Difference between revisions of "Hugging Face"

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|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS  
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|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
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|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools  
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[https://www.youtube.com/results?search_query=ai+Hugging+Face YouTube]
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[https://www.quora.com/search?q=ai%20Hugging%20Face...X ... Quora]
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[https://www.google.com/search?q=ai+Hugging+Face ...Google search]
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[https://news.google.com/search?q=ai+Hugging+Face ...Google News]
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[https://www.bing.com/news/search?q=ai+Hugging+Face...X&qft=interval%3d%228%22 ...Bing News]
  
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[https://www.youtube.com/results?search_query=Hugging+Face+deep+machine+learning+ML+artificial+intelligence YouTube search...]
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* [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless]] ... [[Hugging Face]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]
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[https://www.google.com/search?q=Hugging+Face+deep+machine+learning+ML+artificial+intelligence ...Google search]
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* [https://huggingface.co/ Hugging Face] ... The AI community building the future
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* [https://huggingface.co/models Models | Hugging Face] ... click on Sort: Trending
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* [[Embedding]] ... [[Fine-tuning]] ... [[Retrieval-Augmented Generation (RAG)|RAG]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]
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* [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)]]
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* [https://www.youtube.com/watch?v=00GKzGyWFEs&list=PLo2EIpI_JMQvWfQndUesu0nPBAtZ9gP1o Hugging Face course]
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* [https://bytexd.com/what-is-hugging-face-beginners-guide What is Hugging Face - A Beginner's Guide | ByteXD] ... allows users to share machine learning models and datasets
  
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* [https://huggingface.co/ Hugging Face]
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Hugging Face is an American company that develops tools for building applications using 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. Hugging Face is a community and a platform for artificial intelligence and data science that aims to democratize AI knowledge and assets used in AI models. The platform allows users to build, train and deploy state of the art models powered by open source machine learning. It also provides a place where a broad community of data scientists, researchers, and ML engineers can come together and share ideas, get support and contribute to open source projects. Is there anything else you would like to know? - [https://en.wikipedia.org/wiki/Hugging_Face Wikipedia]
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* [https://www.youtube.com/watch?v=00GKzGyWFEs&list=PLo2EIpI_JMQvWfQndUesu0nPBAtZ9gP1o Hugging Face course]
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* [[Reinforcement Learning (RL) from Human Feedback (RLHF)]]
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<youtube>agwbNgxwkHc</youtube>
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<youtube>QEaBAZQCtwE</youtube>
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= <span id="Hugging Face's Research"></span>Hugging Face's Research =
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<youtube>eqOSQeQNqaw</youtube>
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= Hugging Face Community =
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* [https://towardsdatascience.com/whats-hugging-face-122f4e7eb11a What's Hugging Face? An AI community for sharing ML models and datasets]
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* [[Agents#HuggingGPT|HuggingGPT]]  ... in partnership with [[Microsoft]]
 
* [https://www.topbots.com/pretrain-transformers-models-in-pytorch/ Pretrain Transformers Models in PyTorch Using Hugging Face Transformers | George Mihaila - TOPBOTS]
 
* [https://www.topbots.com/pretrain-transformers-models-in-pytorch/ Pretrain Transformers Models in PyTorch Using Hugging Face Transformers | George Mihaila - TOPBOTS]
 
* [https://www.together.xyz/blog/openchatkit OpenChatKit | TogetherCompute] ... The first open-source [[ChatGPT]] alternative released; a 20B chat-GPT model under the Apache-2.0 license, which is available for free on Hugging Face.
 
* [https://www.together.xyz/blog/openchatkit OpenChatKit | TogetherCompute] ... The first open-source [[ChatGPT]] alternative released; a 20B chat-GPT model under the Apache-2.0 license, which is available for free on Hugging Face.
 
