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fine-tuning

139 articles · Page 1 of 2
Qunhe Technology Quality Tech
Qunhe Technology Quality Tech
Jun 23, 2026 · Artificial Intelligence

Why Pixel Diff Failed and How VLM Fine‑Tuning Became the Eyes of UI Automation

Traditional pixel‑by‑pixel UI comparison breaks on complex CAD drawings due to semantic changes, so a team built a visual‑language‑model fine‑tuning pipeline that turns failure cases into training data, achieves ~95% AI accuracy, improves regression efficiency by over 40%, and now powers hundreds of daily automation tests.

AI monitoringUI automationVLM
0 likes · 12 min read
Why Pixel Diff Failed and How VLM Fine‑Tuning Became the Eyes of UI Automation
Data Party THU
Data Party THU
Jun 19, 2026 · Artificial Intelligence

The Six Critical Choices Every AI Engineer Must Make

This article examines six production trade‑offs that AI engineers face—build vs. buy LLMs, model complexity vs. maintainability, data quantity vs. quality, batch vs. real‑time inference, prompt engineering vs. fine‑tuning, and automation vs. human‑in‑the‑loop—backed by surveys, research studies, and concrete cost analyses.

AI EngineeringData QualityLLM build vs buy
0 likes · 15 min read
The Six Critical Choices Every AI Engineer Must Make
DeepHub IMBA
DeepHub IMBA
Jun 17, 2026 · Artificial Intelligence

How a 1.5B Parameter Model Can Add External Knowledge to Any Frozen LLM

The article analyzes MEMO, a framework that equips a frozen large language model with a lightweight 1.5B‑parameter memory model fine‑tuned on a target corpus, detailing its architecture, five‑step data synthesis pipeline, structured inference protocol, experimental advantages over RAG and fine‑tuning, as well as its limitations and future research directions.

Knowledge IntegrationLLMMemory Model
0 likes · 19 min read
How a 1.5B Parameter Model Can Add External Knowledge to Any Frozen LLM
AgentGuide
AgentGuide
Jun 5, 2026 · Artificial Intelligence

RAG vs Fine‑Tuning vs Long Context: Choosing the Right Technique for AI Agents

The article explains why Retrieval‑Augmented Generation (RAG) addresses the static knowledge limitation of large models, contrasts its role of “what to say” with fine‑tuning’s focus on “how to say,” compares costs and performance against long‑context models, and offers a practical hierarchy (Prompt → RAG → LoRA/QLoRA fine‑tuning → Distillation) plus best‑practice combinations.

AI agentsLLMLong Context
0 likes · 9 min read
RAG vs Fine‑Tuning vs Long Context: Choosing the Right Technique for AI Agents
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Jun 3, 2026 · Artificial Intelligence

How 8 Agents Can Converge Stably: Trust‑Region Constraints Reshape Multi‑Agent LLM Workflows

The paper introduces TeamTR, a trust‑region fine‑tuning framework that mitigates compounding occupancy shift in multi‑agent LLM workflows by fresh rollout sampling and token‑level KL constraints, achieving stable performance gains of up to 7.1% overall and dramatic improvements on large‑scale tasks such as AIME24.

AI CoordinationTeamTRfine-tuning
0 likes · 9 min read
How 8 Agents Can Converge Stably: Trust‑Region Constraints Reshape Multi‑Agent LLM Workflows
IT Services Circle
IT Services Circle
May 17, 2026 · Artificial Intelligence

60 Essential AI Terms Every Programmer Should Master

This article walks programmers through 60 core AI concepts—from the basics of large language models and tokens to advanced topics like prompt engineering, retrieval‑augmented generation, fine‑tuning, and inference optimization—organized into progressive skill levels and illustrated with concrete examples and code snippets.

AIInference OptimizationLarge Language Models
0 likes · 25 min read
60 Essential AI Terms Every Programmer Should Master
Weekly Large Model Application
Weekly Large Model Application
May 5, 2026 · Artificial Intelligence

What Pretraining Actually Teaches: Listening to All Sounds

The article explains that pretraining for speech models functions like a broad liberal‑arts education, teaching universal acoustic and linguistic patterns through next‑token prediction, joint audio‑text training, and mask‑or contrast objectives, while clarifying common misconceptions and highlighting data bias and the need for clean, task‑specific fine‑tuning.

audio-text alignmentdata biasfine-tuning
0 likes · 6 min read
What Pretraining Actually Teaches: Listening to All Sounds
Lao Guo's Learning Space
Lao Guo's Learning Space
May 3, 2026 · Artificial Intelligence

2026 Enterprise Guide to Large Model Fine‑Tuning: Choosing, Training, and Deploying

This comprehensive guide explains why enterprises should fine‑tune large language models instead of using raw APIs or RAG, compares six fine‑tuning techniques (Full, LoRA, QLoRA, AdaLoRA, DoRA, Prompt‑Tuning), evaluates popular toolchains, outlines a step‑by‑step workflow, presents cost analyses, real‑world case studies, and practical best‑practice recommendations for 2026.

Enterprise AILarge Language ModelsLoRA
0 likes · 18 min read
2026 Enterprise Guide to Large Model Fine‑Tuning: Choosing, Training, and Deploying
DataFunSummit
DataFunSummit
May 3, 2026 · Artificial Intelligence

From Flawed to Production-Ready: Deep Dive into Building Enterprise-Grade RAG Systems

The article analyzes why early RAG deployments often fall short, dissects the most common technical pain points—from document parsing to vector overload—and presents a systematic roadmap that includes hybrid search, reranking, GraphRAG, Agentic RAG, model selection, scalability tricks, and security controls for robust B‑side production.

Agentic RAGEnterprise AIGraphRAG
0 likes · 20 min read
From Flawed to Production-Ready: Deep Dive into Building Enterprise-Grade RAG Systems
SuanNi
SuanNi
Apr 28, 2026 · Artificial Intelligence

Zero‑Code Fine‑Tuning Hundreds of Large Models with the LLaMA‑Factory MLU Image

This article provides a step‑by‑step guide to deploying the LLaMA‑Factory MLU image on Cambricon MLU hardware, covering environment checks, downloading the modified source package, configuring Python dependencies, and running both the Web UI and command‑line fine‑tuning for models such as Qwen2.5‑0.5B.

