GraphGrail AI (GAI) ICO Details & Financial Information

More Info About ICO

Название ICOGraphGrail AI
СимволGAI
Дата начала19 февраля, 2018
Дата окончания25 мая, 2018
СтранаBritish Virgin Islands
ПлатформаEthereum
Заканчивается
3 лет назад

Dapps marketplace
Whether you are a data scientist or businessman, you can make your own linguistic applications, sell them and make money.

CEO & Founder
Co-founder, Developer
Venture investor, CMO

Ben and I have released GPT-J, 6B JAX-based Transformer LM 🥳

- Performs on par with 6.7B GPT-3
- Performs better and decodes faster than GPT-Neo
- repo + colab + free web demo

article: https://bit.ly/2TH8yl0
repo: https://bit.ly/3eszQ6C

You can do Zero-Shot classification with HuggingFace model. It doesn't require training data, you can run classification tasks straight away with input sentences and candidate labels.

Complete example in Colab: https://colab.research.google.com/drive/15xBXstKXRnBcalJqDHdgfPQVmNfC9hrf

@huggingface #NLP #Python #TensorFlow

"The end goal, between 1-2 years, is GPT-NeoX, which will be open source and have 200 billion parameters." https://boards.4channel.org/vg/thread/338253176#p338266291

It's also possible that most of GPT-3's quality can be obtained from a ~20B model.

GPT-Neo, the #OpenSource cousin of GPT3, can do practically anything in #NLP from sentiment analysis to writing SQL queries: just tell it what to do, in your own words. 🤯

How does it work? 🧐
Want to try it out? 🎮
👉 https://huggingface.co/blog/few-shot-learning-gpt-neo-and-inference-api

🔥JAX meets Transformers🔥

@GoogleAI's JAX/Flax library can now be used as Transformers' backbone ML library.

JAX/Flax makes distributed training on TPU effortless and highly efficient!

👉 Google Colab: https://colab.research.google.com/github/huggingface/notebooks/blob/master/Text_Classification_on_GLUE_on_TPU_using_Jax_Flax.ipynb
👉 Runtime evaluation:
https://github.com/huggingface/transformers/tree/master/examples/flax/text-classification

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Цена0.10 USD Продажа100,000,000 Способ оплатыETH, BTC
Минимальная инвестицияN/A Распределение50% Собрано$1,947,387
Софт-кап2,000,000 USD Хард-кап10,000,000 USD
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