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Data Party THU
Data Party THU
May 20, 2026 · Artificial Intelligence

How Introspection Adapters Enable LLMs to Self‑Report Hidden Behaviors

Anthropic's new paper introduces lightweight LoRA‑based introspection adapters that let large language models translate their internal activations into natural‑language reports of learned behaviors, achieving a 59% success rate on the AuditBench benchmark and exposing previously undetectable encrypted fine‑tuning attacks.

AI SafetyAuditBenchEncrypted Fine‑Tuning
0 likes · 20 min read
How Introspection Adapters Enable LLMs to Self‑Report Hidden Behaviors
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
May 3, 2026 · Artificial Intelligence

Anthropic’s Introspection Adapter Enables LLMs to Self‑Report Hidden Behaviors

A new Anthropic paper introduces an ultra‑lightweight LoRA plug‑in called the Introspection Adapter that lets large language models translate their internal activations into natural‑language reports of learned malicious or biased behaviors, achieving a 59% success rate on the AuditBench benchmark and outperforming existing black‑box and white‑box audit tools.

AI SafetyAuditBenchEncrypted Fine‑Tuning Attack
0 likes · 21 min read
Anthropic’s Introspection Adapter Enables LLMs to Self‑Report Hidden Behaviors
Lao Guo's Learning Space
Lao Guo's Learning Space
May 3, 2026 · Artificial Intelligence

2026 Enterprise Guide to Large Model Fine‑Tuning: Choosing, Training, and Deploying

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.

Cost OptimizationEnterprise AIFine-tuning
0 likes · 18 min read
2026 Enterprise Guide to Large Model Fine‑Tuning: Choosing, Training, and Deploying
PMTalk Product Manager Community
PMTalk Product Manager Community
Apr 30, 2026 · Artificial Intelligence

10 Essential Large‑Model Fine‑Tuning Techniques for AI Product Managers

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.

AdapterFine-tuningLarge Model
0 likes · 13 min read
10 Essential Large‑Model Fine‑Tuning Techniques for AI Product Managers
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Apr 29, 2026 · Interview Experience

ByteDance Interviewer Asks: What Rank r Do You Use for LoRA? I Said 64—He Said I'm Wasting GPU Memory

The article examines a common interview scenario where candidates are asked about LoRA rank selection, outlines two typical mistakes—guessing or staying silent—and presents a three‑step strategy of honest boundary setting, logical derivation, and asking a focused question, illustrating the approach with concrete LoRA calculations and a vLLM case study.

AI EngineeringLoRAinterview strategy
0 likes · 13 min read
ByteDance Interviewer Asks: What Rank r Do You Use for LoRA? I Said 64—He Said I'm Wasting GPU Memory
Woodpecker Software Testing
Woodpecker Software Testing
Apr 24, 2026 · Artificial Intelligence

Practical Guide to Optimizing Large Model Performance in Production

This guide details how enterprises can move large language models from lab to production by defining specific SLI/SLO metrics, diagnosing hidden bottlenecks such as tokenizer latency, and applying four quantifiable optimization levers that dramatically improve latency, throughput, and cost efficiency.

Continuous BatchingGPU OptimizationLoRA
0 likes · 6 min read
Practical Guide to Optimizing Large Model Performance in Production
CodeTrend
CodeTrend
Apr 24, 2026 · Artificial Intelligence

How Large Language Models Acquire Tool‑Calling Ability: SFT, RLHF & LoRA Explained

The article explains why pretrained LLMs cannot call tools, then breaks down the three‑stage training pipeline—Supervised Fine‑Tuning, Reinforcement Learning from Human Feedback, and knowledge distillation—showing how each step teaches models to read tool schemas, decide when to invoke a tool, generate JSON calls, and finally transfer the capability to smaller models with LoRA.

