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Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Feb 18, 2024 · Artificial Intelligence

Llama 2: Open Foundation and Fine‑Tuned Chat Models – Overview and Technical Details

The article provides a comprehensive overview of Meta’s Llama 2 series, detailing model sizes, pre‑training data, architectural enhancements, supervised fine‑tuning, RLHF procedures, safety evaluations, reward‑model training, and iterative improvements, highlighting its open‑source release and comparative performance.

AI safetyLarge Language ModelLlama2
0 likes · 27 min read
Llama 2: Open Foundation and Fine‑Tuned Chat Models – Overview and Technical Details
DeWu Technology
DeWu Technology
Jan 22, 2024 · Artificial Intelligence

How to Integrate Business Systems with LLMs: Prompt, RAG, and Fine‑Tuning Strategies

This article outlines three practical approaches—direct prompting, retrieval‑augmented generation (RAG), and fine‑tuning—to connect enterprise applications to large language models, explains key prompt‑engineering techniques, details RAG workflow and vector‑database integration, and provides step‑by‑step guidance for fine‑tuning on the KubeAI platform.

AI for businessKubeAILLM integration
0 likes · 20 min read
How to Integrate Business Systems with LLMs: Prompt, RAG, and Fine‑Tuning Strategies
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Jan 12, 2024 · Artificial Intelligence

Deploy and Fine‑Tune Mixtral‑8x7B on Alibaba Cloud PAI: A Step‑by‑Step Guide

This guide introduces the open‑source Mixtral‑8x7B large language model, explains its architecture and performance, and provides detailed instructions for using Alibaba Cloud PAI‑QuickStart to deploy, invoke via API or SDK, and fine‑tune the model with LoRA on Lingjun GPU resources.

Alibaba Cloud PAIMixtralModel Deployment
0 likes · 16 min read
Deploy and Fine‑Tune Mixtral‑8x7B on Alibaba Cloud PAI: A Step‑by‑Step Guide
Sohu Tech Products
Sohu Tech Products
Dec 27, 2023 · Artificial Intelligence

OCR-Based Video Review System: Technology Selection, Optimization, and Model Fine-Tuning

An OCR‑based video review system using PaddleOCR’s DB detector and SVTR recognizer, combined with multi‑level frame deduplication, message‑queue task decoupling, Redis prioritization, and dynamic thread‑pool scheduling, was fine‑tuned on 5 000 samples to cut daily frames from 794 million to 3.6 million, achieving automated detection of over 230 abnormal videos per day and replacing three manual reviewers, with future plans for GPU acceleration and cross‑instance GRPC dispatch.

AIModel selectionOCR
0 likes · 20 min read
OCR-Based Video Review System: Technology Selection, Optimization, and Model Fine-Tuning
DataFunSummit
DataFunSummit
Nov 13, 2023 · Artificial Intelligence

SWIFT: A Scalable Light‑Weight Training and Inference Framework for Efficient Model Fine‑Tuning

SWIFT is an open‑source, PyTorch‑based framework that integrates multiple efficient fine‑tuning methods such as LoRA, QLoRA, Adapter, and the proprietary ResTuning, enabling developers to fine‑tune large language and multimodal models on consumer‑grade GPUs with significantly reduced memory and compute requirements.

LoRAModelScopePyTorch
0 likes · 13 min read
SWIFT: A Scalable Light‑Weight Training and Inference Framework for Efficient Model Fine‑Tuning
Baobao Algorithm Notes
Baobao Algorithm Notes
Nov 7, 2023 · Artificial Intelligence

A Complete Technical Guide to LLM Foundations, Advanced Topics, Fine‑Tuning, and LangChain Applications

This article provides an in‑depth technical overview of large language models (LLMs), covering core model families, architectural differences, emergent abilities, common challenges such as repetition and token limits, detailed fine‑tuning strategies including PEFT, practical guidance for training custom models, and a thorough introduction to the LangChain framework with code examples, core concepts, and troubleshooting tips for building LLM‑powered applications.

