| import gradio as gr |
|
|
| gr.Markdown(""" |
| # Big Science Bloom is a 176B Parameter Large Language ML Model. |
| https://www.youtube.com/watch?v=wA8rjKueB3Q |
| https://www.youtube.com/watch?v=2MBJOuVq380&t=241s |
| # Big Science Papers and Code - Exciting AI Developments! 🤖💻🔬 |
| https://paperswithcode.com/paper/bloom-a-176b-parameter-open-access |
| """) |
|
|
| api = gr.Interface.load("models/bigscience/bloom") |
|
|
| def complete_with_gpt(text): |
| |
| |
| |
| return text[:-100] + api(text[-100:]) |
|
|
|
|
| with gr.Blocks() as demo: |
| with gr.Row(): |
| textbox = gr.Textbox(placeholder="Type here and press enter...", lines=14) |
| with gr.Column(): |
| btn = gr.Button("Generate") |
|
|
| btn.click(complete_with_gpt, textbox, textbox) |
|
|
| with gr.Row(): |
| gr.Markdown(""" |
| |
| # Example on how to prompt. |
| |
| Create a pattern sequence of text. In this example I use language names then click generate to add each line after adding another heading for a language. |
| |
| English: Hi my name is Aaron. I am a computer scientist and senior principal engineer. |
| Japanese: 私はアランです。コンピューター科学者とプログラ |
| English: Hi my name is Aaron. I am a computer scientist and senior principal engineer. |
| Chinese: 你好，我叫Aaron。我是一个计算机科学家和高级首席工程师。 |
| English: Hi my name is Aaron. I am a computer scientist and senior principal engineer. |
| Spanish: Hola, me llamo Aaron. Soy un cientifico de la computacion y un ingeniero principal |
| English: Hi my name is Aaron. I am a computer scientist and senior principal engineer. |
| Sanskrit: नमस्ते, मेरा नाम है Aaron. मैं एक कंप्यूटर वैज्ञानिक और वरिष्ठ प्रमुख इंजीनियर हूँ। |
| French: Bonjour, je m'appelle Aaron. Je suis un scientifique en informatique et un ingénieur senior. |
| |
| |
| ## Language Models 🗣️ |
| |
| 🏆 Bloom sets new record for most performant and efficient AI model in science! 🌸 |
| |
| ### Comparison of Large Language Models |
| |
| | Model Name | Model Size (in Parameters) | |
| | ----------------- | -------------------------- | |
| | BigScience-tr11-176B | 176 billion | |
| | GPT-3 | 175 billion | |
| | OpenAI's DALL-E 2.0 | 500 million | |
| | NVIDIA's Megatron | 8.3 billion | |
| | Transformer-XL | 250 million | |
| | XLNet | 210 million | |
| |
| ## ChatGPT Datasets 📚 |
| |
| - WebText |
| - Common Crawl |
| - BooksCorpus |
| - English Wikipedia |
| - Toronto Books Corpus |
| - OpenWebText |
| |
| ## ChatGPT Datasets - Details 📚 |
| |
| - **WebText:** A dataset of web pages crawled from domains on the Alexa top 5,000 list. This dataset was used to pretrain GPT-2. |
| - [WebText: A Large-Scale Unsupervised Text Corpus by Radford et al.](https://paperswithcode.com/dataset/webtext) |
| |
| - **Common Crawl:** A dataset of web pages from a variety of domains, which is updated regularly. This dataset was used to pretrain GPT-3. |
| - [Language Models are Few-Shot Learners](https://paperswithcode.com/dataset/common-crawl) by Brown et al. |
| |
| - **BooksCorpus:** A dataset of over 11,000 books from a variety of genres. |
| - [Scalable Methods for 8 Billion Token Language Modeling](https://paperswithcode.com/dataset/bookcorpus) by Zhu et al. |
| |
| - **English Wikipedia:** A dump of the English-language Wikipedia as of 2018, with articles from 2001-2017. |
| - [Improving Language Understanding by Generative Pre-Training](https://huggingface.co/spaces/awacke1/WikipediaUltimateAISearch?logs=build) Space for Wikipedia Search |
| |
| - **Toronto Books Corpus:** A dataset of over 7,000 books from a variety of genres, collected by the University of Toronto. |
| - [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://paperswithcode.com/dataset/bookcorpus) by Schwenk and Douze. |
| |
| - **OpenWebText:** A dataset of web pages that were filtered to remove content that was likely to be low-quality or spammy. This dataset was used to pretrain GPT-3. |
| - [Language Models are Few-Shot Learners](https://paperswithcode.com/dataset/openwebtext) by Brown et al. |
| |
| |
| ## Big Science Model 🚀 |
| |
| - 📜 Papers: |
| 1. BLOOM: A 176B-Parameter Open-Access Multilingual Language Model [Paper](https://arxiv.org/abs/2211.05100) |
| 2. Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism [Paper](https://arxiv.org/abs/1909.08053) |
| 3. 8-bit Optimizers via Block-wise Quantization [Paper](https://arxiv.org/abs/2110.02861) |
| 4. Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation [Paper](https://arxiv.org/abs/2108.12409) |
| 5. [Other papers related to Big Science](https://huggingface.co/models?other=doi:10.57967/hf/0003) |
| 6. [217 other models optimized for use with Bloom](https://huggingface.co/models?other=bloom) |
| |
| - 📚 Datasets: |
| |
| **Datasets:** |
| |
