This guide explains how to use ModelScope’s trainer components to fine‑tune a pretrained backbone for text classification, covering dataset loading, configuration modification, trainer construction, training, evaluation, prediction, and checkpoint management with concrete code examples.
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.
This guide walks AI developers through downloading the Mixtral 8x7B MOE large language model, fine‑tuning it with Swift or Deepspeed on Alibaba Cloud PAI‑DSW, testing inference with Transformers, and finally deploying the tuned model as an online service using PAI‑EAS.
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.
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.
This guide walks you through preparing the environment, downloading Qwen‑72B‑Chat, performing LoRA fine‑tuning on PAI‑DSW, merging weights, and deploying the model for offline inference, web UI, API, and PAI SDK services on Alibaba Cloud.
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.
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.
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.
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.
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.
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.
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.
This guide walks you through using Alibaba Cloud's PAI platform to quickly fine‑tune Llama 2 with LoRA or full‑parameter methods, deploy the models as online inference services, and launch an interactive WebUI, covering preparation, data formatting, training jobs, and deployment details.
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.
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.
OpenAI has opened its GPT‑4 API, along with GPT‑3.5 Turbo, DALL·E and Whisper APIs, to all paying customers, announced fine‑tuning tests, a new 8K‑token context, upcoming model retirements, and plans to expand access to new developers by month‑end.
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.
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.
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.
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.
This guide details the end‑to‑end process of installing GPU drivers, setting up a Python environment, deploying the open‑source Alpaca‑LoRA model, fine‑tuning it with Chinese data on a multi‑GPU server, and performing inference, while highlighting practical challenges and performance observations.
This tutorial walks through the complete process of fine‑tuning a Vision Transformer (ViT) model for 14‑class flower image classification on ModelScope, covering dataset preparation, model loading, training configuration, evaluation, and inference with practical code examples.
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.
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.
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.
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.
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.
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.
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.
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.