AI trainingFunction CallingLLM
0 likes · 19 min read
How Large Language Models Acquire Tool‑Calling Ability: SFT, RLHF & LoRA Explained
Fun with Large Models
Fun with Large Models
Apr 17, 2026 · Artificial Intelligence

Mastering Large Model Training: Practical Parameter Tuning from Beginner to Pro

This guide walks you through interpreting training logs and loss curves, diagnosing common issues such as oscillation, under‑fitting, and over‑fitting, and applying concrete adjustments to learning rate, LoRA settings, batch size, and epochs, with scenario‑specific strategies to turn a novice into a tuning expert.

AI trainingLarge ModelLoRA
0 likes · 23 min read
Mastering Large Model Training: Practical Parameter Tuning from Beginner to Pro
Baidu MEUX
Baidu MEUX
Apr 15, 2026 · Industry Insights

How AI Revolutionized the “One Orange” IP Design Workflow

This article examines the Baidu Search team’s “One Orange” IP case, revealing why they shifted from a traditional, labor‑intensive pipeline to an AI‑driven process, how multimodal models, LoRA and ControlNet enabled massive innovation and efficiency gains, and what this means for designers and the broader industry.

AIControlNetIP design
0 likes · 10 min read
How AI Revolutionized the “One Orange” IP Design Workflow
Old Zhang's AI Learning
Old Zhang's AI Learning
Apr 10, 2026 · Artificial Intelligence

How a 9B‑parameter Qwen3.5 model achieves full‑auto data analysis on a consumer GPU

The open‑source CoPaw‑Flash‑9B‑DataAnalyst‑LoRA model, fine‑tuned via LoRA, can autonomously load, explore, statistically analyze, visualize, and generate structured reports for CSV/Excel/JSON datasets, achieving a 90% success rate with an average of 26 iteration rounds, and it runs on a single consumer‑grade GPU using vLLM and the Data Analyst framework.

AgentData AnalystGPU
0 likes · 10 min read
How a 9B‑parameter Qwen3.5 model achieves full‑auto data analysis on a consumer GPU
AI Tech Publishing
AI Tech Publishing
Apr 9, 2026 · Artificial Intelligence

Engineering‑Focused Guide to Training and Inference of Large Language Models

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.

Fine-tuningInferenceLLM
0 likes · 13 min read
Engineering‑Focused Guide to Training and Inference of Large Language Models
Fun with Large Models
Fun with Large Models
Apr 1, 2026 · Artificial Intelligence

A Beginner's Deep Dive into Large‑Model Training Parameters with LLaMAFactory

This article walks readers through the three major training methods—full‑parameter, LoRA, and QLoRA—explaining their memory costs, data requirements, and trade‑offs, then provides a line‑by‑line breakdown of LLaMAFactory configuration files, hyper‑parameter tuning guidelines, and the process for merging LoRA adapters into a deployable model.

LLaMAFactoryLoRAQLoRA
0 likes · 27 min read
A Beginner's Deep Dive into Large‑Model Training Parameters with LLaMAFactory
AI Engineer Programming
AI Engineer Programming
Mar 28, 2026 · Artificial Intelligence

How to Start Training Your Own AI Model: A Complete Roadmap

This guide maps the end-to-end process for building a small AI model—from leveraging open-source base models and applying SFT with LoRA/QLoRA, through alignment techniques like DPO or ORPO, to low-cost distillation and final quantization for local deployment, while recommending free GPU resources and essential tooling.

AIAlignmentDistillation
0 likes · 12 min read
How to Start Training Your Own AI Model: A Complete Roadmap
Qborfy AI
Qborfy AI
Mar 24, 2026 · Artificial Intelligence

Why Full Fine‑Tuning Beats LoRA: When and How to Update Every Model Parameter

This article explains full fine‑tuning—updating all parameters of a pretrained model—to achieve the highest task performance, compares it with LoRA and prompt tuning, shows when it is appropriate, provides a step‑by‑step Hugging Face implementation, memory‑saving tricks, common pitfalls, and practical takeaways.

