# entries

id: 5e787d5a923543d380051c20dd9c626b
author: anna nymous
created_at: 2023-05-23 12:14:38
source:
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.

source_snippet:
Diffusers is the go-to library for state-of-the-art pretrained diff...erformance, simple over easy, and customizability over abstractions.


id: ac503d2a8dde4e1daf80d2c15732c183
author: anna nymous
created_at: 2023-05-23 12:14:56
source:
https://images.openai.com/blob/8a2b0833-55f2-44d6-bf4f-85f9471078f5/Anastronautridingahorseinaphotorealisticstyle6.jpg

source_snippet:
https://images.openai.com/blob/8a2b0833-55f2-44d6-bf4f-85f9471078f5/Anastronautridingahorseinaphotorealisticstyle6.jpg


# summaries

entry_id: 5e787d5a923543d380051c20dd9c626b
summarizer_name: hf_default
created_at: 2023-05-23 12:14:57
summary:
Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio and even 3D structures of molecules. The library is designed with a focus on usability over performance, simple over easy and customizability over abstractions.


entry_id: d3fe0d479078447ea7477d09e6f9fb0d
summarizer_name: hf_default
created_at: 2023-05-23 12:15:16
summary:
Hugging Face, Inc. was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. The company developed a chatbot app for teenagers. After open-sourcing the model behind the chatbot, the company pivoted to being a platform for machine learning. It is most notable for its transformers library built for natural language processing applications and its platform that allows users to share machine learning models and datasets. In March 2021, Hugging Face raised $40 million in a Series B funding round. On May 5, 2022 the company announced its Series C funding round led by Coatue and Sequoia and received a $


entry_id: ac503d2a8dde4e1daf80d2c15732c183
summarizer_name: hf_default
created_at: 2023-05-23 12:15:29
summary:
A man riding a white horse in the desert is seen in a photo.    in the photo is a man riding his white horse on the desert.  In the photo, the horse is riding on a white man on the white man.  in this photo the man is riding a horse.


entry_id: ce36911b395c45f5a828b65ec372e382
summarizer_name: hf_default
created_at: 2023-05-23 12:16:08
summary:
In this series of videos, we'll try to understand what makes a transformer network and explain it in simple, high-level terms. We'll study the transformer architecture in terms of the encoders, decoders and encoder-decoders.


# inputs

entry_id: d3fe0d479078447ea7477d09e6f9fb0d
created_at: 2023-05-23 12:15:16
input:
https://en.wikipedia.org/wiki/Hugging_Face

