instruction tuning

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.

Fine-tuningdataset preparationinstruction tuning

0 likes · 33 min read

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

Architect

Feb 11, 2025 · Artificial Intelligence

DeepSeek: Training Process, Working Principles, and Recent Innovations

The article explains DeepSeek's two‑stage training pipeline—including massive pre‑training on trillions of tokens and post‑training via instruction tuning and reinforcement learning from human feedback—describes the differences between its V3 instruction model and R1 reasoning model, and highlights performance optimizations and emerging research directions.

AIDeepSeekPretraining

0 likes · 8 min read

DeepSeek: Training Process, Working Principles, and Recent Innovations

Bilibili Tech

Jun 14, 2024 · Artificial Intelligence

Technical Report on the Index-1.9B Series: Model Variants, Pre‑training Optimizations, and Alignment Experiments

The report presents the open‑source Index‑1.9B family—base, pure, chat, and character variants—detailing benchmark results, pre‑training optimizations such as a normalized LM‑Head and deeper‑slim architectures, the importance of modest instruction data, alignment via SFT/DPO, role‑play enhancements with RAG, and acknowledges remaining safety and factual limitations.

LLMPretrainingalignment

0 likes · 15 min read

Technical Report on the Index-1.9B Series: Model Variants, Pre‑training Optimizations, and Alignment Experiments

DataFunSummit

May 23, 2024 · Artificial Intelligence

GraphGPT: Enabling Large Language Models as Zero‑Shot Graph Learners

GraphGPT integrates large language models with graph neural networks by introducing graph tokens and instruction tuning, enabling zero‑shot graph learning for tasks such as node classification and link prediction, and demonstrates superior performance and generalization across supervised and zero‑shot benchmarks.

Graph Neural NetworksGraphGPTZero-shot Learning

0 likes · 15 min read

GraphGPT: Enabling Large Language Models as Zero‑Shot Graph Learners

Sohu Tech Products

Mar 20, 2024 · Artificial Intelligence

Comparison of Base LLM and Instruction Tuned LLM

The diagram contrasts a Base LLM, which merely predicts the next word from training data and can continue stories or answer simple facts but may generate unsafe text, with an Instruction‑Tuned LLM that is fine‑tuned via RLHF to understand and follow commands, delivering more accurate, useful, and safe responses.

AIAI applicationsBASE model

0 likes · 7 min read

Comparison of Base LLM and Instruction Tuned LLM

Sohu Tech Products

Oct 11, 2023 · Artificial Intelligence

EcomGPT: Training an E-commerce Domain Large Language Model via Instruction Tuning

EcomGPT, an Alibaba‑trained e‑commerce large language model, uses a 1.5 million‑sample instruction dataset (EcomInstruct) to demonstrate that domain‑specific instruction tuning dramatically outperforms general‑purpose models on e‑commerce tasks, reducing hallucinations and improving task accuracy, with performance scaling as data diversity increases.

Alibaba NLPDomain-Specific AIE-commerce NLP

0 likes · 7 min read

EcomGPT: Training an E-commerce Domain Large Language Model via Instruction Tuning

Rare Earth Juejin Tech Community

Jul 30, 2023 · Artificial Intelligence

Understanding Codex: Training Framework, Evaluation Methodology, and Model Performance in ChatGPT’s Code Generation Ability

This article explains how Codex, built on the GPT‑3.5 architecture, is trained and fine‑tuned to give ChatGPT the ability to generate code, detailing the data collection, supervised fine‑tuning, evaluation using HumanEval and the pass@k metric, and presenting performance comparisons with GPT‑3 and Codex‑S.

AI model trainingChatGPTCodex

0 likes · 11 min read

Understanding Codex: Training Framework, Evaluation Methodology, and Model Performance in ChatGPT’s Code Generation Ability

IT Architects Alliance

Apr 20, 2023 · Artificial Intelligence

Overview of Prominent Large Language Models and Instruction‑Finetuned Variants

This article provides a comprehensive overview of major large language models—including GPT series, T5, LaMDA, LLaMA, BLOOM, and others—detailing their architectures, parameter scales, open‑source status, and the evolution of instruction‑fine‑tuning techniques that improve zero‑shot and few‑shot performance.

AI researchLLM comparisoninstruction tuning

0 likes · 24 min read

Overview of Prominent Large Language Models and Instruction‑Finetuned Variants

Architect

Apr 14, 2023 · Artificial Intelligence

Overview of Prominent Large Language Models and Instruction Fine‑Tuning Techniques

The article surveys major large language models—including GPT‑3, T5, LaMDA, Jurassic‑1, MT‑NLG, Gopher, Chinchilla, PaLM, U‑PaLM, OPT, LLaMA, BLOOM, GLM‑130B, and ERNIE 3.0 Titan—explains their architectures, scaling trade‑offs, and then details instruction‑fine‑tuned variants such as T0, FLAN, GPT‑3.5, ChatGPT, GPT‑4, Alpaca and ChatGLM, providing references for further study.

AIChatGPTGPT-3

0 likes · 27 min read

Overview of Prominent Large Language Models and Instruction Fine‑Tuning Techniques

Architecture Digest

Feb 17, 2023 · Artificial Intelligence

Analyzing the Emergent Abilities of ChatGPT and the Technical Roadmap of GPT‑3.5

This article dissects how ChatGPT acquired its surprising capabilities by tracing the evolution from the original GPT‑3 model through instruction tuning, code‑based pre‑training, and reinforcement learning from human feedback, ultimately presenting a comprehensive technical roadmap for reproducing GPT‑3.5‑scale models.

ChatGPTEmergent AbilitiesGPT-3.5

0 likes · 26 min read

Analyzing the Emergent Abilities of ChatGPT and the Technical Roadmap of GPT‑3.5

IT Architects Alliance

Feb 9, 2023 · Artificial Intelligence

Analyzing the Evolution and Emergent Abilities of GPT‑3.5 Models

This article examines how OpenAI's GPT‑3.5 series evolved from the original GPT‑3 through large‑scale pre‑training, instruction tuning, code training, and RLHF, detailing the origins of language generation, world knowledge, in‑context learning, code understanding, complex reasoning, and the trade‑offs introduced by alignment.

Code TrainingEmergent AbilitiesGPT-3.5

0 likes · 25 min read

Analyzing the Evolution and Emergent Abilities of GPT‑3.5 Models

Top Architect

Feb 8, 2023 · Artificial Intelligence

A Technical Roadmap of GPT‑3.5: From Pre‑training to RLHF and Emerging Capabilities

This article analyses how ChatGPT and the GPT‑3.5 series evolved from the original GPT‑3 through large‑scale pre‑training, code‑based training, instruction tuning, and reinforcement learning from human feedback, identifying the origins of their language generation, in‑context learning, world knowledge, code understanding, chain‑of‑thought reasoning, and alignment capabilities while also outlining current limitations.

ChatGPTEmergent AbilitiesGPT-3.5

0 likes · 27 min read

A Technical Roadmap of GPT‑3.5: From Pre‑training to RLHF and Emerging Capabilities

Architect's Guide

Dec 9, 2022 · Artificial Intelligence

Technical Principles and Training Process of ChatGPT

The article explains how ChatGPT builds on the GPT‑3.5 large language model, using human‑annotated data and Reinforcement Learning from Human Feedback (RLHF) across three training stages to improve instruction understanding, answer quality, and continual model enhancement, while also discussing its potential to complement or replace traditional search engines.

AIChatGPTRLHF

0 likes · 15 min read

Technical Principles and Training Process of ChatGPT