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.
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.
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.
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.
This article systematically presents ten large‑model training and fine‑tuning methods—from full‑parameter finetuning to parameter‑efficient PEFT—detailing their principles, suitable scenarios, step‑by‑step workflows, code examples, and practical selection guidance for AI product managers.
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.
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.
The article explains how pretraining on massive generic data creates a reusable base model, while fine‑tuning uses smaller, high‑quality task‑specific data to adapt the model, covering objectives, data scale, cost, methods, and why most projects prefer fine‑tuning.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
A comprehensive benchmark fine‑tunes twelve small language models on eight diverse tasks, compares them against a 120B teacher model, and reveals which models excel overall, which are most "plastic" for improvement, and how small models can rival much larger ones.
This article explains how product managers can demystify and apply the three core AI techniques—Retrieval‑Augmented Generation, fine‑tuning, and agents—using the Dify platform, showing step‑by‑step setups, practical benefits, cost considerations, and when to choose each approach.
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.
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.
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.
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.
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.
The article explains how to assess fine‑tuned large models using both human judgment and dataset‑driven metrics, outlines common pitfalls, introduces benchmark datasets and evaluation frameworks, and provides concise answers to related interview questions.
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.
The article explains how to fine‑tune large multimodal models by focusing on the projection layer, optionally using LORA for language‑model adaptation, and highlights data alignment, common applications, and the added difficulty of modality alignment for interview preparation.
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.
The article explains how to estimate GPU memory and overall hardware requirements for fine-tuning and training large dense and MoE models, detailing calculations for full-parameter and LoRA approaches, scaling rules, and hidden costs relevant to interview assessments.
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.
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.
This comprehensive guide walks you through the evolution of AI model architectures—from CNNs and RNNs to Transformers and GANs—explaining their core concepts, typical use cases, and how to select, train, and fine‑tune pre‑trained models using practical code examples.
This article explains post‑training concepts, compares SFT, DPO, and GRPO fine‑tuning methods, and provides step‑by‑step guidance for using Alibaba Cloud's PAI platform—including Model Gallery and DSW—to fine‑tune large language models with code examples and practical tips.
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.
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.
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.
This tutorial walks through LLaMA‑Factory fine‑tuning by explaining how to choose learning rate, epochs, batch size, cutoff length, LoRA rank, and validation split, and shows how to estimate and reduce GPU memory usage with techniques like gradient accumulation, liger_kernel, and DeepSpeed.
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.
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.
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.
This article walks through using LLaMA‑Factory—a unified framework that supports over 100 LLMs—to install dependencies, prepare Alpaca‑style datasets, perform LoRA fine‑tuning, run inference, and export the tuned model, all with concrete command‑line examples.
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.
This article examines why carefully crafted prompts often deliver higher efficiency and lower cost than fine‑tuning large language models, outlining the strengths, pitfalls, and practical decision‑making guidelines for choosing the right approach.
The article analyzes major limitations of large language models—hallucination, outdated knowledge, and insufficient domain expertise—and explains how Retrieval‑Augmented Generation and various fine‑tuning strategies can address these issues while outlining practical cost considerations.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
The article breaks down multiple practical approaches for data preparation, training code handling, and experiment analysis in large‑language‑model fine‑tuning, showing how deeper engagement in each step can boost personal expertise even when final model performance appears similar.
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.
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.
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.
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.
This article explains how to use a large language model to automatically convert long‑form texts into Alpaca‑style question‑answer pairs, build a LangChain processing chain, and then fine‑tune a model such as Phi‑3‑mini‑4k‑instruct with LoRA, providing full Python code examples.
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.
This article explores JD Retail's AI‑driven multi‑agent system that mimics real‑world merchant decision processes, detailing the ReAct paradigm, agent roles, workflow, training methods, monitoring, and future directions for building intelligent e‑commerce assistants.
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.
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.
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.
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.
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.
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.
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.
This article explains the concept, motivations, and step‑by‑step workflow for fine‑tuning large language models—specifically Qwen‑14B—covering data preparation, training commands with DeepSpeed, hyper‑parameter settings, evaluation, and deployment via FastChat, all illustrated with code snippets and configuration details.
This article reviews the common business and technical architectures used in large language model (LLM) applications, explains AI Embedded, AI Copilot, and AI Agent modes—including single‑ and multi‑agent systems—and offers guidance on selecting appropriate technology stacks such as prompt‑only, function‑calling agents, RAG, and fine‑tuning.
This article reviews a recent study that bridges the knowledge gap between large language models and recommendation systems by generating natural‑language auxiliary tasks, fine‑tuning the models, and achieving notable performance gains on Amazon domain benchmarks.
This tutorial walks through fine‑tuning Shanghai AI Lab’s open‑source InternLM models with XTuner, explaining chat‑format conventions, loading and inference (including multimodal InternLM‑XComposer), dataset preparation, configuration sections, DeepSpeed acceleration, and memory‑efficient QLoRA details for 7‑B‑parameter chat models.
This article explains why current AI cannot achieve self‑awareness, outlines data‑science steps for large models—including preprocessing, exploratory analysis, modeling, and evaluation—then surveys general and vertical applications of large language models and details a complete machine‑learning workflow with transformer fine‑tuning techniques.
Sequoia Capital’s “Generative AI Act Two” report highlights a shift from hype‑driven model releases to user‑centric, end‑to‑end solutions, emphasizing the rise of foundational models as components, the importance of developer tools, emerging RAG and fine‑tuning techniques, and the evolving competitive landscape.
This article introduces Alibaba Cloud's open‑source Qwen1.5 large language model series, highlights its multilingual, human‑preference alignment, and long‑context capabilities, and provides step‑by‑step guidance on using PAI‑QuickStart for model deployment, fine‑tuning, and Python SDK integration.
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.