| 1. - **Universal Dependencies:** A collection of annotated corpora for natural language processing in a range of languages, with a focus on dependency parsing. |
| - [Universal Dependencies official website.](https://universaldependencies.org/) |
| 2. - **WMT 2014:** The fourth edition of the Workshop on Statistical Machine Translation, featuring shared tasks on translating between English and various other languages. |
| - [WMT14 website.](http://www.statmt.org/wmt14/) |
| 3. - **The Pile:** An English language corpus of diverse text, sourced from various places on the internet. |
| - [The Pile official website.](https://pile.eleuther.ai/) |
| 4. - **HumanEval:** A dataset of English sentences, annotated with human judgments on a range of linguistic qualities. |
| - [HumanEval: An Evaluation Benchmark for Language Understanding](https://github.com/google-research-datasets/humaneval) by Gabriel Ilharco, Daniel Loureiro, Pedro Rodriguez, and Afonso Mendes. |
| 5. - **FLORES-101:** A dataset of parallel sentences in 101 languages, designed for multilingual machine translation. |
| - [FLORES-101: A Massively Multilingual Parallel Corpus for Language Understanding](https://flores101.opennmt.net/) by Aman Madaan, Shruti Rijhwani, Raghav Gupta, and Mitesh M. Khapra. |
| 6. - **CrowS-Pairs:** A dataset of sentence pairs, designed for evaluating the plausibility of generated text. |
| - [CrowS-Pairs: A Challenge Dataset for Plausible Plausibility Judgments](https://github.com/stanford-cogsci/crows-pairs) by Andrea Madotto, Zhaojiang Lin, Chien-Sheng Wu, Pascale Fung, and Caiming Xiong. |
| 7. - **WikiLingua:** A dataset of parallel sentences in 75 languages, sourced from Wikipedia. |
| - [WikiLingua: A New Benchmark Dataset for Cross-Lingual Wikification](https://arxiv.org/abs/2105.08031) by Jiarui Yao, Yanqiao Zhu, Ruihan Bao, Guosheng Lin, Lidong Bing, and Bei Shi. |
| 8. - **MTEB:** A dataset of English sentences, annotated with their entailment relationships with respect to other sentences. |
| - [Multi-Task Evaluation Benchmark for Natural Language Inference](https://github.com/google-research-datasets/mteb) by Michał Lukasik, Marcin Junczys-Dowmunt, and Houda Bouamor. |
| 9. - **xP3:** A dataset of English sentences, annotated with their paraphrase relationships with respect to other sentences. |
| - [xP3: A Large-Scale Evaluation Benchmark for Paraphrase Identification in Context](https://github.com/nyu-dl/xp3) by Aniket Didolkar, James Mayfield, Markus Saers, and Jason Baldridge. |
| 10. - **DiaBLa:** A dataset of English dialogue, annotated with dialogue acts. |
| - [A Large-Scale Corpus for Conversation Disentanglement](https://github.com/HLTCHKUST/DiaBLA) by Samuel Broscheit, António Branco, and André F. T. Martins. |
| |
| |
| - 📚 Dataset Papers with Code |
| |
| 1. [Universal Dependencies](https://paperswithcode.com/dataset/universal-dependencies) |
| 2. [WMT 2014](https://paperswithcode.com/dataset/wmt-2014) |
| 3. [The Pile](https://paperswithcode.com/dataset/the-pile) |
| 4. [HumanEval](https://paperswithcode.com/dataset/humaneval) |
| 5. [FLORES-101](https://paperswithcode.com/dataset/flores-101) |
| 6. [CrowS-Pairs](https://paperswithcode.com/dataset/crows-pairs) |
| 7. [WikiLingua](https://paperswithcode.com/dataset/wikilingua) |
| 8. [MTEB](https://paperswithcode.com/dataset/mteb) |
| 9. [xP3](https://paperswithcode.com/dataset/xp3) |
| 10. [DiaBLa](https://paperswithcode.com/dataset/diabla) |
| |
| # Deep RL ML Strategy 🧠 |
| |
| The AI strategies are: |
| - Language Model Preparation using Human Augmented with Supervised Fine Tuning 🤖 |
| - Reward Model Training with Prompts Dataset Multi-Model Generate Data to Rank 🎁 |
| - Fine Tuning with Reinforcement Reward and Distance Distribution Regret Score 🎯 |
| - Proximal Policy Optimization Fine Tuning 🤝 |
| - Variations - Preference Model Pretraining 🤔 |
| - Use Ranking Datasets Sentiment - Thumbs Up/Down, Distribution 📊 |
| - Online Version Getting Feedback 💬 |
| - OpenAI - InstructGPT - Humans generate LM Training Text 🔍 |
| - DeepMind - Advantage Actor Critic Sparrow, GopherCite 🦜 |
| - Reward Model Human Prefence Feedback 🏆 |
| |
| For more information on specific techniques and implementations, check out the following resources: |
| - OpenAI's paper on [GPT-3](https://arxiv.org/abs/2005.14165) which details their Language Model Preparation approach |
| - DeepMind's paper on [SAC](https://arxiv.org/abs/1801.01290) which describes the Advantage Actor Critic algorithm |
| - OpenAI's paper on [Reward Learning](https://arxiv.org/abs/1810.06580) which explains their approach to training Reward Models |
| - OpenAI's blog post on [GPT-3's fine-tuning process](https://openai.com/blog/fine-tuning-gpt-3/) |
| |
| """) |
|
|
| demo.launch() |