CLICambriconLLM
0 likes · 7 min read
Zero‑Code Fine‑Tuning Hundreds of Large Models with the LLaMA‑Factory MLU Image
AgentGuide
AgentGuide
Apr 26, 2026 · Artificial Intelligence

Can You Explain Large Model Training Without Complex Formulas? A Simple, Clear Guide

This article breaks down the fundamentals of large model training—covering data, parameters, neural networks, loss functions, gradient descent, pre‑training, and fine‑tuning—in plain language so readers can grasp how massive models learn without needing to dive into complex mathematics.

Model Trainingfine-tuninggradient descent
0 likes · 12 min read
Can You Explain Large Model Training Without Complex Formulas? A Simple, Clear Guide
AI Explorer
AI Explorer
Apr 24, 2026 · Artificial Intelligence

Hands‑On Large‑Model Tutorial: From Fine‑Tuning to Security Attacks (34k‑Star Repo)

This article introduces the open‑source "Dive into LLMs" tutorial (34k+ GitHub stars) that offers a complete, hands‑on workflow for large language models—from fine‑tuning and deployment to prompt engineering, knowledge editing, math reasoning, watermarking, and jailbreak security experiments—along with step‑by‑step Jupyter notebooks and easy setup instructions.

AI securityJupyter NotebookLLM tutorial
0 likes · 6 min read
Hands‑On Large‑Model Tutorial: From Fine‑Tuning to Security Attacks (34k‑Star Repo)
Old Zhang's AI Learning
Old Zhang's AI Learning
Apr 19, 2026 · Artificial Intelligence

From Zero to Deployment: A Complete Qwen3.5 Fine‑Tuning Guide

This guide shows how to fine‑tune Qwen3.5 models—from 0.8B to 122B—using Unsloth Studio or pure code, covering text SFT, vision fine‑tuning, MoE models, reinforcement‑learning (GRPO), extensive GGUF quantization benchmarks, hardware requirements, export formats, and deployment tips.

LLMQwen3.5Unsloth
0 likes · 12 min read
From Zero to Deployment: A Complete Qwen3.5 Fine‑Tuning Guide
AI Tech Publishing
AI Tech Publishing
Apr 9, 2026 · Artificial Intelligence

Engineering‑Focused Guide to Training and Inference of Large Language Models

This article walks engineers through the full LLM stack—from tokenization and positional encoding to transformer blocks, efficient fine‑tuning, quantization, and production‑grade inference techniques such as KV‑cache, FlashAttention, PagedAttention, continuous batching, and speculative decoding—highlighting trade‑offs, toolchains, and practical workflow steps.

LLMLoRAQuantization
0 likes · 13 min read
Engineering‑Focused Guide to Training and Inference of Large Language Models
Lao Guo's Learning Space
Lao Guo's Learning Space
Apr 2, 2026 · Artificial Intelligence

Large Model Pretraining and Fine‑Tuning: A 2026 Technical Guide from Scaling Laws to Post‑Training Revolution

This article explains the full lifecycle of large language models in 2026, covering pretraining fundamentals, the limits of classic Scaling Laws, data‑centric advances, fine‑tuning strategies, RLHF, DPO, and the emerging post‑training methods GRPO, DAPO and RLVR, with concrete benchmarks and cost analyses.

DAPODPOGRPO
0 likes · 17 min read
Large Model Pretraining and Fine‑Tuning: A 2026 Technical Guide from Scaling Laws to Post‑Training Revolution
Amazon Cloud Developers
Amazon Cloud Developers
Apr 1, 2026 · Artificial Intelligence

Achieving Pro‑Level Vision Detection with Minimal Cost: Fine‑Tuning Amazon Nova Lite

By fine‑tuning Amazon Nova Lite 1.0 on Amazon Bedrock, the study demonstrates how a small training dataset can dramatically improve instruction following and reduce detection boxes—up to 92% fewer—while achieving Pro‑grade accuracy in aerial group detection and low‑light monitoring, all at a fraction of the cost.

Amazon BedrockAmazon Nova Litecomputer vision
0 likes · 20 min read
Achieving Pro‑Level Vision Detection with Minimal Cost: Fine‑Tuning Amazon Nova Lite
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Mar 28, 2026 · Artificial Intelligence

How to Ace LLM Interview Questions: Deep Dive into Pre‑training, SFT, DPO & RLHF

This guide breaks down the four major large‑model training paradigms—pre‑training, supervised fine‑tuning, preference alignment, and RLHF—explaining which parameters are updated, how attention is reshaped, and what capabilities are gained, so you can deliver a structured, interview‑ready answer.

AI interviewLLMLarge Language Models
0 likes · 8 min read
How to Ace LLM Interview Questions: Deep Dive into Pre‑training, SFT, DPO & RLHF
Black & White Path
Black & White Path
Mar 21, 2026 · Artificial Intelligence

Japan’s ‘Self‑Developed’ 700B AI Model: A DeepSeek Re‑skin Flop

Rakuten AI 3.0 was billed as Japan’s largest, self‑developed 700‑billion‑parameter model backed by government funds, but a quick look at its Hugging Face config reveals it merely re‑uses DeepSeek V3, prompting a broader critique of the hype, funding motives, and strategic trade‑offs behind the launch.

AI Industry AnalysisDeepSeekGovernment funding
0 likes · 5 min read
Japan’s ‘Self‑Developed’ 700B AI Model: A DeepSeek Re‑skin Flop
Didi Tech
Didi Tech
Mar 12, 2026 · Artificial Intelligence

How STAPO Improves Large‑Model Fine‑Tuning by Silencing Spurious Tokens

The STAPO (Spurious‑Token‑Aware Policy Optimization) algorithm, introduced by Tsinghua University's iDLab and Didi's Deep Sea Lab, tackles policy‑entropy instability and performance oscillation in reinforcement‑learning fine‑tuning of large models by mathematically analyzing token collision probability, defining spurious tokens, and applying a Silencing Spurious Tokens mechanism that yields state‑of‑the‑art results on multiple math‑reasoning benchmarks.

