Embedding Models Embedding models map high-dimensional data into a lower-dimensional space, which makes the data easier to process and analyze. Text Models Here is a leaderboard, https://huggingface.co/spaces/mteb/leaderboard The leaderboard above gives each model’s score, parameter count, memory usage, vector dimension, maximum tokens, and other information. Below are some commonly used open-source models:

1. Deploying Jumpserver You need a StorageClass ready in advance to store Jumpserver’s data. Besides the database mentioned below, each component — jms-core, jms-web, jms-koko, jms-lion, jms-chen — also needs a PV for storage. 1.1 Deploying MySQL Refer to https://github.com/shaowenchen/ops-hub/blob/main/database/mysql8.yaml to deploy MySQL. You need to adjust the StorageClass field

1. Data Processing Architecture It is mainly divided into four layers: Processing capability layer: Spark on Kubernetes provides streaming data processing capability Data management layer: Iceberg provides dataset access operations such as ACID and tables Storage layer: Hive MetaStore manages Iceberg table metadata, PostgreSQL serves as the storage backend for

1. Compiling Models with TensorRT-LLM 1.1 Introduction to TensorRT-LLM When using TensorRT, you usually need to convert the model to ONNX format, then convert the ONNX to TensorRT format, and finally run inference in TensorRT or Triton Server. But this conversion process is not simple, and you often run into

Paid integration service available at 200 RMB/year; technical support is also available at 200 RMB per session, capped at 1 hour. Follow the official account to get contact details. 1. Information You Need to Provide Go to https://mp.weixin.qq.com/ and open 【Settings and