Tiles with random angles and Y-axis-based spacing Tiles with noise-based angles and random line spacing Tiles with random angles and random line spacing Tiles with random angles and uniform line spacing Paul in his plots rather it is simply my interpretation of how I would goĪbout generating something similar. I should point out that I do not know if this is the exact technique used by Style of a single square from the above images. In this post, we’ll look at an algorithm that enables us to recreate the If you tried it yourself, you’ll find that there’s some algorithmic sauce Naturally I sat around noodling away on a Saturday trying to recreate it! Of squares, with lines at arbitrary angles with variations in the spacingĭetail of section showing clipped parallel lines Breaking it Down Looking closely at one of his works, one sees that it’s comprised of a grid If you haven’t seen them yet, go check them out over at Over on Twitter and he’s been churning out absolutely beautiful stuff on his I’ve been following the work of Paul Rickards For a copy-pasteable version of the algorithm, click here. To that end, we are continually expanding our dataset and developing better models.All Processing code for this article, along with images, can be found on Github. ![]() Our ultimate aim is to not only develop an open-source version of Github's Code Copilot but one which is of comparable performance and ease of use. FutureĮleuther AI has kindly agreed to provide us with the necessary computing resources to continue developing our project. Please visit our models page to see the models we trained and the results of our fine-tuning. However, we heavily modified this script to support the GPT3 learning rate scheduler, weights and biases report monitoring, and gradient accumulation since we only had access to TPUv3-8s for training and so large batch sizes (1024-2048) would not fit in memory. Our training scripts are based on the Flax Causal Language Modelling script from here. Therefore, all of the versions of our models are fine-tuned. However, fine-tuning allowed the model to converge faster than training from scratch. We decided to fine-tune rather than train from scratch since in OpenAI's GPT-Codex paper, they report that training from scratch and fine-tuning the model are both equally in performance. Modifying the batch size and learning rate as suggested by people in EleutherAI's discord server when fine-tuning the model. We used the hyperparameters discussed in the GPT-3 small configuration from EleutherAI's GPT-Neo model. Please visit our datasets page for more information regarding them. To train our model, we used Huggingface's Transformers library and specifically their Flax API to fine-tune our model on various code datasets including one of our own, which we scraped from GitHub.
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