the use_spot_instances parameter to True and define your max_wait and max_run time. You can read more about the managed spot training lifecycle here.
Example Notebook
# hyperparameters, which are passed into the training job hyperparameters={'epochs': 1, 'train_batch_size': 32, 'model_name':'distilbert-base-uncased', 'output_dir':'/opt/ml/checkpoints' } # create the Estimator huggingface_estimator = HuggingFace( entry_point='train.py', source_dir='./scripts', instance_type='ml.p3.2xlarge', instance_count=1, checkpoint_s3_uri=f's3://{sess.default_bucket()}/checkpoints' use_spot_instances=True, max_wait=3600, # This should be equal to or greater than max_run in seconds' max_run=1000, role=role, transformers_version='4.4', pytorch_version='1.6', py_version='py36', hyperparameters = hyperparameters ) # Training seconds: 874 # Billable seconds: 105 # Managed Spot Training savings: 88.0%
Git Repositories
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. If Git support is enabled, then entry_point and source_dir should be relative paths in the Git repo if provided.
As an example to use git_config with an example script from the transformers repository.
Be aware that you need to define output_dir as a hyperparameter for the script to save your model to S3 after training. Suggestion: define output_dir as /opt/ml/model since it is the default SM_MODEL_DIR and will be uploaded to S3.
Example Notebook
# configure git settings git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'master'} # create the Estimator huggingface_estimator = HuggingFace( entry_point='run_glue.py', source_dir='./examples/text-classification', git_config=git_config, instance_type='ml.p3.2xlarge', instance_count=1, role=role, transformers_version='4.4', pytorch_version='1.6', py_version='py36', hyperparameters=hyperparameters )
SageMaker Metrics
SageMaker Metrics can automatically parse the logs for metrics and send those metrics to CloudWatch. If you want SageMaker to parse logs you have to specify the metrics that you want SageMaker to send to CloudWatch when you configure the training job. You specify the name of the metrics that you want to send and the regular expressions that SageMaker uses to parse the logs that your algorithm emits to find those metrics.
Example Notebook
# define metrics definitions metric_definitions = [ {"Name": "train_runtime", "Regex": "train_runtime.*=\D*(.*?)$"}, {"Name": "eval_accuracy", "Regex": "eval_accuracy.*=\D*(.*?)$"}, {"Name": "eval_loss", "Regex": "eval_loss.*=\D*(.*?)$"}, ] # create the Estimator huggingface_estimator = HuggingFace( entry_point='train.py', source_dir='./scripts', instance_type='ml.p3.2xlarge', instance_count=1, role=role, transformers_version='4.4', pytorch_version='1.6', py_version='py36', metric_definitions=metric_definitions, hyperparameters = hyperparameters )
FAQ 🎯
You can find the complete Frequently Asked Questions in the documentation.
Q: What are Deep Learning Containers?
A: Deep Learning Containers (DLCs) are Docker images pre-installed with deep learning frameworks and libraries (e.g. transformers, datasets, tokenizers) to make it easy to train models by letting you skip the complicated process of building and optimizing your environments from scratch.
Q: Do I have to use the SageMaker Python SDK to use the Hugging Face Deep Learning Containers?
A: You can use the HF DLC without the SageMaker Python SDK and launch SageMaker Training jobs with other SDKs, such as the AWS CLI or boto3. The DLCs are also available through Amazon ECR and can be pulled and used in any environment of choice.
Q: Why should I use the Hugging Face Deep Learning Containers?
A: The DLCs are fully tested, maintained, optimized deep learning environments that require no installation, configuration, or maintenance.
Q: Why should I use SageMaker Training to train Hugging Face models?
A: SageMaker Training provides numerous benefits that will boost your productivity with Hugging Face : (1) first it is cost-effective: the training instances live only for the duration of your job and are paid per second. No risk anymore to leave GPU instances up all night: the training cluster stops right at the end of your job! It also supports EC2 Spot capacity, which enables up to 90% cost reduction. (2) SageMaker also comes with a lot of built-in automation that facilitates teamwork and MLOps: training metadata and logs are automatically persisted to a serverless managed metastore, and I/O with S3 (for datasets, checkpoints and model artifacts) is fully managed. Finally, SageMaker also allows to drastically scale up and out: you can launch multiple training jobs in parallel, but also launch large-scale distributed training jobs
Q: Once I've trained my model with Amazon SageMaker, can I use it with 🤗/Transformers ?
A: Yes, you can download your trained model from S3 and directly use it with transformers or upload it to the Hugging Face Model Hub.
Q: How is my data and code secured by Amazon SageMaker?
A: Amazon SageMaker provides numerous security mechanisms including encryption at rest and in transit, Virtual Private Cloud (VPC) connectivity and Identity and Access Management (IAM). To learn more about security in the AWS cloud and with Amazon SageMaker, you can visit Security in Amazon SageMaker and AWS Cloud Security.
