DALL-E Mini Explained with Demo

Tech report:

- Financed by Google Cloud and HF, essentially an advertising campaign for JAX, 8 person team
- 27x smaller than the original, trained on a single TPU v3-8 for only 3 days + ~3 weeks for experiments, 400M params
- 30m image-text pairs, only 2m used to fine-tune the VQGAN encoder
- Could use preemptible TPU instances
- Pre-trained BART Encoder
- Pre-trained VQGAN encoder
- Pre-trained CLIP is used to select the best generated images
- (so the actual cost probably is actually ~1-2 orders of magnitude higher)
- (compare with 20k GPU days stipulated by Sber)
- The report is expertly written and easy to read

💬 Yandex: An Open-source Yet another Language Model 100B

YaLM 100B is trained for 2 terabyte of text: dataset the Pile and web-pages, including not only Wikipedia, news articles, and books, but also Github and arxiv.org. Yandex has applied the generative neural networks YaLM in the recent Y1 search update. Now they are already helping to give answers to searches in Yandex and Alice.

Github: https://github.com/yandex/YaLM-100B

@ai_machinelearning_big_data

In 15 minutes, start the last section of DataFestOnline 2022.

[language of presentations - Russian]

Mikhail Neretin - Topic management in Kafka
Evgeniy Sorokin - Human and ML => collaboration
Olga Filippova - Data drift & Concept drift: How to monitor ML models in production
Dmitry Beloborodov - Matrix factorizations with embeddings of variable dimension
Stanislav Yarkin - Summarization Is All You Need: summarization in RecSys
Artem Trofimov - MLOps by DS forces
Possible: Secret Speaker - MLEM: Version and deploy your ML models following Hitops principles

Start at 11:00 MSK (UTC+3)
There is an opportunity to ask a question to the speaker

Where?
Come to the session -> https://live.ods.ai/
Room: Last chance
Password: followthepinkparrot

Today mimesis has been designated as a critical project on PyPI.

It's ain't much, but I feel warm when I think about how many people use think I built.

Thank you everyone!

P.S If you don't know what the hell mimesis is, then go and check it out. Maybe you'll find it useful for you.

#mimesis #pypi #python

​​No Language Left Behind
Scaling Human-Centered Machine Translation

No Language Left Behind (NLLB) is a first-of-its-kind, AI breakthrough project that open-sources models capable of delivering high-quality translations directly between any pair of 200+ languages — including low-resource languages like Asturian, Luganda, Urdu and more. It aims to help people communicate with anyone, anywhere, regardless of their language preferences.

To enable the community to leverage and build on top of NLLB, the lab open source all they evaluation benchmarks (FLORES-200, NLLB-MD, Toxicity-200), LID models and training code, LASER3 encoders, data mining code, MMT training and inference code and our final NLLB-200 models and their smaller distilled versions, for easier use and adoption by the research community.


Paper: https://research.facebook.com/publications/no-language-left-behind/
Blog: https://ai.facebook.com/blog/nllb-200-high-quality-machine-translation/
GitHub: https://github.com/facebookresearch/fairseq/tree/26d62ae8fbf3deccf01a138d704be1e5c346ca9a

#nlp #translations #dl #datasets

​​the #dalle prompt book
first edition

free to download, it features 82 pages of artistic inspiration with over 300 examples of dalle2 images

A.W.E.S.O.M. O is an extensive list of interesting open source projects written in various languages.

#python #rust #js #php #golang #go #ts #kotlin #js #clojure #erlang #elixir #c #cpp #dart #ocaml #etc

DALL·E Now Available in Beta

#Dalle by #openai was released for public (though obviously moderated) access. Join waitlist to play around.

Images are also available for commercial use.

By default user can generate 460 images, further generations (or variations on generated images) will be available on paid plan.

Link: https://openai.com/blog/dall-e-now-available-in-beta/

#image #CV #GAN #generation #generatinveart

Some stats to get the perspective of the development of #dalle

«Used 1000 prompts in Dalle over the last 2 days, about 9 hours each day. Of those, saved ~300. 50 I like enough to share w/ socials. 12 enough to rework for future projects. 3 were perfect, may mint someday. Curation is *1* step of AI art. I used to finish physicals in less time.»

Source: https://twitter.com/clairesilver12/status/1550709299797577729

#visualization #gan #generation #generatinveart #aiart #artgentips

​​Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios

While vision transformers demostrate high performance, they can't be deployed as efficiently as CNNs in realistic industrial deployment scenarios, e. g. TensorRT or CoreML.

