app.py

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):
    # Use the last 50 characters of the text as context
    # return text[:-50] + api(text[-50:])
    # Use the last 100 characters of the text as context
    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()