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Fun with Large Models
Fun with Large Models
Apr 17, 2026 · Artificial Intelligence

Mastering Large Model Training: Practical Parameter Tuning from Beginner to Pro

This guide walks you through interpreting training logs and loss curves, diagnosing common issues such as oscillation, under‑fitting, and over‑fitting, and applying concrete adjustments to learning rate, LoRA settings, batch size, and epochs, with scenario‑specific strategies to turn a novice into a tuning expert.

AI trainingLarge ModelLoRA
0 likes · 23 min read
Mastering Large Model Training: Practical Parameter Tuning from Beginner to Pro
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Apr 7, 2026 · Artificial Intelligence

Why Hybrid Retrieval Beats Pure Vector Search: BM25, RRF, and Real‑World Experiments

This article dissects the shortcomings of pure vector retrieval, explains how BM25 complements it, compares weighted‑sum and Reciprocal Rank Fusion (RRF) strategies, shows experimental results that identify optimal weight and k values, and provides practical engineering tips for deploying hybrid search in RAG systems.

BM25Hybrid RetrievalParameter Tuning
0 likes · 24 min read
Why Hybrid Retrieval Beats Pure Vector Search: BM25, RRF, and Real‑World Experiments
Model Perspective
Model Perspective
Dec 28, 2025 · Fundamentals

Mastering PID Control: Theory, Tuning, and MATLAB/Simulink Implementation

Explore the core principles of PID control—from proportional, integral, and derivative actions that eliminate error—to practical parameter tuning methods and step-by-step MATLAB/Simulink simulations, including code snippets and block diagrams that illustrate how to model, discretize, and validate a PID controller for real‑world systems.

AutomationMATLABPID
0 likes · 11 min read
Mastering PID Control: Theory, Tuning, and MATLAB/Simulink Implementation
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Nov 1, 2025 · Artificial Intelligence

AutoCCL: Automatic NCCL Tuning to Boost Distributed Deep Learning Performance

AutoCCL analyzes NCCL’s six key performance parameters, uses coordinate‑descent and an online leader‑worker architecture to automatically adjust them during training, overcoming state‑space explosion and compute‑communication interference, and achieves 1.07‑1.32× faster iteration times on models such as Phi‑2, Llama‑3.1‑8B and VGG‑19.

AutoCCLCoordinate DescentDistributed Deep Learning
0 likes · 5 min read
AutoCCL: Automatic NCCL Tuning to Boost Distributed Deep Learning Performance
Aikesheng Open Source Community
Aikesheng Open Source Community
Jul 9, 2025 · Databases

Unlocking OceanBase Thread Limits: How min_worker_cnt and max_worker_cnt Are Calculated

This article explains why OceanBase enforces minimum values for min_worker_cnt and max_worker_cnt, shows the source‑code formulas used to compute them, demonstrates how to verify and adjust these parameters, and clarifies the impact of tenant memory and Meta‑tenant reservations on the final values.

Database ConfigurationOceanBaseParameter Tuning
0 likes · 12 min read
Unlocking OceanBase Thread Limits: How min_worker_cnt and max_worker_cnt Are Calculated
ByteDance Data Platform
ByteDance Data Platform
Apr 25, 2025 · Databases

How ByteDance’s AQETuner Cuts Query Latency by 23% and Boosts Reliability

ByteDance Data Platform’s recent breakthroughs in database research—spanning query‑level Bayesian tuning, adaptive stream‑processing parallelism, and learned cardinality estimation—were highlighted by two papers accepted at VLDB 2025 and ICDE 2025, showcasing significant performance gains and real‑world deployments.

AIParameter Tuningcardinality estimation
0 likes · 5 min read
How ByteDance’s AQETuner Cuts Query Latency by 23% and Boosts Reliability
Ops Development & AI Practice
Ops Development & AI Practice
Apr 6, 2025 · Artificial Intelligence

Mastering Ollama Modelfile: Build and Customize Your Own LLM

This guide explains how to retrieve, analyze, and modify an Ollama Modelfile—using commands like `ollama show --modelfile`, dissecting key directives such as FROM, TEMPLATE, LICENSE, PARAMETER, SYSTEM, and ADAPTER—and walks through step‑by‑step creation of a custom model.

AI modelLLM customizationLoRA
0 likes · 9 min read
Mastering Ollama Modelfile: Build and Customize Your Own LLM
Architect
Architect
Feb 12, 2025 · Artificial Intelligence

Master Prompt Engineering: A Universal Framework for LLMs

This article presents a comprehensive, step‑by‑step Prompt engineering framework—including role definition, problem description, goal setting, and requirement specification—augmented with techniques such as RAG, few‑shot examples, memory handling, and parameter tuning, enabling users to craft effective prompts for large language models across domains.

AI Prompt OptimizationFew-ShotMemory
0 likes · 27 min read
Master Prompt Engineering: A Universal Framework for LLMs
DataFunSummit
DataFunSummit
Feb 7, 2025 · Artificial Intelligence

Fusion Ranking and Multi-Objective Optimization in Recommendation Systems

This article introduces the role of ranking formulas in recommendation systems, compares sequence and value fusion methods, discusses multi‑objective trade‑offs, explains offline parameter search principles, and demonstrates the open‑source ParaDance framework for automated ranking formula optimization.