Deep LearningDeepSpeedGPU Memory
0 likes · 9 min read
Why Full Fine‑Tuning Beats LoRA: When and How to Update Every Model Parameter
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Mar 24, 2026 · Artificial Intelligence

Convert Any Text to LLM LoRA in a Single Forward Pass with SHINE

The SHINE hypernetwork can turn arbitrary text into LoRA parameters for a large language model with just one forward pass, internalizing the knowledge for multi‑turn dialogue, achieving efficiency and scaling comparable to in‑context methods while outperforming traditional fine‑tuning baselines.

LoRAhypernetworkparameter-efficient fine-tuning
0 likes · 17 min read
Convert Any Text to LLM LoRA in a Single Forward Pass with SHINE
Fun with Large Models
Fun with Large Models
Mar 20, 2026 · Artificial Intelligence

Step‑by‑Step Guide to Using LLaMAFactory for Full‑Cycle Large‑Model Training (Part 9)

This article walks through the complete workflow of fine‑tuning a Qwen2.5‑0.5B model with LLaMAFactory, covering environment setup, model download, dataset preparation, configuration editing, training execution, LoRA weight merging, and deployment via vLLM, while highlighting the framework’s minimal‑code and broad model support.

AI model trainingLLaMAFactoryLoRA
0 likes · 12 min read
Step‑by‑Step Guide to Using LLaMAFactory for Full‑Cycle Large‑Model Training (Part 9)
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Mar 15, 2026 · Artificial Intelligence

HY‑WU: Real‑Time Adaptive AI Model That Generates Parameters On‑The‑Fly

HY‑WU demonstrates that generating model parameters dynamically during inference enables a single foundation model to perform diverse image‑editing tasks, outperforming fixed‑parameter baselines in human and automatic evaluations, benchmark tests, and conflict‑task experiments, highlighting a practical real‑time adaptation approach for AI systems.

HY-WULoRATransformer
0 likes · 16 min read
HY‑WU: Real‑Time Adaptive AI Model That Generates Parameters On‑The‑Fly
Old Zhang's AI Learning
Old Zhang's AI Learning
Mar 12, 2026 · Artificial Intelligence

Distilling Claude Opus 4.6 into Qwen3.5‑27B: High‑Quality Reasoning on a Single RTX 3090

The article details how Claude Opus 4.6's chain‑of‑thought data were used to distill the 27‑billion‑parameter Qwen3.5‑27B model with Unsloth and LoRA, achieving full‑context inference on a single RTX 3090/4090, while outlining performance numbers, hyper‑parameter tips, benchmark gains and the trade‑offs of losing multimodal abilities.

Claude Opus 4.6GPU inferenceLoRA
0 likes · 7 min read
Distilling Claude Opus 4.6 into Qwen3.5‑27B: High‑Quality Reasoning on a Single RTX 3090
SuanNi
SuanNi
Mar 9, 2026 · Artificial Intelligence

How Hypernetworks Turn Documents into Instant LLM Skills

This article analyzes the memory and adaptation limits of large language models and presents a hypernetwork‑based approach that instantly converts documents or task descriptions into low‑rank LoRA modules, enabling cheap, on‑demand model updates and cross‑modal knowledge transfer.

AILLMLoRA
0 likes · 9 min read
How Hypernetworks Turn Documents into Instant LLM Skills
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Mar 9, 2026 · Artificial Intelligence

Instant LoRA Generation and Long‑Document Internalization: Cost‑Amortized Model Updates via 0.1‑Second Forward Pass

The article analyzes the quadratic attention and KV‑Cache bottlenecks of Transformers on ultra‑long inputs and the heavy compute cost of traditional supervised fine‑tuning, then presents Sakana AI's Cost Amortization framework—Doc‑to‑LoRA and Text‑to‑LoRA—that shifts weight updates to a meta‑training hypernetwork, achieving sub‑50 MB memory for 128K‑token inference, sub‑GB update memory for long‑document QA, and zero‑shot task adaptation with sub‑second latency.