The authors propose Next-ViT, which has a higher latency/accuracy trade-off than existing CNN and ViT models. They develop two new architecture blocks and a new paradigm to stack them. As a result, On TensorRT, Next-ViT surpasses ResNet by 5.4 mAP (from 40.4 to 45.8) on COCO detection and 8.2% mIoU (from 38.8% to 47.0%) on ADE20K segmentation. Also, it achieves comparable performance with CSWin, while the inference speed is accelerated by
3.6×. On CoreML, Next-ViT surpasses EfficientFormer by 4.6 mAP (from 42.6 to 47.2) on COCO detection and 3.5% mIoU (from 45.2% to 48.7%) on ADE20K segmentation under similar latency.

Paper: https://arxiv.org/abs/2207.05501

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-next-vit

#deeplearning #cv #transformer #computervision

Now it is official.

I've started writing a book on JAX. This seems to be the first book ever on this topic.

For those who don't know, JAX is an exceptionally cool numeric computations library from Google, a kind of NumPy on steroids, with autodiff, XLA compilation, and hardware acceleration on TPU/GPU. JAX also brings the functional programming paradigm to deep learning.

JAX is heavily used for deep learning and already pretends to be the deep learning framework #3. Some companies, like DeepMind, have already switched to JAX internally. There are rumors that Google also switches to JAX.

JAX ecosystem is constantly growing. There are a lot of high-quality deep learning-related modules. But JAX is not limited to deep learning. There are many exciting applications and libraries on top of JAX for physics, including molecular dynamics, fluid dynamics, rigid body simulation, quantum computing, astrophysics, ocean modeling, and so on. There are libraries for distributed matrix factorization, streaming data processing, protein folding, and chemical modeling, with other new applications emerging constantly.

Anyway, it's a perfect time to start learning JAX!

The book is available today as a part of the Manning Early Access Program (MEAP), so you can read the book as I write it 🙂 This is a very smart way of learning something new, you do not have to wait until the complete book is ready. You can start learning right away, and at the moment the book is published, you already know everything. Your feedback will also be very valuable, and you can influence how the book is made.

Here's a link to the book: https://mng.bz/QvAG

If you want a decent discount, use the discount code mlsapunov. It will provide you with 40% off, and it's valid through August 11th.

Frankfurt Data Science welcome you back to the onsite event in Frankfurt-am-Main! As usual special - a demo of the new #dalle2 model by statworx team, Sebastian Heinz and Fabian Müller, socialising time, and a cocktail bar with free cocktails. Frankfurt DS Community thanks SAS for supporting and TechQuartier for hosting! They are looking for more supporters.

Event details:
Date: 24th August 2022
Time: 6PM - 11PM
Location: TechQuartier, Frankfurt

Register here

RuLeanALBERT : https://github.com/yandex-research/RuLeanALBERT

RuLeanALBERT is a pretrained masked language model for the Russian language using a memory-efficient architecture.

#opensource : RuLeanALBERT от Yandex Research
2.9B трансформер для русского, которая влезет в домашнюю ПеКарню ресерчера

Мало того, что это самая большая БЕРТ-подобная модель для русского языка, которая показывает крутые результаты в бенчмарках, так еще и с кодом для fine-tuning-а

GitHub

А в статье можете узнать, как обучалась эта модель (а-ля коллаборативное глубокое обучение) на фреймворке по децентрализованному обучению Hivemind

Kolesa Conf 2022 — Kolesa Group's conference for IT community

Kolesa Group will host its traditional conference on 8 October. Registration is open. Kolesa Conf is an annual Kazakhstan IT conference where leading experts from the best IT companies of the CIS take part.

One of the tracks is devoted to Data. There will be 12 speakers. Some of featured reports:

• «Methodology of construction of cloud CDW: case Air Astana».
• How to correctly estimate the sample size and time of the test. And what non-obvious difficulties can be encountered along the way.
• «Creation of a synthetic control group with the help of Propensity Score Matching».
• How the dynamic minimum cheque (surge pricing) hated by users was developed and implemented.
• «Analytics at the launch of a new product. An example of market place spare parts»

Conference website: https://bit.ly/3UmCoWC

#conference #it #data #analysis

Wanna have impromptu Data Science breakfast tomorrow in the center of Helsinki. Please dm @malev if you are interested