** [https://laion.ai/ LAION]
 
** [https://laion.ai/ LAION]
 
** [https://huggingface.co/ontocord Ontocord]
 
** [https://huggingface.co/ontocord Ontocord]
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** [[Wolfram]] ChatGPT
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** [[FLAN-T5 LLM]]
  
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Their platform is home to a large community of developers and researchers who work together to solve problems in audio, vision, and language with AI.
  
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<youtube>eqOSQeQNqaw</youtube>
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== <span id="Hugging Face's Open-source Library"></span>Hugging Face's Open-source Library ==
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<youtube>agwbNgxwkHc</youtube>
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Hugging Face's open-source library, Transformers, is widely used for [[Natural Language Processing (NLP)]]  tasks. The company also offers an Inference API that allows developers to serve their models directly from Hugging Face infrastructure and run large scale [[[[Natural Language Processing (NLP)|NLP]] models in milliseconds with just a few lines of code. Hugging Face offers a wide range of machine learning models and datasets, as well as tools for building, training, and deploying state-of-the-art models.
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<youtube>QEaBAZQCtwE</youtube>
 
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<youtube>8xYYvO7LGBw</youtube>
 
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= <span id="HuggingGPT"></span>HuggingGPT =
 
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* [[Assistants]] ... [[Hybrid Assistants]]  ... [[Agents]]  ... [[Negotiation]] ... [[Hugging_Face#HuggingGPT|HuggingGPT]] ... [[LangChain]]
 
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* [https://www.marktechpost.com/2023/04/07/meet-hugginggpt-a-framework-that-leverages-llms-to-connect-various-ai-models-in-machine-learning-communities-hugging-face-to-solve-ai-tasks/ HuggingGPT] ... A Framework That Leverages LLMs to Connect Various AI Models in Machine Learning Communities ([[Hugging Face]]) to Solve AI Tasks
 
  
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<img src="https://miro.medium.com/v2/resize:fit:828/format:webp/1*EcGhNOz8cxE2rueYU_JE_w.png" width="1000">
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== <span id="Private Hub"></span>Private Hub ==
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* [https://huggingface.co/platform Private Hub]
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** [https://huggingface.co/docs/hub/main Hugging Face Hub documentation]
  
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<youtube>PfY9lVtM_H0</youtube>
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== <span id="LightGPT"></span>LightGPT ==
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<youtube>Rog9oHtVmjM</youtube>
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* [https://huggingface.co/amazon/LightGPT amazon/LightGPT]
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* [https://huggingface.co/amazon/LightGPT/blob/main/README.md  README.md · amazon/LightGPT]
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* [https://huggingface.co/EleutherAI/gpt-j-6b EleutherAI/gpt-j-6b] 
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* [https://en.wikipedia.org/wiki/GPT-J  GPT-J | Wikipedia]
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* [https://huggingface.co/blog/gptj-sagemaker  Deploy GPT-J 6B for inference using Hugging Face Transformers]
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* [https://betterprogramming.pub/fine-tuning-gpt-j-6b-on-google-colab-or-equivalent-desktop-or-server-gpu-b6dc849cb205 Fine-tuning GPT-J 6B on Google Colab or Equivalent Desktop or Server]
  
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= Hugging Face NLP Library - Open Parallel Corpus (OPUS) =
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LightGPT is a language model developed by AWS Contributors. It is based on GPT-J 6B and was instruction fine-tuned on the high-quality, Apache-2.0 licensed OIG-small-chip instruction dataset with ~200K training examples. The model is designed to generate text based on a given instruction, and it can be deployed to [[Amazon]] [[SageMaker]]
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* [https://venturebeat.com/2020/05/16/hugging-face-dives-into-machine-translation-with-release-of-1000-models/ [[Hugging Face]] dives into machine translation with release of 1,000 models | Khari Johnson - VentureBeat]
 
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* [https://towardsdatascience.com/build-your-own-machine-translation-service-with-transformers-d0709df0791b Build Your Own Machine Translation Service with Transformers Using the latest Helsinki NLP models available in the Transformers library to create a standardized machine translation service | Kyle Gallatin - Towards Data Science]
 