Cost AmortizationCross-modalLoRA
0 likes · 13 min read
Instant LoRA Generation and Long‑Document Internalization: Cost‑Amortized Model Updates via 0.1‑Second Forward Pass
AIWalker
AIWalker
Mar 8, 2026 · Artificial Intelligence

FireRed-Image-Edit v1.1 Boosts OOTD Element Fusion and Portrait Consistency

The Super Intelligence team at Xiaohongshu unveils FireRed-Image-Edit v1.1, an open‑source image‑editing model that dramatically improves ID‑consistent edits, multi‑element OOTD fusion, portrait makeup, and font style rendering while delivering end‑to‑end generation in 4.5 seconds on 30 GB VRAM, backed by a full training‑distillation pipeline and a technical report on arXiv.

AI modelFireRed-Image-EditLoRA
0 likes · 10 min read
FireRed-Image-Edit v1.1 Boosts OOTD Element Fusion and Portrait Consistency
Old Zhang's AI Learning
Old Zhang's AI Learning
Mar 8, 2026 · Artificial Intelligence

Twinkle – A Lightweight, Fully Chinese Large‑Model Training Framework from ModelScope

Twinkle is a lightweight client‑server training framework open‑sourced by ModelScope that abstracts away Ray clusters, data and model parallelism, offers three run modes (torchrun, Ray, HTTP), multi‑tenant LoRA training, dual back‑ends (Transformers and Megatron), and a serverless Training‑as‑a‑Service gateway for enterprise and individual developers.

LoRAModelScopeTaaS
0 likes · 14 min read
Twinkle – A Lightweight, Fully Chinese Large‑Model Training Framework from ModelScope
AIWalker
AIWalker
Mar 7, 2026 · Artificial Intelligence

YOLO-Master v2026.02 Unveils Four Innovations for SOTA Object Detection

Tencent’s YOLO-Master v2026.02 adds a Mixture‑of‑Experts architecture, zero‑overhead LoRA fine‑tuning, Sparse SAHI inference for large images, and Cluster‑Weighted NMS, delivering 3‑5× faster inference, up to 70% reduced training resources, and markedly higher detection accuracy across diverse benchmarks.

Computer VisionLoRAMixture of Experts
0 likes · 15 min read
YOLO-Master v2026.02 Unveils Four Innovations for SOTA Object Detection
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Mar 6, 2026 · Artificial Intelligence

Why Reasoning and Tool-Use Clash in Agentic RL—and How DART Solves It

Recent studies reveal that in Agentic RL, jointly training reasoning and tool-use on shared parameters creates a persistent negative interaction, with gradients nearly orthogonal, limiting performance; a disentangled tuning approach (DART) using separate LoRA adapters isolates the two abilities and restores gains across benchmarks.

DARTGradient InterferenceLoRA
0 likes · 12 min read
Why Reasoning and Tool-Use Clash in Agentic RL—and How DART Solves It
AIWalker
AIWalker
Mar 1, 2026 · Artificial Intelligence

How X2HDR Enables AI to Achieve True Transparent HDR Imaging

X2HDR tackles the long‑standing HDR generation problem by converting color data into a perceptual uniform space and applying LoRA lightweight fine‑tuning, dramatically boosting visual fidelity while slashing data and compute demands for film, gaming, and VR.

AIHDR imagingLoRA
0 likes · 3 min read
How X2HDR Enables AI to Achieve True Transparent HDR Imaging
Data Party THU
Data Party THU
Mar 1, 2026 · Artificial Intelligence

Unlocking Efficient LLM Fine‑Tuning: LoRA, QLoRA, and DoRA Compared

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.

