Gorse Now Supports RISC-V Architecture

Thanks to the Bianbu Cloud RISC-V cloud computing instances provided by SpacemiT, Gorse has now completed support for the RISC-V architecture.

Text Embedding Benchmark for Recommender Systems

Embedding models encode multimodal information such as images and text into high-dimensional vectors, enabling the calculation of relationships between multimodal information by measuring the distance between embedding vectors in online services like search engines and recommender systems. Text embeddings are the most widely used. Major AI service providers offer text embedding APIs to their users, and there are also many open-source text embedding models available for self-hosting. The current mainstream evaluation standard for text embedding models is MTEB. However, MTEB does not assess the capabilities of text embedding models in recommender systems, and this post will attempt to evaluate the performance of text embedding models in recommender systems.

RISC-V Workflows on GitHub Actions (QEMU)

GitHub Actions provides various continuous integration environments for projects hosted on GitHub, including three operating systems (Linux, macOS, and Windows) and two architectures (AMD64 and ARM64). These environments are sufficient for most projects, but RISC-V developers may find it challenging to run RISC-V workflows on GitHub Actions. Commercial companies can use self-hosted runners (refer to Supporting runners on 64bit RISC-V) or service providers (RISC-V Runners and Cloud-V), but for individual developers, this represents a significant expense.

BF16 in the Go Programming Language

In the era of large language models, low-precision floating-point numbers are no strangers to developers, with BF16 being one of the most widely supported low-precision floating-point formats. This article will introduce how to use BF16 in the Go programming language.

How to Use AVX512 in Golang via C Compiler

AVX512 is the latest generation of SIMD instructions released by Intel, which can process 512 bits of data in one instruction cycle, equivalent to 16 single-precision floating point numbers or 8 double-precision floating point numbers. The training and inference process of recommendation models in Gorse requires a lot of vector computation, and AVX512 can theoretically bring some acceleration effect. Unfortunately, the Go compiler does not automatically generate SIMD instructions.

Integrate Frontend Artifacts via Go Modules

The ability to compile into a single binary is a great feature of the Go programming language, avoiding dependency management at deployment time. However, if the project contains front-end code, we need to find a way to embed the front-end artifact into the Go binary at compile time. The compilation process is as follows.