LLMLangChainVector Store
0 likes · 97 min read
A Complete Technical Guide to LLM Foundations, Advanced Topics, Fine‑Tuning, and LangChain Applications
UCloud Tech
UCloud Tech
Sep 11, 2023 · Artificial Intelligence

Build a Soul‑Healing Chatbot with LangChain & Llama 2: A Step‑by‑Step Guide

This article walks through constructing a domain‑specific, soul‑healing chatbot using LangChain and Llama 2, comparing fine‑tuning versus external knowledge bases, detailing environment setup, data loading, text splitting, embedding with a Chinese model, vector store creation, prompt engineering, inference, and optimization strategies.

Knowledge BaseLangChainLlama-2
0 likes · 14 min read
Build a Soul‑Healing Chatbot with LangChain & Llama 2: A Step‑by‑Step Guide
AI Large Model Application Practice
AI Large Model Application Practice
Sep 6, 2023 · Artificial Intelligence

Prompt Engineering vs Fine‑Tuning: How to Choose the Best Strategy for Reliable LLM Outputs

This article compares Prompt Engineering and Supervised Fine‑Tuning for large language models, explains their principles, showcases common prompt patterns such as Chain‑of‑Thought, ReAct and Self‑Ask, outlines fine‑tuning stages and trade‑offs, and provides practical guidance on selecting the most suitable approach for specific enterprise AI Agent scenarios.

AI AgentLLMLarge Language Model
0 likes · 17 min read
Prompt Engineering vs Fine‑Tuning: How to Choose the Best Strategy for Reliable LLM Outputs
JD Tech
JD Tech
Jul 31, 2023 · Artificial Intelligence

Local Deployment, Fine‑tuning, and Inference of the Open‑source Alpaca‑LoRA Model on GPU Servers

This article details the step‑by‑step process of installing GPU drivers, setting up a Python environment, deploying the open‑source Alpaca‑LoRA large language model, fine‑tuning it with Chinese data on a multi‑GPU server, and running inference, while discussing practical challenges and performance observations.

AlpacaGPULarge Language Model
0 likes · 14 min read
Local Deployment, Fine‑tuning, and Inference of the Open‑source Alpaca‑LoRA Model on GPU Servers
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Jul 30, 2023 · Artificial Intelligence

ChatGPT Technical Analysis Series – Part 2: GPT1, GPT2, and GPT3 (Encoder vs Decoder, Zero‑Shot, and Scaling)

This article reviews the evolution of the GPT family from GPT‑1 to GPT‑3, comparing encoder‑decoder architectures, explaining the shift from supervised fine‑tuning to zero‑shot and few‑shot learning, and highlighting the architectural and training innovations that enabled large‑scale language models.

GPTLLMTransformer
0 likes · 13 min read
ChatGPT Technical Analysis Series – Part 2: GPT1, GPT2, and GPT3 (Encoder vs Decoder, Zero‑Shot, and Scaling)
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Jul 29, 2023 · Artificial Intelligence

Getting Started with GPT: How Generative Pre‑Training and Discriminative Fine‑Tuning Work

This article explains GPT's two‑stage learning—unsupervised generative pre‑training on large raw corpora followed by discriminative fine‑tuning on labeled tasks—detailing the underlying Transformer decoder architecture, loss functions, and task‑specific input transformations.

GPTGenerative Pre‑TrainingNLP
0 likes · 5 min read
Getting Started with GPT: How Generative Pre‑Training and Discriminative Fine‑Tuning Work
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Jul 25, 2023 · Artificial Intelligence

Fine‑Tune and Deploy Llama 2 on Alibaba Cloud PAI in Minutes

This guide walks you through using Meta's open‑source Llama 2 models on Alibaba Cloud's PAI platform, covering low‑code LoRA fine‑tuning, full‑parameter fine‑tuning with PAI‑DSW, and rapid WebUI deployment via PAI‑EAS, complete with step‑by‑step instructions, code snippets, and resource requirements.