DoRAFine-tuningLoRA
0 likes · 20 min read
Unlocking Efficient LLM Fine‑Tuning: LoRA, QLoRA, and DoRA Compared
Old Zhang's AI Learning
Old Zhang's AI Learning
Feb 21, 2026 · Artificial Intelligence

Why Fine‑Tuning Large Models Is Now Ridiculously Easy

The article explains how Unsloth dramatically lowers the barrier to fine‑tuning large language models, offering one‑click installation, free Colab GPU support, extensive model coverage, impressive speed and memory gains, and detailed step‑by‑step guides that let anyone with basic Python skills train powerful models.

ColabGPULoRA
0 likes · 14 min read
Why Fine‑Tuning Large Models Is Now Ridiculously Easy
Qborfy AI
Qborfy AI
Feb 20, 2026 · Artificial Intelligence

Mastering Model Fine‑Tuning: Theory, Workflow, and Real‑World Code

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.

Fine-tuningLoRAOpenAI
0 likes · 20 min read
Mastering Model Fine‑Tuning: Theory, Workflow, and Real‑World Code
Old Zhang's AI Learning
Old Zhang's AI Learning
Feb 1, 2026 · Artificial Intelligence

Microsoft VibeVoice‑ASR Open‑Source: One‑Shot 60‑Minute Transcription with Speaker ID and Timestamps

Microsoft’s newly open‑sourced VibeVoice‑ASR model can transcribe up to 60‑minute audio in a single pass, preserving global context while providing built‑in speaker diarization and timestamps, supports 50+ languages, offers custom hot‑word injection, and can be deployed via Docker, Gradio, or vLLM for high‑throughput API services.

ASRDockerLoRA
0 likes · 9 min read
Microsoft VibeVoice‑ASR Open‑Source: One‑Shot 60‑Minute Transcription with Speaker ID and Timestamps
AI Cyberspace
AI Cyberspace
Jan 29, 2026 · Artificial Intelligence

Step‑by‑Step Guide to Efficient LLM Fine‑Tuning with LoRA, QLoRA, and Llama‑Factory

This tutorial explains the concepts, methods, and practical commands for fine‑tuning large language models using efficient techniques like LoRA and QLoRA, covering model selection, resource considerations, Docker deployment, dataset preparation, training configuration, evaluation metrics, model merging, and deployment with GGUF and Ollama.

GGUFGPU memory optimizationLLM fine-tuning
0 likes · 27 min read
Step‑by‑Step Guide to Efficient LLM Fine‑Tuning with LoRA, QLoRA, and Llama‑Factory
AI Frontier Lectures
AI Frontier Lectures
Jan 12, 2026 · Artificial Intelligence

How GraphKeeper Tackles Catastrophic Forgetting in Domain‑Incremental Graph Learning

This article analyzes the GraphKeeper framework, which combines multi‑domain graph decoupling, unbiased ridge‑regression knowledge preservation, and a domain‑aware distribution discriminator to overcome catastrophic forgetting in domain‑incremental graph neural network training, and validates its superiority through extensive experiments and ablations.

Catastrophic ForgettingDomain Incremental LearningGraphKeeper
0 likes · 15 min read
How GraphKeeper Tackles Catastrophic Forgetting in Domain‑Incremental Graph Learning
Design Hub
Design Hub
Dec 24, 2025 · Artificial Intelligence

Qwen-Image-Edit-2511 Boosts Designer Control with Stronger AI Image Editing

The open‑source Qwen-Image-Edit-2511 model from Alibaba introduces major upgrades—enhanced multi‑person consistency, built‑in LoRA styles, reduced image drift, and stronger geometric reasoning—while community tests, GGUF local deployment, and a 42.55× LightX2V speed boost demonstrate its practical impact for designers.