AI safetySTAPOfine-tuning
0 likes · 7 min read
How STAPO Improves Large‑Model Fine‑Tuning by Silencing Spurious Tokens
Old Zhang's AI Learning
Old Zhang's AI Learning
Mar 3, 2026 · Artificial Intelligence

How to Deploy and Fine‑Tune Qwen3.5 Small Models (0.8B‑9B) Locally

This guide walks you through deploying Qwen3.5's 0.8B, 2B, 4B and 9B models on CPUs or modest GPUs using Unsloth's GGUF quantization, explains hardware requirements, shows how to run them with llama.cpp, llama‑server, vLLM or SGLang, and provides a free Colab fine‑tuning workflow with export options.

AI modelsGGUFQwen3.5
0 likes · 19 min read
How to Deploy and Fine‑Tune Qwen3.5 Small Models (0.8B‑9B) Locally
Data Party THU
Data Party THU
Mar 1, 2026 · Artificial Intelligence

Unlocking Efficient LLM Fine‑Tuning: LoRA, QLoRA, and DoRA Compared

This article examines three parameter‑efficient fine‑tuning (PEFT) techniques—LoRA, QLoRA, and DoRA—explaining their core mechanisms, providing implementation code, benchmark results, memory and speed trade‑offs, and offering guidance on which method best fits different hardware and accuracy requirements.

DORALoRAPEFT
0 likes · 20 min read
Unlocking Efficient LLM Fine‑Tuning: LoRA, QLoRA, and DoRA Compared
Mingyi World Elasticsearch
Mingyi World Elasticsearch
Feb 26, 2026 · Artificial Intelligence

How RAG Gives Large Language Models Their Own Knowledge Base – Illustrated with Easysearch

The article explains why Retrieval‑Augmented Generation (RAG) is needed to overcome large language models' knowledge cut‑off and hallucination issues, details the offline indexing and online retrieval‑generation workflow, compares RAG with fine‑tuning, and shows how Easysearch’s hybrid search makes an effective RAG backbone.

EasysearchHybrid SearchKnowledge Base
0 likes · 10 min read
How RAG Gives Large Language Models Their Own Knowledge Base – Illustrated with Easysearch
Data STUDIO
Data STUDIO
Feb 25, 2026 · Artificial Intelligence

Build a Large Language Model from Scratch with PyTorch—No Libraries, No Shortcuts

This guide walks you through building, training, and fine‑tuning a Transformer‑based large language model entirely from scratch using PyTorch, covering tokenization, self‑attention, multi‑head attention, positional encoding, model architecture, data preparation, training loops, and fine‑tuning on custom lyrics.

GPTLLMPyTorch
0 likes · 43 min read
Build a Large Language Model from Scratch with PyTorch—No Libraries, No Shortcuts
Qborfy AI
Qborfy AI
Feb 20, 2026 · Artificial Intelligence

Mastering Model Fine‑Tuning: Theory, Workflow, and Real‑World Code

This article explains fine‑tuning as a second‑stage training method that adapts large pre‑trained models to specific tasks, outlines the three‑phase workflow, compares it with prompt engineering and retrieval‑augmented generation, and provides four detailed case studies with complete code snippets and best‑practice tips.

HuggingFaceLarge Language ModelsLoRA
0 likes · 20 min read
Mastering Model Fine‑Tuning: Theory, Workflow, and Real‑World Code
DataFunTalk
DataFunTalk
Feb 11, 2026 · Artificial Intelligence

Why Most RAG Deployments Fail and How to Build a Production‑Ready RAG System

This round‑table dissects the gap between RAG’s hype and real‑world production, exposing common pitfalls such as low recall, hallucinations and cost overruns, and then delivers a systematic diagnostic framework, hybrid search strategies, fine‑tuning rules, and practical best‑practice roadmaps for building reliable enterprise RAG solutions.

Agentic RAGHybrid SearchLLM
0 likes · 20 min read
Why Most RAG Deployments Fail and How to Build a Production‑Ready RAG System
AI Tech Publishing
AI Tech Publishing
Feb 6, 2026 · Artificial Intelligence

2026 Large Model Engineering Roadmap: From Foundations to Production

This roadmap outlines a step‑by‑step learning path for building, optimizing, and safely deploying large language model systems, covering fundamentals, vector stores, RAG, advanced techniques, fine‑tuning, inference speed, deployment, observability, agents, and production safeguards.

AgentsDeploymentLLM
0 likes · 5 min read
2026 Large Model Engineering Roadmap: From Foundations to Production
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Feb 3, 2026 · Artificial Intelligence

Why Loss Masking Is the Hidden Key to Effective LLM Fine‑Tuning

The article explains how loss masking in supervised fine‑tuning of large language models prevents the model from learning irrelevant tokens such as user inputs, system prompts, tool outputs, and padding, thereby focusing training on the assistant’s responses and improving performance and generalization.

AI trainingLLMPrompt engineering
0 likes · 10 min read
Why Loss Masking Is the Hidden Key to Effective LLM Fine‑Tuning
JD Tech
JD Tech
Jan 13, 2026 · Artificial Intelligence

Mastering Large Language Models: Transformers, Scaling Laws, and MoE Explained

This extensive guide walks readers through the fundamentals of large language models, covering transformer architecture, pre‑training and fine‑tuning techniques, scaling laws, emergent abilities, mixture‑of‑experts designs, and practical comparisons, providing clear explanations, code snippets, and visual illustrations for deep learning practitioners.

Mixture of Expertsemergent abilitiesfine-tuning
0 likes · 47 min read
Mastering Large Language Models: Transformers, Scaling Laws, and MoE Explained
PMTalk Product Manager Community
PMTalk Product Manager Community
Jan 8, 2026 · Artificial Intelligence

Understanding Fine‑Tuning: A Primer for AI Product Managers

This article explains how large language models are first pre‑trained on massive text corpora and then fine‑tuned with smaller, task‑specific datasets, covering the fine‑tuning process, types such as full‑parameter and PEFT, practical benefits, real‑world analogies, and key challenges like data quality and catastrophic forgetting.

AI product managementLarge Language ModelsModel Adaptation
0 likes · 6 min read
Understanding Fine‑Tuning: A Primer for AI Product Managers
Open Source Tech Hub
Open Source Tech Hub
Dec 5, 2025 · Artificial Intelligence

From Neurons to GPT: A Complete Timeline of AI Evolution and Future Trends

This comprehensive article traces AI from its biological roots and early computers through the birth of artificial intelligence, the rise of machine learning, the emergence of large language models, multimodal agents, and finally explores current breakthroughs, practical applications, and future directions.