AIAlibaba CloudLlama2
0 likes · 16 min read
Fine‑Tune and Deploy Llama 2 on Alibaba Cloud PAI in Minutes
Baobao Algorithm Notes
Baobao Algorithm Notes
Jul 23, 2023 · Artificial Intelligence

Why Cold Starts, Reward Hacking, and Evaluation Matter in LLM Training

The article analyzes key challenges in large‑language‑model pipelines—including the necessity of cold‑start pretraining, the pitfalls of reward‑model hacking, efficiency‑effectiveness trade‑offs, evaluation difficulties, and downstream fine‑tuning limits—offering practical insights for more reliable LLM development.

EfficiencyLLMRLHF
0 likes · 9 min read
Why Cold Starts, Reward Hacking, and Evaluation Matter in LLM Training
Cloud Native Technology Community
Cloud Native Technology Community
Jun 28, 2023 · Artificial Intelligence

Building and Deploying Custom Large Language Models with Alauda Cloud‑Native MLOps

This article explains how enterprises can use the Alauda MLOps platform to quickly set up, fine‑tune, and deploy private large language models on cloud‑native infrastructure, covering notebook preparation, GPU allocation, model download, inference service creation, distributed training pipelines, and Docker image building.

AILarge Language ModelMLOps
0 likes · 9 min read
Building and Deploying Custom Large Language Models with Alauda Cloud‑Native MLOps
DataFunTalk
DataFunTalk
Jun 23, 2023 · Artificial Intelligence

DeepKE-LLM: An Open‑Source Large Language Model Toolkit for Knowledge Extraction

DeepKE-LLM is an open‑source, extensible knowledge‑graph extraction framework that leverages large language models for entity, relation, and attribute extraction, supports multiple LLMs, provides installation scripts, various usage modes, fine‑tuning pipelines, and integrates with the KnowLM project for advanced instruction‑following capabilities.

DeepKELLMPrompt Engineering
0 likes · 8 min read
DeepKE-LLM: An Open‑Source Large Language Model Toolkit for Knowledge Extraction
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Jun 12, 2023 · Artificial Intelligence

Comprehensive Guide to Using OpenAI APIs: Models, Prompts, Embeddings, Fine‑Tuning, LangChain, and Multimodal Applications

This article provides a detailed, step‑by‑step tutorial on OpenAI’s language models, API endpoints, prompt engineering, embeddings, moderation, fine‑tuning, LangChain workflows, memory management, and multimodal capabilities such as audio transcription and image generation, complete with code examples and practical usage tips.

APIEmbeddingLangChain
0 likes · 45 min read
Comprehensive Guide to Using OpenAI APIs: Models, Prompts, Embeddings, Fine‑Tuning, LangChain, and Multimodal Applications
JD Retail Technology
JD Retail Technology
May 18, 2023 · Artificial Intelligence

Local Deployment, Inference, and Fine‑tuning of the Vicuna‑7B Large Language Model

This article details the step‑by‑step process of preparing the environment, merging weights, installing dependencies, running inference, evaluating Vicuna‑7B against other models, and attempting fine‑tuning, while highlighting performance results, encountered issues, and future work for large language model deployment.

GPULarge Language ModelModel Deployment
0 likes · 11 min read
Local Deployment, Inference, and Fine‑tuning of the Vicuna‑7B Large Language Model
Top Architect
Top Architect
Apr 21, 2023 · Artificial Intelligence

Fine‑Tuning LLaMA‑7B with Alpaca‑LoRA to Build a Chinese ChatGPT

This article explains why and how to fine‑tune the LLaMA‑7B model using the cheap Alpaca‑LoRA approach, covering hardware requirements, dataset preparation, LoRA training, optional model merging and quantization, and provides ready‑to‑run code snippets for single‑ and multi‑GPU setups.

Alpaca-LoRAGPULLM
0 likes · 10 min read
Fine‑Tuning LLaMA‑7B with Alpaca‑LoRA to Build a Chinese ChatGPT
ELab Team
ELab Team
Sep 23, 2022 · Artificial Intelligence

Fine‑Tune a Chinese BERT Model for Cloze Tasks in 30 Minutes

This tutorial walks you through NLP fundamentals, the evolution of BERT, the concept of pre‑trained models, and a step‑by‑step guide to fine‑tune a Chinese BERT on a cloze‑style task, complete with code snippets and verification results.

