A NEW ARTICLE The first article of Deep Learning Revision Research Blog(introduced recently) is out: "The Transformer Blueprint: A Holistic Guide to the Transformer Neural Network Architecture". In the article, we discuss the core mechanics of transformer neural
CODE
-
Using PyTorch Vision ViT as Baseline for Model Training
By
–
No that I am aware (but I also haven't looked too deeply). I would probably use the original ViT in Torchvision as a baseline: https://
pytorch.org/vision/main/_m
odules/torchvision/models/vision_transformer.html#vit_b_16
… I have a companion article here showing how to accelerate the training on that model: -
Open-Source Model Fine-tuning Methods and Training Flexibility
By
–
Yes, any finetuning method is allowed, afaik. Technically, you could also just further (pre)train the model. Anything that runs <24 h on open source code and openly available data.
-
10x Engineers Must Embrace AI Tools to Stay Relevant
By
–
weird effect of AI developer tools— the 10x engineers of today will NOT be the 10x engineers of tomorrow because they’ll be too proud to adopt the new AI tools and workflows, which are meaningfully different. the solution—have no ego, let the AI tell you what to do.
-
OpenAI Extends Deprecation Window Amid Developer Backlash
By
–
We also saw OpenAI capitulating to developers by extending a deprecation window to a full year (up from 3mo) due to "feedback" (aka backlash) from developers. It also saw the rise of a narrative of GPT-4's performance dropping, which was a bit of a misinterpretation of a paper.
-

LangSmith Updates and Open Source Recap Released
By
–
First edition out LangSmith updates, Open Source recap, Use-cases we love… and some sneak previews of things on the horizon Concise, high density. s/o @zebriez
-
Llama 2 Coding Limitations Impact on Agentic AI Tasks
By
–
Had an awesome webinar with @RLanceMartin and @jamescalam on Weds around using Llama 2 Llama2 is significantly worse on coding tasks – is that why it's not nearly as good at agentic tasks as GPT models? Up on YouTube now:
-
Leandojo Democratizes Theorem Proving with Large Language Models
By
–
Leandojo democratizes theorem proving with LLMs. Visit @KaiyuYang4 poster today at @icmlconf
-
Comparing SDXL Refiner Model Handoff Timing Across Schedulers
By
–
And compare when to hand off to the SDXL refiner model for different schedulers pic.twitter.com/DruFYqnPlB
— Replicate (@replicate) 28 juillet 2023And compare when to hand off to the SDXL refiner model for different schedulers
-
Explore Different Steps, Schedulers and Guidance Scales
By
–
Try out different steps, schedulers and guidance scales pic.twitter.com/mp6Gb1GcAt
— Replicate (@replicate) 28 juillet 2023Try out different steps, schedulers and guidance scales