AgentsArtificial IntelligencePrompt engineering
0 likes · 39 min read
From Neurons to GPT: A Complete Timeline of AI Evolution and Future Trends
Frontend AI Walk
Frontend AI Walk
Dec 2, 2025 · Artificial Intelligence

Understanding LLMs: A Frontend Developer’s Primer on Large Language Models

The article demystifies large language models for frontend developers by likening token prediction to autocomplete, explaining tokens, context windows, temperature, the two-stage training process, and the critical role of prompts, using concrete code examples and analogies to familiar frontend concepts.

Frontend AnalogyLLMPrompt engineering
0 likes · 10 min read
Understanding LLMs: A Frontend Developer’s Primer on Large Language Models
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Nov 15, 2025 · Artificial Intelligence

How to Build Robust Function Call Training Data for LLM Agents

This article explains why function call capabilities in large language model agents require dedicated training, outlines the four core abilities to teach, describes the structure and sources of effective training data, and compares lightweight LoRA fine‑tuning with full supervised fine‑tuning approaches.

Agent systemsData GenerationLLM training
0 likes · 11 min read
How to Build Robust Function Call Training Data for LLM Agents
Data Party THU
Data Party THU
Oct 20, 2025 · Artificial Intelligence

Fine-Tuning LLMs on TPU with Tunix: A Step‑by‑Step QLoRA Guide

This article introduces Google’s Tunix library for JAX‑based LLM post‑training, explains its core features such as supervised fine‑tuning, reinforcement learning and knowledge distillation, and provides detailed installation steps and a complete TPU‑accelerated QLoRA fine‑tuning workflow on the Gemma 2B model, including code snippets and inference testing.

AIJAXLLM
0 likes · 8 min read
Fine-Tuning LLMs on TPU with Tunix: A Step‑by‑Step QLoRA Guide
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Oct 13, 2025 · Artificial Intelligence

How Large‑and‑Small Language Model Collaboration Is Shaping the Future

The article argues that combining large, high‑capacity models with lightweight, fine‑tuned small models can cut costs, lower latency, enable specialized vertical tasks, and shift development from chasing ever‑bigger models toward optimal system architectures, outlining key techniques such as state‑space models, knowledge distillation, and staged fine‑tuning.

AI ArchitectureEfficiencyKnowledge Distillation
0 likes · 3 min read
How Large‑and‑Small Language Model Collaboration Is Shaping the Future
Fun with Large Models
Fun with Large Models
Sep 6, 2025 · Artificial Intelligence

How to Build a High-Quality Domain-Specific Fine-Tuning Dataset for Large Models

This article outlines a systematic engineering workflow for creating professional domain fine‑tuning datasets for large models, covering data processing, validation, optimal sample size, industrial‑environment practices, and special considerations for reinforcement‑learning based fine‑tuning.

Data ValidationDataset Constructiondata processing
0 likes · 7 min read
How to Build a High-Quality Domain-Specific Fine-Tuning Dataset for Large Models
Fun with Large Models
Fun with Large Models
Sep 2, 2025 · Artificial Intelligence

How to Improve Agent Performance with Fine‑Tuning: Key Strategies for AI Interviews

This article explains how to boost large‑model agent performance for interview questions by using efficient fine‑tuning—building multi‑tool parallel and chain‑call datasets—and reinforcement‑learning fine‑tuning with reward functions that target tool accuracy, task completion, and call efficiency, illustrated with concrete JSON examples and open‑source references.

AgentFunction Callingdataset
0 likes · 9 min read
How to Improve Agent Performance with Fine‑Tuning: Key Strategies for AI Interviews
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 11, 2025 · Artificial Intelligence

How Fine‑Tuning Large Models Solves Code Upgrade Challenges and Boosts Stable Module Matching

This article details an innovative approach that uses large‑model supervised fine‑tuning to overcome the instability of code RAG and code agents during open‑source repository upgrades, addressing domain‑specific terminology, code style differences, and improving recall, accuracy, and deployment efficiency.

AI agentsLLMRAG
0 likes · 11 min read
How Fine‑Tuning Large Models Solves Code Upgrade Challenges and Boosts Stable Module Matching
Alibaba Cloud Developer
Alibaba Cloud Developer
Jul 31, 2025 · Artificial Intelligence

Why Post‑Training Matters: Scaling Laws, Fine‑Tuning, and RL Strategies for LLMs

This article explores the importance of post‑training for large language models, explains scaling laws for pre‑ and post‑training, details common fine‑tuning methods (full, PEFT, LoRA), outlines alignment techniques such as RLHF, DPO, PPO, and presents practical workflows using Llama 3 and DeepSeek‑R1, while also discussing test‑time reasoning optimizations.

LLMRLHFalignment
0 likes · 19 min read
Why Post‑Training Matters: Scaling Laws, Fine‑Tuning, and RL Strategies for LLMs
AI Algorithm Path
AI Algorithm Path
Jul 15, 2025 · Artificial Intelligence

Day 8: Fine‑Tuning CLIP for Image‑Text Tasks – A Beginner’s Guide

This tutorial walks through fine‑tuning OpenAI's CLIP ViT‑B/32 on a small image‑text dataset in a Kaggle notebook, covering environment setup, model loading, data preprocessing with CLIPProcessor, training a linear head, and observing loss convergence to align visual and textual embeddings.

CLIPHuggingFaceKaggle
0 likes · 5 min read
Day 8: Fine‑Tuning CLIP for Image‑Text Tasks – A Beginner’s Guide
Instant Consumer Technology Team
Instant Consumer Technology Team
Jul 9, 2025 · Artificial Intelligence

How Easy Dataset Automates High‑Quality LLM Fine‑Tuning Data from Unstructured Docs

The article introduces Easy Dataset, a GUI‑driven framework that transforms heterogeneous documents into high‑quality, persona‑driven fine‑tuning data for large language models, details its architecture, core contributions, experimental validation on financial QA, and compares it with existing data‑synthesis tools.

