Fine-tuning | BestHub

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

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.

DPOFine-tuningLLM

0 likes · 17 min read

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

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.

Artificial IntelligenceFine-tuningLLM

0 likes · 3 min read

Three Stages of Developing Large Language Models and Practical Guidance

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.

AIFine-tuningPrompt Engineering

0 likes · 17 min read

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

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.

AIFine-tuningLLM

0 likes · 17 min read

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

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.

Fine-tuningLLMLoRA

0 likes · 28 min read

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

Big Data Technology Architecture

Feb 9, 2025 · Artificial Intelligence

Reproducing Deepseek RI Reasoning Ability with GRPO on Qwen2.5‑7B in Colab

This article explains how to replicate Deepseek RI's slow‑thinking inference using the GRPO reinforcement‑learning algorithm on the Qwen2.5‑7B model in a free Colab notebook, covering the underlying COT concept, reward‑function design, data preparation, training configuration, and observed results.

DeepSeekFine-tuningGRPO

0 likes · 14 min read

Reproducing Deepseek RI Reasoning Ability with GRPO on Qwen2.5‑7B in Colab

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.

Fine-tuningcontrastive learninginstruction embedding

0 likes · 15 min read

Instruction Embedding: Latent Representations of Instructions for Task Identification

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 applicationsFine-tuningPretraining

0 likes · 16 min read

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

ZhongAn Tech Team

Nov 16, 2024 · Artificial Intelligence

Weekly AI Digest Issue 2: Video Generation, Large Models, AGI, and LoRA Fine‑Tuning

This weekly AI roundup discusses emerging video generation tools like PixelDance and Vidu 1.5, debates on scaling limits of large models, AGI geopolitical considerations, and a MIT study comparing LoRA with full fine‑tuning for domain adaptation.

AGIAIFine-tuning

0 likes · 8 min read

Weekly AI Digest Issue 2: Video Generation, Large Models, AGI, and LoRA Fine‑Tuning

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.

Fine-tuningLLMRAG

0 likes · 14 min read

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

System Architect Go

Oct 17, 2024 · Artificial Intelligence

Running and Fine‑Tuning Large Language Models Locally with Ollama, Docker, and Cloud Resources

The author chronicles the challenges and solutions of running large language models locally using Ollama, experimenting with cloud GPUs on Google Colab, managing Python dependencies through Docker, and ultimately fine‑tuning a small Qwen model, providing a practical guide for AI enthusiasts.

DockerFine-tuningGoogle Colab

0 likes · 6 min read

Running and Fine‑Tuning Large Language Models Locally with Ollama, Docker, and Cloud Resources

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.

AIChatbotFine-tuning

0 likes · 15 min read

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

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.

AIFine-tuningPretraining

0 likes · 12 min read

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

DataFunSummit

Aug 27, 2024 · Artificial Intelligence

Applying Large Models to Xiao AI Assistant: Intent Routing, Understanding, and Response Generation

This article presents a comprehensive technical overview of how large language models are integrated into Xiaomi's Xiao AI assistant, detailing the architecture for intent routing, domain‑specific intent understanding, function‑calling mechanisms, fine‑tuning strategies, performance gains, and future research directions.

AI AssistantFine-tuningNLP

0 likes · 14 min read

Applying Large Models to Xiao AI Assistant: Intent Routing, Understanding, and Response Generation

DeWu Technology

Aug 19, 2024 · Artificial Intelligence

Multi‑LoRA Deployment for Large Language Models: Concepts, Fine‑tuning, and Cost‑Effective Strategies

The article introduces a multi‑LoRA strategy that lets many scenario‑specific adapters share a single base LLM, dramatically cutting GPU usage and cost while preserving performance, and explains how to fine‑tune with LoRA, merge adapters, and serve them efficiently using VLLM.

Fine-tuningLoRAModel Deployment

0 likes · 10 min read

Multi‑LoRA Deployment for Large Language Models: Concepts, Fine‑tuning, and Cost‑Effective Strategies

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.

Artificial IntelligenceFine-tuningLLM

0 likes · 18 min read

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

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.

Artificial IntelligenceFine-tuningFuture Directions

0 likes · 6 min read

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

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 PlatformFine-tuningLLM

0 likes · 20 min read

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

JD Tech

Jun 19, 2024 · Artificial Intelligence

Advances in Large AI Models: Prompt Engineering, RAG, Agents, Fine‑Tuning, Vector Databases and Knowledge Graphs

This article surveys the rapid expansion of large AI models, covering prompt engineering, structured prompts, retrieval‑augmented generation, AI agents, fine‑tuning strategies, vector database technology, knowledge graphs, function calling, and their collective role in moving toward artificial general intelligence.

AIFine-tuningPrompt Engineering

0 likes · 23 min read

Advances in Large AI Models: Prompt Engineering, RAG, Agents, Fine‑Tuning, Vector Databases and Knowledge Graphs