Parameter Tuningalgorithm engineeringmulti-objective
0 likes · 17 min read
Fusion Ranking and Multi-Objective Optimization in Recommendation Systems
DaTaobao Tech
DaTaobao Tech
Sep 6, 2022 · Big Data

SQL Optimization Techniques for ODPS (Open Data Processing Service)

The article presents practical ODPS SQL optimization strategies—including explicit column selection, partition limiting, multi‑insert, proper handling of nulls, join‑type choices, map‑join and skew hints, bucketed tables, and tuned task parameters—illustrated with three real‑world cases that dramatically cut execution time and resource usage.

Big DataData SkewHive
0 likes · 23 min read
SQL Optimization Techniques for ODPS (Open Data Processing Service)
Tencent Cloud Developer
Tencent Cloud Developer
Apr 8, 2022 · Databases

Tencent Cloud Native Database AI Autonomy: SIGMOD Research and Intelligent Tuning System

Tencent Cloud’s native database team achieved a SIGMOD breakthrough by embedding AI into MySQL, creating an autonomous “database brain” that uses deep‑reinforcement learning, genetic pre‑heating and a closed‑loop learner/actor architecture to automatically observe, analyze, and tune diverse workloads, delivering rapid performance gains, anomaly detection, and self‑optimizing features while addressing adaptability, stability, and interpretability challenges.

AI OptimizationCDBTuneDatabase Autonomy
0 likes · 8 min read
Tencent Cloud Native Database AI Autonomy: SIGMOD Research and Intelligent Tuning System
Alimama Tech
Alimama Tech
Sep 29, 2021 · Artificial Intelligence

Unified Solution to Constrained Bidding in Online Display Advertising (USCB)

The paper proposes a unified solution for real‑time bidding in online display ads that formulates advertiser budget and KPI limits as a constrained linear program, derives a closed‑form optimal bidding function with m+1 parameters, and uses model‑free reinforcement learning to dynamically adjust those parameters, achieving superior traffic‑value capture in large‑scale deployment on Alibaba’s Taobao platform.

Parameter Tuningconstrained optimizationreal-time bidding
0 likes · 11 min read
Unified Solution to Constrained Bidding in Online Display Advertising (USCB)
Aikesheng Open Source Community
Aikesheng Open Source Community
Oct 13, 2020 · Databases

Testing the Impact of group_replication_member_expel_timeout on MySQL Group Replication under Network Latency

This article investigates how the MySQL 8.0 group_replication_member_expel_timeout parameter influences node expulsion in a group replication cluster when network latency is introduced, describing the test environment, methodology, commands, observations, and configuration recommendations.

Database ClusterGroup ReplicationNetwork Latency
0 likes · 7 min read
Testing the Impact of group_replication_member_expel_timeout on MySQL Group Replication under Network Latency
Fulu Network R&D Team
Fulu Network R&D Team
Jul 21, 2020 · Artificial Intelligence

Prophet Parameter Tuning and Practical Guide for Time Series Forecasting

This article provides a comprehensive tutorial on Prophet's key parameters, their meanings, and practical tips for tuning them—including growth, changepoints, seasonalities, holidays, and Bayesian settings—along with Python code examples for grid search and cross‑validation to improve forecasting accuracy.

Parameter TuningProphetPython
0 likes · 14 min read
Prophet Parameter Tuning and Practical Guide for Time Series Forecasting
ITPUB
ITPUB
Feb 5, 2020 · Fundamentals

How a Simple Java Simulation Reveals COVID‑19 Spread Dynamics

This article explains a Java‑based epidemic simulation that models virus transmission using a normal distribution, demonstrates how parameters like initial infections, transmission rate, hospital capacity, and mobility affect outbreak curves, and shows how adjusting these values can illustrate the impact of public health interventions.

COVID-19JavaModeling
0 likes · 8 min read
How a Simple Java Simulation Reveals COVID‑19 Spread Dynamics
Big Data Technology & Architecture
Big Data Technology & Architecture
Aug 12, 2019 · Big Data

Spark SQL Parameter Tuning and Performance Optimization (Spark 2.3.2)

This article explains how to troubleshoot and tune Spark SQL configuration parameters—covering exception‑related settings such as spark.sql.hive.convertMetastoreParquet, file‑ignore options, and partition verification, as well as performance‑focused tweaks like broadcast join thresholds, adaptive execution, and parquet schema merging—while providing a comprehensive parameter reference table.

Big DataHive MigrationParameter Tuning
0 likes · 23 min read
Spark SQL Parameter Tuning and Performance Optimization (Spark 2.3.2)
Qunar Tech Salon
Qunar Tech Salon
Mar 28, 2015 · Artificial Intelligence

Support Vector Machines in R: Theory, Implementation, and Parameter Tuning

This article explains how support vector machines work, how to handle non‑linear and multi‑class problems, and provides a complete R implementation using the e1071 package, including linear and radial kernels, model evaluation, parameter tuning, and visualisation with grid plots.

Grid PlotParameter TuningR
0 likes · 9 min read
Support Vector Machines in R: Theory, Implementation, and Parameter Tuning