Artificial IntelligenceData SynthesisGUI
0 likes · 12 min read
How Easy Dataset Automates High‑Quality LLM Fine‑Tuning Data from Unstructured Docs
ITFLY8 Architecture Home
ITFLY8 Architecture Home
Jun 24, 2025 · Artificial Intelligence

How Transformers and Mixture-of-Experts Power Large Language Models

This article explores the role of Transformers and Mixture‑of‑Experts in large models, outlines five fine‑tuning methods, compares traditional and agentic RAG, presents classic agent design patterns, text‑chunking strategies, levels of intelligent agent systems, and explains KV‑caching techniques.

Large Language ModelsMixture of ExpertsRAG
0 likes · 2 min read
How Transformers and Mixture-of-Experts Power Large Language Models
Instant Consumer Technology Team
Instant Consumer Technology Team
Jun 17, 2025 · Artificial Intelligence

Mastering Fine‑Tuning Datasets: From Basics to Advanced LLM Techniques

This comprehensive guide explains the importance of fine‑tuning datasets for large language models, covering task classification, dataset formats, supervised and instruction tuning, domain adaptation, multimodal data, and practical code examples to help practitioners build effective training, validation, and test sets.

Instruction TuningLarge Language Modelsdataset preparation
0 likes · 33 min read
Mastering Fine‑Tuning Datasets: From Basics to Advanced LLM Techniques
AI Algorithm Path
AI Algorithm Path
Jun 15, 2025 · Artificial Intelligence

Fine‑Tuning Text Embeddings for Domain‑Specific Search: A Complete Walkthrough

This article explains why generic text‑embedding models often fail in specialized retrieval tasks, then demonstrates how to fine‑tune such models using contrastive learning, curated job‑listing data, and the Sentence‑Transformers library, achieving near‑perfect accuracy on a job‑matching benchmark.

HuggingFaceSentence-Transformerscontrastive learning
0 likes · 11 min read
Fine‑Tuning Text Embeddings for Domain‑Specific Search: A Complete Walkthrough
DaTaobao Tech
DaTaobao Tech
Jun 4, 2025 · Artificial Intelligence

Understanding Large Language Model Architecture, Parameters, Memory, Storage, and Fine‑Tuning Techniques

This article provides a comprehensive overview of large language models (LLMs), covering their transformer architecture, parameter counts, GPU memory and storage requirements, and detailed fine‑tuning methods such as prompt engineering, data construction, LoRA, PEFT, RLHF, and DPO, along with practical deployment and inference acceleration strategies.

DPOLLMLoRA
0 likes · 17 min read
Understanding Large Language Model Architecture, Parameters, Memory, Storage, and Fine‑Tuning Techniques
AsiaInfo Technology: New Tech Exploration
AsiaInfo Technology: New Tech Exploration
Apr 25, 2025 · Artificial Intelligence

How Evidence Generation Boosts Document-Grounded Dialogue with LLMs

This study introduces DGDE, a document‑grounded dialogue framework that leverages large language model‑generated evidence, combining retrieval, reranking, fine‑tuning, and iterative question correction to markedly improve accuracy, comprehensiveness, coherence, and completeness on the Doc2dial benchmark.

Large Language Modelsdocument-grounded dialogueevidence generation
0 likes · 21 min read
How Evidence Generation Boosts Document-Grounded Dialogue with LLMs
Cognitive Technology Team
Cognitive Technology Team
Mar 22, 2025 · Artificial Intelligence

Three Stages of Developing Large Language Models and Practical Guidance

The article outlines the three development phases of large language models—building, pre‑training, and fine‑tuning—describes usage options, highlights key factors such as data scale, architecture, training processes, and evaluation, and offers practical advice for cost‑effective development.

LLMModel Developmentfine-tuning
0 likes · 3 min read
Three Stages of Developing Large Language Models and Practical Guidance
Fun with Large Models
Fun with Large Models
Mar 20, 2025 · Artificial Intelligence

Fine‑Tune DeepSeek‑R1 with Just a Few Lines of Code Using Unsloth

This guide walks through setting up an Anaconda environment, installing Unsloth, downloading the DeepSeek‑R1‑Distill‑Llama‑8B model, preparing a medical CoT dataset, configuring LoRA parameters, running a short fine‑tuning job, and evaluating the customized model with structured prompts.

DeepSeekLoRAPython
0 likes · 18 min read
Fine‑Tune DeepSeek‑R1 with Just a Few Lines of Code Using Unsloth
DaTaobao Tech
DaTaobao Tech
Mar 14, 2025 · Artificial Intelligence

AI-Driven Engineering Efficiency: Practices and Insights from a Live-Streaming Team

The article recounts a live‑streaming team’s six‑month experiment using large‑language‑model AI to boost backend, frontend, testing, data‑science and data‑engineering productivity, detailing goals, LLM strengths and limits, and practical tactics such as task splitting, input refinement, human‑AI guidance, retrieval‑augmented generation and fine‑tuning, while emphasizing disciplined task design, prompt iteration, and future vertical integrations.

AIPrompt engineeringRAG
0 likes · 17 min read
AI-Driven Engineering Efficiency: Practices and Insights from a Live-Streaming Team
Tencent Cloud Developer
Tencent Cloud Developer
Mar 11, 2025 · Artificial Intelligence

Fine‑Tuning Local LLaMA‑Factory Models and Building Networked AI Applications

The article walks through preparing a GPU‑enabled environment, downloading and LoRA‑fine‑tuning a DeepSeek model with LLaMA‑Factory, merging the adapter, then wrapping the model in a web UI that queries a ChromaDB vector store via crawled web data, illustrating security‑focused use cases and forecasting domain‑specific LLM adoption.

AILLMLLaMA-Factory
0 likes · 17 min read
Fine‑Tuning Local LLaMA‑Factory Models and Building Networked AI Applications
Ops Development & AI Practice
Ops Development & AI Practice
Feb 15, 2025 · Artificial Intelligence

How to Efficiently Fine‑Tune Llama 3 on a Free Colab T4 GPU with Unsloth

This article provides a step‑by‑step, code‑rich tutorial for fine‑tuning the open‑source Llama 3 1B and 3B models on Google Colab using the Unsloth library and LoRA, covering environment setup, model loading, adapter insertion, dataset preparation, training configuration, inference, and model saving, all while keeping GPU memory usage low.

