[Python] How To Use @contextmanager Decorator
In Python, the context manager decorator @contextmanager from the contextlib module allows developers to conveniently create our own context manager.
Python
Using the Integrated Outlines Tool for Decoding Constraints in the vLLM Inference Acceleration Framework
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
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Implementation of Using Finite-State Machine to Constrain Large Language Model Decoding
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.
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[Machine Learning] Note Of Variational AutoEncoder (VAE)
Introduction
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)[Python] Using the difflib Module to Compare Sequence Differences
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.
[Python] Using @property Decorator To Convert Class Method Into Read-Only Attribute
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.
Note Of Singular Value Decomposition (SVD)
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.
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Using CuPy to Accelerate Matrix Operations with GPU
Introduction
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[Python] Use `httpx` To Replace `requests` For Asynchronous Requests
In Python programming, we often use the requests module for HTTP requests. However, requests can become a bottleneck when connecting frontend and backend services due to its synchronous request handling. Recently, I experienced Kubernetes probe blockages caused by using requests, which led to the unintended deletion of my service container. In such scenarios, httpx might be a more suitable module for asynchronous request handling.
Use `snapshot_download` To Download The Models Of HuggingFace Hub
Introduction
HuggingFace Model Hub is now a widely recognized and essential open-source platform for every one. Every day, countless individuals and organizations upload their latest trained models (including those for text, images, speech, and other domains) to this platform. It can be said that anyone working in AI-related fields frequently browses the HuggingFace platform website.
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