BERTChineseCloze Task
0 likes · 13 min read
Fine‑Tune a Chinese BERT Model for Cloze Tasks in 30 Minutes
DataFunTalk
DataFunTalk
Jun 30, 2022 · Artificial Intelligence

OBERT: A Billion‑Parameter Pretrained Language Model for Large‑Scale NLP Applications

The OPPO XiaoBu team introduced OBERT, a series of 100M‑, 300M‑, and 1B‑parameter pretrained language models that leverage massive TB‑scale corpora, multi‑granular masking, retrieval‑augmented training, and distributed acceleration to achieve state‑of‑the‑art results on CLUE and KgCLUE benchmarks while enabling efficient industrial deployment.

Knowledge augmentationLarge Language ModelNLP
0 likes · 12 min read
OBERT: A Billion‑Parameter Pretrained Language Model for Large‑Scale NLP Applications
Code DAO
Code DAO
May 19, 2022 · Artificial Intelligence

Semi‑Supervised Training Methods for Transformers

This article explains an end‑to‑end semi‑supervised training pipeline for Transformer‑based NLP models, detailing the unsupervised language‑model pre‑training, supervised fine‑tuning, and the internal architecture of embeddings, encoder layers, and downstream tasks such as text classification and NER.

BERTMasked Language ModelNLP
0 likes · 9 min read
Semi‑Supervised Training Methods for Transformers
Sohu Tech Products
Sohu Tech Products
Nov 4, 2020 · Artificial Intelligence

Understanding BERT: Architecture, Pre‑training, Fine‑tuning and Applications in Modern NLP

This article provides a comprehensive overview of BERT and related NLP advances, covering its historical context, model architecture, input‑output mechanisms, comparisons with CNNs, word‑embedding evolution, pre‑training strategies like MLM and next‑sentence prediction, and practical guidance for fine‑tuning and feature extraction.

BERTNLPTransformer
0 likes · 17 min read
Understanding BERT: Architecture, Pre‑training, Fine‑tuning and Applications in Modern NLP
Xueersi Online School Tech Team
Xueersi Online School Tech Team
Jan 17, 2020 · Artificial Intelligence

Fine‑tuning BERT for Sentence Pair Similarity in an Online Education Platform

This article describes how a BERT‑based model is fine‑tuned to compute sentence‑pair similarity for improving recommendation accuracy in an online school, detailing the architecture, training mechanisms, code implementation, experimental results, and future extensions such as sentiment analysis.

BERTChinese NLPdeep learning
0 likes · 20 min read
Fine‑tuning BERT for Sentence Pair Similarity in an Online Education Platform
Amap Tech
Amap Tech
Jan 3, 2020 · Artificial Intelligence

Machine Learning Solutions for User Feedback Intelligence at Amap (Gaode Maps)

Amap replaced its rule‑based feedback pipeline with a three‑stage, LSTM‑driven system that combines word2vec embeddings and structured fields, achieving over 96% classification accuracy, cutting manual workload by 80%, and slashing per‑task costs while enabling scalable, data‑driven map quality improvements.

Gaode MapsLSTMMachine Learning
0 likes · 14 min read
Machine Learning Solutions for User Feedback Intelligence at Amap (Gaode Maps)
DataFunTalk
DataFunTalk
Nov 24, 2018 · Artificial Intelligence

Comprehensive Guide to Fine‑Tuning BERT on Chinese Datasets

This article provides a step‑by‑step guide for fine‑tuning Google’s open‑source BERT on Chinese datasets, covering model download, processor customization, code examples, training commands, and insights into the underlying TensorFlow estimator architecture and deployment considerations.

BERTChinese NLPTensorFlow
0 likes · 11 min read
Comprehensive Guide to Fine‑Tuning BERT on Chinese Datasets