AIColabGPU
0 likes · 13 min read
How to Efficiently Fine‑Tune Llama 3 on a Free Colab T4 GPU with Unsloth
vivo Internet Technology
vivo Internet Technology
Feb 12, 2025 · Artificial Intelligence

Bidirectional Optimization of NLLB-200 and ChatGPT for Low-Resource Language Translation

The paper proposes a bidirectional optimization framework that fine‑tunes the low‑resource NLLB‑200 translation model with LoRA using data generated by ChatGPT, while also translating low‑resource prompts with NLLB before feeding them to LLMs, thereby improving multilingual translation quality yet requiring careful validation of noisy synthetic data.

LLMLoRAMachine Translation
0 likes · 28 min read
Bidirectional Optimization of NLLB-200 and ChatGPT for Low-Resource Language Translation
Xiaohongshu Tech REDtech
Xiaohongshu Tech REDtech
Dec 26, 2024 · Artificial Intelligence

Instruction Embedding: Latent Representations of Instructions for Task Identification

The paper introduces Instruction Embedding—a task‑focused text representation learned on the new Instruction Embedding Benchmark—and shows that Prompt‑based Instruction Embedding (PIE) outperforms standard embeddings in clustering, similarity, and downstream tasks such as data selection, in‑context example retrieval, test‑set compression, and task‑correlation analysis.

Large Language Modelscontrastive learningfine-tuning
0 likes · 15 min read
Instruction Embedding: Latent Representations of Instructions for Task Identification
NewBeeNLP
NewBeeNLP
Dec 23, 2024 · Artificial Intelligence

What’s New in Qwen2.5? A Deep Dive into the Latest LLM Advances

The Qwen2.5 Technical Report introduces a new series of large language models with up to 72 B parameters, expanded pre‑training data to 18 trillion tokens, advanced supervised fine‑tuning and reinforcement learning pipelines, and demonstrates strong performance across comprehension, reasoning, coding, and long‑context tasks.

LLMQwen2.5fine-tuning
0 likes · 5 min read
What’s New in Qwen2.5? A Deep Dive into the Latest LLM Advances
Baobao Algorithm Notes
Baobao Algorithm Notes
Dec 15, 2024 · Artificial Intelligence

What Are the Best Practices for Retrieval‑Augmented Generation (RAG)?

This comprehensive study evaluates various components of Retrieval‑Augmented Generation pipelines—including query classification, chunking, embedding models, vector databases, retrieval, re‑ranking, summarization, and generator fine‑tuning—identifies optimal configurations, and proposes best‑practice guidelines for both performance‑maximizing and efficiency‑balanced RAG systems.

LLMRAGRetrieval-Augmented Generation
0 likes · 17 min read
What Are the Best Practices for Retrieval‑Augmented Generation (RAG)?
DevOps
DevOps
Dec 8, 2024 · Artificial Intelligence

Understanding Fine-Tuning in Machine Learning: Concepts, Importance, Steps, and Applications

This article explains fine‑tuning in machine learning, covering its definition, why it matters, the role of pre‑trained models, detailed step‑by‑step procedures, advantages, and diverse applications such as NLP, computer vision, speech and finance, with practical examples like face recognition and object detection.

AI ApplicationsModel Optimizationfine-tuning
0 likes · 16 min read
Understanding Fine-Tuning in Machine Learning: Concepts, Importance, Steps, and Applications
AI Product Manager Community
AI Product Manager Community
Dec 7, 2024 · Artificial Intelligence

How Reinforcement Fine-Tuning (RFT) Is Redefining AI Customization

Reinforcement Fine-Tuning (RFT), unveiled at OpenAI’s 12‑day launch, introduces a feedback‑loop approach that transforms generic models into specialized experts using reinforcement learning, small data, and domain‑specific scorers, offering product managers a powerful tool for rapid, cost‑effective AI customization across industries.

AI customizationProduct Managementfine-tuning
0 likes · 7 min read
How Reinforcement Fine-Tuning (RFT) Is Redefining AI Customization
Data Thinking Notes
Data Thinking Notes
Nov 12, 2024 · Artificial Intelligence

Unlock Data Power with DB‑GPT: An Open‑Source AI Framework for Data Development

DB‑GPT is an open‑source AI‑native data application framework that unifies multi‑model management, RAG, agents, and workflow orchestration to simplify building large‑model‑driven data solutions, offering features such as private Q&A, multi‑source analytics, automated fine‑tuning, and robust privacy security.

AIAgentsData Framework
0 likes · 13 min read
Unlock Data Power with DB‑GPT: An Open‑Source AI Framework for Data Development
Alibaba Cloud Developer
Alibaba Cloud Developer
Nov 1, 2024 · Artificial Intelligence

Fine‑Tune Qwen2‑VL with LLaMA Factory on Alibaba Cloud to Build a Tourism QA Bot

This guide walks you through using Alibaba Cloud's PAI‑DSW service together with the open‑source LLaMA Factory to fine‑tune the multimodal Qwen2‑VL model, set up a tourism‑focused knowledge‑question answering bot, and run inference via the Web UI, while covering environment setup, dataset handling, training parameters, and post‑experiment cleanup.

AIAlibaba CloudLLaMA-Factory
0 likes · 9 min read
Fine‑Tune Qwen2‑VL with LLaMA Factory on Alibaba Cloud to Build a Tourism QA Bot
System Architect Go
System Architect Go
Oct 24, 2024 · Artificial Intelligence

How to Fine‑Tune Translation Models on Kubernetes Docs with LoRA

This article walks through the complete process of fine‑tuning both domain‑specific and large‑language translation models on Kubernetes documentation, covering data preparation, model selection, training configurations, the differences between Seq2Seq and CausalLM, and how LoRA can dramatically reduce resource usage while improving performance.

AILLMLoRA
0 likes · 7 min read
How to Fine‑Tune Translation Models on Kubernetes Docs with LoRA
DataFunSummit
DataFunSummit
Oct 18, 2024 · Artificial Intelligence

Building Efficient RAG Applications with a Small Team: Insights from PingCAP AI Lab

This article details how PingCAP's three‑person AI Lab leveraged Retrieval‑Augmented Generation (RAG) techniques—including basic RAG, fine‑tuned embeddings, re‑ranking, graph RAG, and agent‑based RAG—to create scalable, multilingual document‑question answering services while addressing large‑scale documentation challenges, model limitations, and user feedback loops.