AI Image EditingGGUFLightX2V acceleration
0 likes · 7 min read
Qwen-Image-Edit-2511 Boosts Designer Control with Stronger AI Image Editing
AI Algorithm Path
AI Algorithm Path
Dec 23, 2025 · Artificial Intelligence

Fine‑Tuning Qwen‑Video‑8B with LLaMA‑Factory for Domain‑Specific Video Understanding

This article details how the Qwen‑Video‑8B model, built on Qwen3‑VL‑8B‑Instruct, is fine‑tuned with the LLaMA‑Factory framework using a curated city‑scenery dataset, addresses challenges of domain knowledge, temporal modeling and multimodal fusion, and demonstrates improved video captioning across baseline, English‑fine‑tuned and Chinese‑fine‑tuned versions.

AI fine-tuningLLaMA-FactoryLoRA
0 likes · 10 min read
Fine‑Tuning Qwen‑Video‑8B with LLaMA‑Factory for Domain‑Specific Video Understanding
Instant Consumer Technology Team
Instant Consumer Technology Team
Dec 16, 2025 · Artificial Intelligence

How Mind Lab Trained a Trillion‑Parameter Agentic Memory with Only 10% GPU Power

This article explains how the Mind Lab team tackled the challenges of training a 1‑trillion‑parameter mixture‑of‑experts model for agentic memory using reinforcement learning, LoRA, and a custom Megatron‑Bridge architecture, achieving a ten‑fold speedup while consuming just a fraction of the usual GPU resources.

AIAgentic AppsLoRA
0 likes · 9 min read
How Mind Lab Trained a Trillion‑Parameter Agentic Memory with Only 10% GPU Power
58UXD
58UXD
Oct 28, 2025 · Artificial Intelligence

How AI-Powered Size Extension Boosts Image Workflow Efficiency

This article explains how an AI‑driven size‑extension technique using a custom Kontext LoRA model and workflow dramatically speeds up image adaptation for automotive marketing, detailing the training process, resource consumption, labeling strategy, test results, and future prospects while acknowledging current limitations.

AICase StudyLoRA
0 likes · 8 min read
How AI-Powered Size Extension Boosts Image Workflow Efficiency
Data Party THU
Data Party THU
Oct 9, 2025 · Artificial Intelligence

Can One Model Master All Audio‑Visual Tasks? Introducing Crab’s Unified Approach

This article presents Crab, a unified audio‑visual scene understanding model that leverages a novel display‑cooperation learning paradigm, introduces the AV‑UIE dataset with explicit reasoning steps, and demonstrates superior performance across temporal, spatial, pixel‑level, and spatio‑temporal tasks through extensive experiments and ablations.

BenchmarkDatasetLoRA
0 likes · 12 min read
Can One Model Master All Audio‑Visual Tasks? Introducing Crab’s Unified Approach
Baobao Algorithm Notes
Baobao Algorithm Notes
Sep 22, 2025 · Artificial Intelligence

How to Add Special Tokens to LLMs Without Losing Performance

This guide explains why naïvely adding special tokens during supervised fine‑tuning can destabilize a large language model, and provides step‑by‑step strategies—including tokenizer updates, embedding resizing, smart initialization, and LoRA‑based PEFT—to integrate new tokens while preserving the model's original capabilities.

LLMLoRAspecial tokens
0 likes · 9 min read
How to Add Special Tokens to LLMs Without Losing Performance
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Sep 19, 2025 · Artificial Intelligence

Master Parameter-Efficient Fine‑Tuning: LoRA & QLoRA Explained for Interviews

This article explains why full fine‑tuning of large models is impractical, introduces parameter‑efficient fine‑tuning (PEFT) with LoRA and QLoRA, provides mathematical foundations, implementation code, resource‑usage analysis, interview question templates, and practical deployment tips for real‑world AI projects.

