The first time I heard about Vector Quantization (VQ) was from a friend who was working on audio processing, which gave me a vague understanding that VQ is a technique used for data feature compression and representation. At that time, I still wasn’t clear on how it differed from dimensionality reduction techniques like PCA.
Read More »[Machine Learning] Vector Quantization (VQ) Notes
In the FastAPI official documentation, there’s a section about ‘Automatic Interactive API Documentation‘:
In Python, the context manager decorator @contextmanager from the contextlib module allows developers to conveniently create our own context manager.
Recently, I integrated several applications of Outlines into my current workflow. Among them, the one I use most frequently is with vLLM. However, for some reason, its documentation has not been merged into the vLLM GitHub repository, so while designing the process, I had to constantly refer to the source code of a rejected PR for guidance XD
Read More »Using the Integrated Outlines Tool for Decoding Constraints in the vLLM Inference Acceleration Framework
This is a simple Python implementation, used to test Finite-State Machine (FSM) constraints for a Large Language Model (LLM) to decode responses in a specific format. It also serves as an introduction to the concept behind the Outlines tool. Of course, my implementation is far simpler compared to the actual Outlines tool.
Read More »Implementation of Using Finite-State Machine to Constrain Large Language Model Decoding
Variational AutoEncoder (VAE) is an advanced variant of the AutoEncoder (AE). The architecture is similar to the original AutoEncoder, consisting of an encoder and a decoder.
Read More »[Machine Learning] Note Of Variational AutoEncoder (VAE)difflib is a module in the Python standard library used to compare differences between sequences (often text). Back when I was doing my thesis, I implemented this by hand. It’s funny and a bit frustrating to realize now in my work that there’s such a neat module for this.
In Python class construction, the @property decorator is commonly used and has significant benefits. Its main purpose is to transform a class method into a read-only attribute, allowing users to retrieve computed results via attribute access.
Singular Value Decomposition (SVD) is a method for decomposing a matrix into three matrices, revealing the rank, data dimensions, and key directions of the original matrix. It is often used in dimensionality reduction, compression, and structural analysis.
Read More »Note Of Singular Value Decomposition (SVD)
CuPy is an open-source GPU-accelerated numerical computation library designed for deep learning and scientific computing. It shares many of the same methods and functions as the popular NumPy package in Python but extends its capabilities to perform computations on the GPU. In short, tasks that can benefit from parallel computation on the GPU, such as matrix operations, can achieve significant acceleration with CuPy.
Read More »Using CuPy to Accelerate Matrix Operations with GPU