Q: Is this available in my region?
A: For a list of the supported regions, please visit the AWS region table for all AWS global infrastructure.
Q: Do I need to pay for a license from Hugging Face to use the DLCs?
A: No - the Hugging Face DLCs are open source and licensed under Apache 2.0.
Q: How can I run inference on my trained models?
A: You have multiple options to run inference on your trained models. One option is to use Hugging Face Accelerated Inference-API hosted service: start by uploading the trained models to your Hugging Face account to deploy them publicly, or privately. Another great option is to use SageMaker Inference to run your own inference code in Amazon SageMaker. We are working on offering an integrated solution for Amazon SageMaker with Hugging Face Inference DLCs in the future - stay tuned!
Q: Do you offer premium support or support SLAs for this solution?
A: AWS Technical Support tiers are available from AWS and cover development and production issues for AWS products and services - please refer to AWS Support for specifics and scope.
If you have questions which the Hugging Face community can help answer and/or benefit from, please post them in the Hugging Face forum.
If you need premium support from the Hugging Face team to accelerate your NLP roadmap, our Expert Acceleration Program offers direct guidance from our open source, science and ML Engineering team - contact us to learn more.
Q: What are you planning next through this partnership?
A: Our common goal is to democratize state of the art Machine Learning. We will continue to innovate to make it easier for researchers, data scientists and ML practitioners to manage, train and run state of the art models. If you have feature requests for integration in AWS with Hugging Face, please let us know in the Hugging Face community forum.
Q: I use Hugging Face with Azure Machine Learning or Google Cloud Platform, what does this partnership mean for me?
A: A foundational goal for Hugging Face is to make the latest AI accessible to as many people as possible, whichever framework or development environment they work in. While we are focusing integration efforts with Amazon Web Services as our Preferred Cloud Provider, we will continue to work hard to serve all Hugging Face users and customers, no matter what compute environment they run on. |
https://huggingface.co/johnyi | John
johnyi
jyidiego
jyidiego
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https://huggingface.co/mkamp | Mariano Kamp
mkamp
mkamp
marianokamp
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mkamp/distilbert-base-uncased-finetuned-emotion
Updated Jan 22, 2022
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https://huggingface.co/portisto | 2
Luka Riester
portisto
portisto
portisto
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https://huggingface.co/facebook | Research interests
None defined yet.
Collections 1
SeamlessM4T
SeamlessM4T is designed to provide high quality translation, allowing people from different linguistic communities to communicate effortlessly.
Running ona100
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facebook/seamless-m4t-large
Updated 18 days ago • 427
facebook/seamless-m4t-medium
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facebook/seamless-m4t-unity-small
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facebook/bart-large-cnn
Summarization • Updated about 9 hours ago • 1.8M • 596
facebook/hf-seamless-m4t-medium
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facebook/hf-seamless-m4t-large
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facebook/mbart-large-50-many-to-many-mmt
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facebook/convnextv2-nano-22k-224
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facebook/convnextv2-nano-1k-224
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facebook/belebele
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https://huggingface.co/shulindt | 1
Lei Shu
shulindt
https://leishu02.github.io/
leishu02
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Language Model, NLP, Multi-modal Pretrained Model
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https://huggingface.co/sunbc0120 | Baichuan Sun
sunbc0120
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Generative AI, Computer Vision, LLMs/NLP, Reinforcement Learning
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https://huggingface.co/rafidka | Rafid Al-Humaimidi
rafidka
Research interests
Natural Language Processing, Computer Vision
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https://huggingface.co/rppth | Raj Pathak
rppth
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Named Entity Recognition Document Classification Text Generation
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https://huggingface.co/5cp | Scott Perry
5cp
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5cp/whisper-small-hi
Updated Dec 7, 2022
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https://huggingface.co/tomofi | Tomofumi Inoue
tomofi
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TensorFlow Certified Developer
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spaces 13
models 1 |
https://huggingface.co/Sa-m | 1 15
samarth
Sa-m
samarth70
Research interests
Deep Learning, NLP
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spaces 11
pinned
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Manifesto Explainer
Building
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Flask App Trial
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Political Party Symbol Detector V1
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Vehicles Detection Custom YoloV7
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Emotions Analyser
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Sa-m/cropsVSweed
Viewer • Updated Jul 12, 2022 • 1 |
https://huggingface.co/tapas | Tapas Agarwal
tapas
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Deep Learning | Computer Vision | NLP | Multimodal Learning
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https://huggingface.co/pronoyc | Pronoy Chopra
pronoyc
https://pronoy.in
DarkSector
Research interests
IoT + AI/ML
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https://huggingface.co/sermolin | 2
Sergey Ermolin
sermolin
sermolin
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ML practice on AWS and other clouds
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