LoRAQLoRAlow-rank adaptation
0 likes · 24 min read
Master Parameter-Efficient Fine‑Tuning: LoRA & QLoRA Explained for Interviews
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Aug 23, 2025 · Artificial Intelligence

Why LoRA, QLoRA, Prompt & Prefix Tuning Are Changing Large‑Model Fine‑Tuning

This article explains the mathematical basis of LoRA, compares it with QLoRA, Prompt Tuning, Prefix Tuning and P‑tuning, shows practical PyTorch implementations, and provides mixed‑precision training tips so readers can choose the most memory‑efficient fine‑tuning method for their large language models.

LoRAPrompt TuningQLoRA
0 likes · 17 min read
Why LoRA, QLoRA, Prompt & Prefix Tuning Are Changing Large‑Model Fine‑Tuning
Amap Tech
Amap Tech
Aug 18, 2025 · Artificial Intelligence

How Omni-Effects Enables Spatially Controllable Multi‑VFX Generation with LoRA‑MoE

Omni-Effects introduces a unified framework that combines LoRA‑based expert mixture models and spatially aware prompts to generate multiple, precisely placed visual effects in video, supported by the new Omni‑VFX dataset and evaluation suite, demonstrating superior spatial control and diversity over prior single‑effect methods.

AILoRAVideo Generation
0 likes · 8 min read
How Omni-Effects Enables Spatially Controllable Multi‑VFX Generation with LoRA‑MoE
Tencent Technical Engineering
Tencent Technical Engineering
Aug 14, 2025 · Artificial Intelligence

Why Do Large Language Models Hallucinate? Causes, Risks, and Multi‑Dimensional Solutions

This article systematically examines the root causes of hallucinations in large language models, evaluates their pros and cons, and presents a comprehensive set of optimization techniques—including prompt engineering, RAG, sampling tweaks, supervised fine‑tuning, LoRA, RLHF, chain‑of‑thought reasoning, and agent/workflow designs—to build more reliable and trustworthy AI applications.

AILLMLoRA
0 likes · 29 min read
Why Do Large Language Models Hallucinate? Causes, Risks, and Multi‑Dimensional Solutions
AI Algorithm Path
AI Algorithm Path
Aug 9, 2025 · Artificial Intelligence

How LoRA Enables Multimodal Capabilities in Large Language Models

This article compares two ways to add vision to large language models—training a native multimodal model from scratch or attaching a visual module to a pretrained LLM—then details the VoRA approach that uses LoRA adapters to inject visual knowledge without extra inference cost.

ChameleonLLaVALoRA
0 likes · 7 min read
How LoRA Enables Multimodal Capabilities in Large Language Models
Amap Tech
Amap Tech
Aug 7, 2025 · Artificial Intelligence

Boosting Codebase Upgrades with Code RAG and Agent‑Driven Fine‑Tuning

This article describes how the Gaode terminal team tackled large‑scale repository upgrades by building a code‑RAG and code‑Agent tool, addressing recall and stability issues, then fine‑tuning a small LLM (Qwen3‑4B) with LoRA and custom datasets to achieve reliable, low‑cost, on‑device code‑query performance.

Code AgentKnowledge GraphLLM
0 likes · 11 min read
Boosting Codebase Upgrades with Code RAG and Agent‑Driven Fine‑Tuning
58UXD
58UXD
Jul 30, 2025 · Artificial Intelligence

How to Build AI-Powered Real‑Person 3D Scenes: From Style Exploration to LoRA Fusion

This guide walks creators through the complete workflow of generating realistic AI‑driven human scenes and converting them into 3D‑style visuals, covering style definition, tool‑assisted pipelines with Doubao and Stable Diffusion, LoRA training, and flexible weight blending for diverse artistic outcomes.

3D renderingAI-generated imageryDigital Content Creation
0 likes · 9 min read
How to Build AI-Powered Real‑Person 3D Scenes: From Style Exploration to LoRA Fusion
AI Algorithm Path
AI Algorithm Path
Jul 19, 2025 · Artificial Intelligence

Understanding LoRA and QLoRA: Techniques for Efficient LLM Fine‑Tuning

This article explains how low‑rank adaptation (LoRA) and its quantized variant (QLoRA) compress large language model weights, reduce training cost, and enable flexible adapter switching, while detailing matrix decomposition, training mechanics, and trade‑offs with concrete examples and quantitative analysis.