Hugging Face
This article relies excessively on references to primary sources. (February 2023)
|Type||Private|
|Industry||Artificial intelligence, machine learning, software development|
|Founded||2016New York Cityin|
|Headquarters|
New York City,
U.S.
Area served
|Worldwide|
Key people
|Products||Transformers, datasets, spaces|
|Website||huggingface|
Hugging Face, Inc. is an American company that develops tools for building applications using machine learning.[1] It is most notable for its transformers library built for natural language processing applications and its platform that allows users to share machine learning models and datasets.
History[edit]
The company was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.
In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]
On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
On December 21, 2021, the company announced its acquisition of Gradio, a software library used to make interactive browser demos of machine learning models.[6]
On May 5, 2022, the company announced its Series C funding round led by Coatue and Sequoia.[7] The company received a $2 billion valuation.
On May 13, 2022, the company introduced its Student Ambassador Program to help fulfill its mission to teach machine learning to 5 million people by 2023.[8]
On May 26, 2022, the company announced a partnership with Graphcore to optimize its Transformers library for the Graphcore IPU.[9]
On August 3, 2022, the company announced the Private Hub, an enterprise version of its public Hugging Face Hub that supports SaaS or on-premise deployment.[10]
In February 2023, the company announced partnership with Amazon Web Services (AWS) which would allow Hugging Face's products available to AWS customers to use them as the building blocks for their custom applications. The company also said the next generation of BLOOM will be run on Trainium, a proprietary machine learning chip created by AWS.[11][12]
Services and technologies[edit]
Transformers Library[edit]
The Transformers library is a Python package that contains open-source implementations of transformer models for text, image, and audio tasks. It is compatible with the PyTorch, TensorFlow and JAX deep learning libraries and includes implementations of notable models like BERT and GPT.[13] The library was originally called "pytorch-pretrained-bert"[14] which was then renamed to "pytorch-transformers" and finally "transformers."
Hugging Face Hub[edit]
The Hugging Face Hub is a platform (centralized web service) for hosting:[15]
- Git-based code repositories, with features similar to GitHub, including discussions and pull requests for projects.
- models, also with Git-based version control;
- datasets, mainly in text, images, and audio;
- web applications ("spaces" and "widgets"), intended for small-scale demos of machine learning applications.
Other Libraries[edit]
In addition to Transformers and the Hugging Face Hub, the Hugging Face ecosystem contains libraries for other tasks, such as dataset processing ("Datasets"), model evaluation ("Evaluate"), simulation ("Simulate"), machine learning demos ("Gradio").[16]
References[edit]
- ^ "Hugging Face – The AI community building the future". huggingface.co. Retrieved 2022-08-20.
- ^ "Hugging Face wants to become your artificial BFF". TechCrunch. 9 March 2017. Retrieved 2022-08-20.
- ^ "Hugging Face raises $40 million for its natural language processing library". 11 March 2021.
- ^ "Inside BigScience, the quest to build a powerful open language model". 10 January 2022.
- ^ "BLOOM". bigscience.huggingface.co. Retrieved 2022-08-20.
- ^ "Gradio is joining Hugging Face!". huggingface.co. Retrieved 2022-08-20.
- ^ Cai, Kenrick. "The $2 Billion Emoji: Hugging Face Wants To Be Launchpad For A Machine Learning Revolution". Forbes. Retrieved 2022-08-20.
- ^ "Student Ambassador Program's call for applications is open!". huggingface.co. Retrieved 2022-08-20.
- ^ "Graphcore and Hugging Face Launch New Lineup of IPU-Ready Transformers". huggingface.co. Retrieved 2022-08-19.
- ^ "Introducing the Private Hub: A New Way to Build With Machine Learning". huggingface.co. Retrieved 2022-08-20.
- ^ Bass, Dina (2023-02-21). "Amazon's Cloud Unit Partners With Startup Hugging Face as AI Deals Heat Up". Bloomberg News.
- ^ Nellis, Stephen (2023-02-21). "Amazon Web Services pairs with Hugging Face to target AI developers". Reuters.
- ^ "🤗 Transformers". huggingface.co. Retrieved 2022-08-20.
- ^ "First release". Github. Nov 17, 2018. Retrieved 28 March 2023.
- ^ "Hugging Face Hub documentation". huggingface.co. Retrieved 2022-08-20.
- ^ "Hugging Face - Documentation". huggingface.co. Retrieved 2023-02-18.


entry_id: ce36911b395c45f5a828b65ec372e382
created_at: 2023-05-23 12:16:08
input:
https://www.youtube.com/watch?v=H39Z_720T5s

 Let's study the transformer architecture. This video is the introductory video to the encoders, decoders and encoder-decoder series of videos. In the series we'll try to understand what makes a transformer network and we'll try to explain it in simple, high-level terms. No advanced understanding of neural networks is necessary, but an understanding of basic vectors and tensors may help.  To get started, we'll take up this diagram from the original transformer paper entitled, Attention is All You Need by Pazuanoyet A. As we'll see here, we can leverage only some parts of it according to what we're trying to do. We want to dive into the specific layers building up that architecture, but we'll try to understand the different ways this architecture can be used. Let's first start by splitting that architecture into two parts. On the left, we have the encoder.  and at the right, the decoder. These two can be used together, but they can also be used independently. Let's understand how these work. The encoder accepts inputs that represent text. It converts these texts, these words, into numerical representations. These numerical representations can also be called embeddings or features. We'll see that it uses the self-attention mechanism as its main component. And we recommend you check out the video on encoders specifically to understand  this numerical presentation as well as how it works. We'll study the self-attention mechanism in more detail as well as its bidirectional properties. The decoder is similar to the encoder. It can also accept text inputs. It uses a similar mechanism as the encoder, which is the masked self-attention as well. It differs from the encoder due to its unidirectional feature and is traditionally used in an autoregressive manner. Here too, we recommend  and you check out the video on decoders especially to understand how all of this works. Combining the two parts results in what is known as an encoder-decoder or a sequence-to-sequence transformer. The encoder accepts inputs and computes a high-level representation of those inputs. These outputs are then passed to the decoder. The decoder uses the encoder's outputs alongside other inputs to generate a prediction. It then predicts an output, which is what we use in future iterations.  hence the term autoregressive. Finally, to get an understanding of encoder-decoders as a whole, we recommend you check out the video on encoder-decoders.