AgentEmbeddingLLM
0 likes · 14 min read
Building Efficient RAG Applications with a Small Team: Insights from PingCAP AI Lab
Baidu Tech Salon
Baidu Tech Salon
Oct 17, 2024 · Artificial Intelligence

How to Deploy Yuan 2.0 LLM with PaddleNLP: A Step‑by‑Step Guide

This article explains how the open‑source Yuan 2.0 large language model is fully integrated with Baidu’s PaddleNLP, covering its capabilities, fine‑tuning optimizations, step‑by‑step deployment instructions, interaction examples, and training/finetuning results with loss‑curve visualizations.

AIPaddleNLPYuan 2.0
0 likes · 10 min read
How to Deploy Yuan 2.0 LLM with PaddleNLP: A Step‑by‑Step Guide
DaTaobao Tech
DaTaobao Tech
Oct 9, 2024 · Artificial Intelligence

Building a Vertical Domain QA Bot with Vector Search, RAG, and SFT

This guide walks entry‑level developers through building a logistics‑focused QA bot by first embedding documents for vector similarity search, then adding retrieval‑augmented generation, fine‑tuning a small model, integrating hybrid checks, and optimizing deployment with feedback loops to achieve fast, accurate, out‑of‑scope‑aware answers.

AIChatbotRAG
0 likes · 15 min read
Building a Vertical Domain QA Bot with Vector Search, RAG, and SFT
Bilibili Tech
Bilibili Tech
Sep 18, 2024 · Artificial Intelligence

Index-1.9B-32K: A 2% GPT-Size Model with Powerful Long-Context Capabilities

Index-1.9B-32K is a 1.9B-parameter model with a 32K token context window, achieving strong long‑text performance comparable to larger models while using only about 2% of GPT‑4’s compute, trained via long pre‑training and supervised fine‑tuning, with a trade‑off of reduced short‑context ability.

AIEvaluationLong Context
0 likes · 12 min read
Index-1.9B-32K: A 2% GPT-Size Model with Powerful Long-Context Capabilities
Huawei Cloud Developer Alliance
Huawei Cloud Developer Alliance
Sep 6, 2024 · Artificial Intelligence

Fine‑Tune Large AI Models on Huawei Cloud in One Minute

This guide explains why fine‑tuning large language models is essential, demonstrates a practical example, and walks developers through selecting a model, uploading a dataset, launching fine‑tuning, deploying the customized model as an online inference service, and validating its performance on Huawei Cloud AI Gallery.

Artificial Intelligencefine-tuning
0 likes · 7 min read
Fine‑Tune Large AI Models on Huawei Cloud in One Minute
Baobao Algorithm Notes
Baobao Algorithm Notes
Aug 27, 2024 · Artificial Intelligence

Unlock Free GLM-4-Flash API: Step-by-Step Guide, Code Samples, and Logic Puzzle Test

This article explores the free GLM-4-Flash API from Zhipu AI, detailing its lightweight architecture, performance specs, a logic‑puzzle demonstration, and provides a comprehensive step‑by‑step tutorial—including data upload, model fine‑tuning, deployment commands and example code for building a LangChain‑based knowledge‑base retrieval system.

AI DeploymentFree APIGLM-4-Flash
0 likes · 11 min read
Unlock Free GLM-4-Flash API: Step-by-Step Guide, Code Samples, and Logic Puzzle Test
NewBeeNLP
NewBeeNLP
Aug 22, 2024 · Artificial Intelligence

How to Fine‑Tune GPT‑4o for Free: Costs, Steps, and Real‑World Benchmarks

OpenAI has launched low‑cost fine‑tuning for GPT‑4o, offering free daily training tokens, a simple dashboard workflow, and early benchmark results that show significant performance gains, while the community debates the merits of fine‑tuning versus prompt‑caching for efficient AI applications.

AI benchmarksGPT-4oOpenAI
0 likes · 6 min read
How to Fine‑Tune GPT‑4o for Free: Costs, Steps, and Real‑World Benchmarks
DaTaobao Tech
DaTaobao Tech
Aug 21, 2024 · Artificial Intelligence

Mastering Custom Large‑Model Training: Data Strategies, LoRA Tricks, and Resource Planning

This article provides a comprehensive, step‑by‑step guide to training customized large language models, covering industry‑specific needs, data privacy, meticulous data cleaning, optimal data‑ratio balancing, token budgeting, GPU memory accounting, LoRA fine‑tuning techniques, and practical evaluation metrics for robust AI deployment.

AI trainingData preprocessingGPU memory
0 likes · 23 min read
Mastering Custom Large‑Model Training: Data Strategies, LoRA Tricks, and Resource Planning
Open Source Tech Hub
Open Source Tech Hub
Jul 31, 2024 · Artificial Intelligence

Understanding LLMs, AI Agents, and Retrieval-Augmented Generation: Key Concepts and Challenges

This article explains the fundamentals of large language models, artificial general intelligence, AI-generated content, AI agents, retrieval‑augmented generation, knowledge bases, multimodal processing, fine‑tuning, alignment, tokens, vectors, and related tools, highlighting their capabilities, limitations, and practical considerations.

AI AgentArtificial IntelligenceLLM
0 likes · 14 min read
Understanding LLMs, AI Agents, and Retrieval-Augmented Generation: Key Concepts and Challenges
JD Tech
JD Tech
Jul 22, 2024 · Artificial Intelligence

Task‑Aware Decoding (TaD): A Plug‑and‑Play Method to Mitigate Hallucinations in Large Language Models

This article presents Task‑aware Decoding (TaD), a plug‑and‑play technique introduced by JD Tech and Tsinghua University and accepted at IJCAI 2024, which reduces intrinsic hallucinations in large language models by comparing pre‑ and post‑fine‑tuning outputs, and demonstrates its effectiveness combined with Retrieval‑Augmented Generation across various tasks.

HallucinationLLMRetrieval-Augmented Generation
0 likes · 18 min read
Task‑Aware Decoding (TaD): A Plug‑and‑Play Method to Mitigate Hallucinations in Large Language Models
Smart Era Software Development
Smart Era Software Development
Jul 3, 2024 · Artificial Intelligence

Deploying Domain Models with Open-Source LLMs: Lessons from SECon 2024

The article analyzes the rapid rise of open‑source large language models, explains how Llama 3 serves as a strong base for domain‑specific models, details a data‑driven pipeline, fine‑tuning, reinforcement learning, engineering optimizations, and a comprehensive evaluation framework, and showcases the XuanYuan series that outperforms GPT‑4 on several finance benchmarks.