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[[Hugging Face]]
 
  
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OPUS is a growing collection of translated texts from the web. In the OPUS project we try to convert and align free online data, to add linguistic annotation, and to provide the community with a publicly available parallel corpus. OPUS is a project undertaken by the University of Helsinki and global partners to gather and open-source a wide variety of language data sets.  
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GPT-J 6B is a transformer model trained using Ben Wang's Mesh Transformer JAX. "GPT-J" refers to the class of model, while "6B" represents the number of trainable parameters. The model consists of 28 layers with a model dimension of 4096, and a feedforward dimension of 16384. The model dimension is split into 16 heads, each with a dimension of 256. Rotary Position [[Embedding]] (RoPE) is applied to 64 dimensions of each head. The model is trained with a tokenization vocabulary of 50257, using the same set of BPEs as GPT-2/GPT-3. GPT-J learns an inner representation of the English language that can be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating text from a prompt. GPT-J-6B is not intended for deployment without [[fine-tuning]], supervision, and/or moderation. It is not a product in itself and cannot be used for human-facing interactions. For example, the model may generate harmful or offensive text. Please evaluate the risks associated with your particular use case.
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OPUS is based on open source products and the corpus is also delivered as an open content package. We used several tools to compile the current collection. All pre-processing is done automatically. No manual corrections have been carried out. The OPUS collection is growing!
 
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[https://opus.nlpl.eu/ ... OPUS the open parallel corpus]
 
  
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<youtube>G3pOvrKkFuk</youtube>
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== <span id="Whisper"></span>Whisper ==
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<youtube>G5lmya6eKtc</youtube>
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<youtube>8xYYvO7LGBw</youtube>

Latest revision as of 20:15, 26 April 2024

YouTube ... Quora ...Google search ...Google News ...Bing News

Hugging Face is an American company that develops tools for building applications using 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. Hugging Face is a community and a platform for artificial intelligence and data science that aims to democratize AI knowledge and assets used in AI models. The platform allows users to build, train and deploy state of the art models powered by open source machine learning. It also provides a place where a broad community of data scientists, researchers, and ML engineers can come together and share ideas, get support and contribute to open source projects. Is there anything else you would like to know? - Wikipedia

Hugging Face's Research


Hugging Face Community

Their platform is home to a large community of developers and researchers who work together to solve problems in audio, vision, and language with AI.

Hugging Face's Open-source Library

Hugging Face's open-source library, Transformers, is widely used for Natural Language Processing (NLP) tasks. The company also offers an Inference API that allows developers to serve their models directly from Hugging Face infrastructure and run large scale [[NLP models in milliseconds with just a few lines of code. Hugging Face offers a wide range of machine learning models and datasets, as well as tools for building, training, and deploying state-of-the-art models.

Private Hub

LightGPT

LightGPT is a language model developed by AWS Contributors. It is based on GPT-J 6B and was instruction fine-tuned on the high-quality, Apache-2.0 licensed OIG-small-chip instruction dataset with ~200K training examples. The model is designed to generate text based on a given instruction, and it can be deployed to Amazon SageMaker

GPT-J 6B is a transformer model trained using Ben Wang's Mesh Transformer JAX. "GPT-J" refers to the class of model, while "6B" represents the number of trainable parameters. The model consists of 28 layers with a model dimension of 4096, and a feedforward dimension of 16384. The model dimension is split into 16 heads, each with a dimension of 256. Rotary Position Embedding (RoPE) is applied to 64 dimensions of each head. The model is trained with a tokenization vocabulary of 50257, using the same set of BPEs as GPT-2/GPT-3. GPT-J learns an inner representation of the English language that can be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating text from a prompt. GPT-J-6B is not intended for deployment without fine-tuning, supervision, and/or moderation. It is not a product in itself and cannot be used for human-facing interactions. For example, the model may generate harmful or offensive text. Please evaluate the risks associated with your particular use case.

Whisper

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