∫ntegrabℓε ∂iﬀerentiαℓs · unorganised notes, code, and writings of random topics

Peng et al (2023) YaRN: Efficient Context Window Extension of Large Language Models

November 9, 2023 paper

This paper studies the positional encoding in large language models, since it is the factor that needs to change if we want to support a longer context length than it was pretrained. The original transformer model’s position encoding scheme was called the absolute sinusoidal position encoding. Then, there are models... [more]

Reading the LLaMA code

October 30, 2023 blog code

The LLaMA and LLaMA 2 model released by Meta/Facebook is available on GitHub and there’s a guide to help you using it. Of course, from Meta, this model is using PyTorch. But surprisingly, the repo on GitHub is very short that you can read and understand it in a day... [more]

Naveed et al (2023) A Comprehensive Overview of LLMs

October 25, 2023 paper

This is a survey paper of the current status of large language models. It is a nice read because it is short (37 pages only) and covered some key components needed to understand what these models are about. [more]

Self-hosted Copilot for Your VSCode

July 17, 2023 blog system

GitHub has its Copilot service that we can pay a subscription for. It is a coding assistant in your IDE, which requires a plugin on your editor and performs auto-complete for the code you typed. There are off-the-shelf language models that can generate code like GitHub’s Copilot. But the model... [more]

Conda and CUDA

July 12, 2023 blog system

If we want to run TensorFlow or PyTorch with CUDA on Linux, for example, we can install CUDA as a system library first and then install the Python package with pip (or via apt-get, in the rare case). This way, the package will find the CUDA library at system locations.... [more]