Llama 3data pipelinedomain model
0 likes · 12 min read
Deploying Domain Models with Open-Source LLMs: Lessons from SECon 2024
DataFunTalk
DataFunTalk
Jul 2, 2024 · Artificial Intelligence

Application of Large Language Models in Recommendation Systems: Overview and Future Directions

This article provides a comprehensive overview of how large language models (LLMs) are applied in recommendation systems, covering two main paradigms—LLM+RS as a component and LLM as a standalone recommender—detailing their impact on pre‑training, fine‑tuning, prompting, and future research challenges.

Future DirectionsLLMPre‑training
0 likes · 6 min read
Application of Large Language Models in Recommendation Systems: Overview and Future Directions
DataFunTalk
DataFunTalk
Jun 21, 2024 · Artificial Intelligence

Fine‑tuning Large Language Models with Alibaba Cloud PAI: Practices, Techniques, and Deployment

This article introduces the Alibaba Cloud PAI platform for large language model (LLM) fine‑tuning, covering model‑training pipelines, performance‑cost trade‑offs, retrieval‑augmented generation, fine‑tuning methods such as full‑parameter, LoRA and QLoRA, model selection, data preparation, evaluation, and real‑world deployment examples.

AI platformLLMModel Deployment
0 likes · 20 min read
Fine‑tuning Large Language Models with Alibaba Cloud PAI: Practices, Techniques, and Deployment
Practical DevOps Architecture
Practical DevOps Architecture
May 30, 2024 · Artificial Intelligence

Eight‑Week LLM and Large Model Training Course Outline

This article outlines an eight‑week curriculum covering LLM evolution, PyTorch fundamentals, CUDA training, large‑model fine‑tuning, LangChain application development, cloud‑based quantization, industry case studies, and a recruitment session, providing video resources for each topic.

AILLMLangChain
0 likes · 5 min read
Eight‑Week LLM and Large Model Training Course Outline
NewBeeNLP
NewBeeNLP
May 16, 2024 · Artificial Intelligence

How Large Language Models Transform Advertising Copy Generation

This article examines the adoption of large language models for intelligent advertising copy creation, detailing business challenges, model selection criteria, training data preparation, fine‑tuning methods, performance evaluation, deployment results, while highlighting the trade‑offs between model size, cost, and output quality.

AI marketingLarge Language Modelsadvertising copy
0 likes · 20 min read
How Large Language Models Transform Advertising Copy Generation
DataFunSummit
DataFunSummit
May 10, 2024 · Artificial Intelligence

LLMOps: Definition, Fine‑tuning Techniques, Application Architecture, Challenges and Solutions

This article introduces LLMOps by defining large language model operations, explains the three stages of LLM development, details modern fine‑tuning methods such as PEFT, Adapter, Prefix, Prompt and LoRA, outlines the architecture for building LLM applications, discusses the main difficulties of agent‑based deployments, and presents practical solutions including Prompt IDE, low‑code deployment, monitoring and cost control.

AI OperationsLLMOpsModel Deployment
0 likes · 14 min read
LLMOps: Definition, Fine‑tuning Techniques, Application Architecture, Challenges and Solutions
21CTO
21CTO
Apr 29, 2024 · Artificial Intelligence

Fine‑Tuning vs. Context Learning: Building Apps with the Emerging LLM Tech Stack

This article explores how developers can integrate large language models into applications by comparing fine‑tuning and context learning, detailing each method’s advantages and drawbacks, and presenting a four‑layer LLM tech stack—data, model, orchestration, and operations—with practical tooling examples.

AI StackContext LearningLLM
0 likes · 16 min read
Fine‑Tuning vs. Context Learning: Building Apps with the Emerging LLM Tech Stack
JavaEdge
JavaEdge
Apr 22, 2024 · Artificial Intelligence

Why Large Language Models Still Struggle and How to Fix Them

Large language models still suffer from limited memory, constrained context windows, outdated knowledge, inability to control external systems, and poor domain expertise, but the article outlines two main remedies—fine‑tuning (Model‑as‑a‑Service) and prompt‑engineering—detailing their mechanisms, suitable scenarios, and trade‑offs.

Artificial IntelligenceLLMModel as a Service
0 likes · 9 min read
Why Large Language Models Still Struggle and How to Fix Them
DevOps
DevOps
Apr 17, 2024 · Artificial Intelligence

Engineering Capabilities for Enterprise Large Model Applications: Prompt Engineering, RAG, and Fine‑Tuning

The article explores how enterprises can build and improve large‑model applications by combining prompt engineering, retrieval‑augmented generation (RAG), and fine‑tuning, discusses their relationships, optimization dimensions, testing challenges, and provides practical guidance for SE4AI implementation.

AI EngineeringEnterprise AILarge Language Models
0 likes · 20 min read
Engineering Capabilities for Enterprise Large Model Applications: Prompt Engineering, RAG, and Fine‑Tuning
Alimama Tech
Alimama Tech
Apr 17, 2024 · Artificial Intelligence

Applying Large Language Models to Advertising Copy Generation

The article examines how large language models can streamline advertising copy creation by addressing format diversity, creativity, and new media demands, detailing model evaluation, fine‑tuning of Chinese‑adapted LLMs—ultimately selecting QWen 1.5‑7B—and showing that deployment boosts copy quality, click‑through and conversion rates while outlining future personalization and data‑efficient scaling.

AICopy GenerationLLM
0 likes · 18 min read
Applying Large Language Models to Advertising Copy Generation
DaTaobao Tech
DaTaobao Tech
Apr 17, 2024 · Artificial Intelligence

Challenges and Practices of LLM‑Based Knowledge Bases and Personal Assistants

The article examines how LLM‑driven knowledge‑base QA and personal‑assistant agents struggle with context management, token limits, multimodal data, and tool‑parameter parsing, reviews open‑source frameworks such as LangChain, AutoGen and MetaGPT, and argues that fine‑tuning (e.g., LoRA) is essential for domain‑specific, scalable solutions.

AgentKnowledge BaseLLM
0 likes · 11 min read
Challenges and Practices of LLM‑Based Knowledge Bases and Personal Assistants