AdapterLLM fine-tuningLoRA
0 likes · 11 min read
Understanding LoRA and QLoRA: Techniques for Efficient LLM Fine‑Tuning
AI Frontier Lectures
AI Frontier Lectures
Jun 20, 2025 · Artificial Intelligence

Can One Model Master All Audio‑Visual Tasks? Introducing Crab’s Unified Approach

Researchers from RUC, Tsinghua, and Tencent present Crab, a unified audio‑visual scene understanding model that leverages explicit cooperation and a new AV‑UIE dataset with visible reasoning steps, achieving state‑of‑the‑art performance across temporal, spatial, pixel‑level, and spatio‑temporal tasks.

LoRAaudio-visualscene understanding
0 likes · 13 min read
Can One Model Master All Audio‑Visual Tasks? Introducing Crab’s Unified Approach
Data Thinking Notes
Data Thinking Notes
Jun 15, 2025 · Artificial Intelligence

Mastering Fine-Tuning: From Basics to Advanced Techniques for Large Language Models

Fine‑tuning transforms a general‑purpose large language model into a domain‑specific expert by training on a small, labeled dataset, and this guide explains its background, core concepts, technical mechanisms, various methods—including full‑parameter, LoRA, adapters, and prompt tuning—plus practical use cases, advantages, challenges, and best‑practice recommendations.

AIAdapterLoRA
0 likes · 13 min read
Mastering Fine-Tuning: From Basics to Advanced Techniques for Large Language Models
DataFunSummit
DataFunSummit
Jun 10, 2025 · Artificial Intelligence

How Quwan’s Kaitian Model Tackles Emotional AI for Social Apps – Architecture, Training Tricks, and Safety

Quwan Technology presents its Kaitian social large model, designed for personalized, emotionally rich, multimodal AI interactions, detailing its scene‑specific goals, CPT+SFT+RLHF training pipeline, data desensitization, LoRA fine‑tuning, evaluation methods, pruning, latency trade‑offs, safety mechanisms, and future feedback loops.

AI SafetyLoRAModel Pruning
0 likes · 13 min read
How Quwan’s Kaitian Model Tackles Emotional AI for Social Apps – Architecture, Training Tricks, and Safety
DaTaobao Tech
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
Alibaba Cloud Developer
Alibaba Cloud Developer
May 28, 2025 · Artificial Intelligence

Unlocking LLM Fine‑Tuning: From Architecture to LoRA, DPO and Deployment

This article provides a comprehensive guide to large language model fine‑tuning, covering model architecture, parameter and memory calculations, prompt engineering, data construction, LoRA and PEFT techniques, reinforcement learning methods such as DPO, and practical deployment workflows on internal platforms.

Fine‑TuningLLMLoRA
0 likes · 21 min read
Unlocking LLM Fine‑Tuning: From Architecture to LoRA, DPO and Deployment
AI Frontier Lectures
AI Frontier Lectures
May 19, 2025 · Artificial Intelligence

DreamO: Multi‑Condition Image Customization with a 400M Flux‑Based Model

DreamO, a collaborative effort by ByteDance and Peking University, introduces a unified 400M‑parameter framework built on Flux‑1.0‑dev that enables simultaneous control of identity, style, appearance, and virtual try‑on, offering open‑source, low‑cost, and fast image customization comparable to commercial large models.

AI researchDreamOFlux model
0 likes · 6 min read
DreamO: Multi‑Condition Image Customization with a 400M Flux‑Based Model
Mafengwo Technology
Mafengwo Technology
Apr 30, 2025 · Artificial Intelligence

How MaFengWo’s mfw-32B Travel LLM Outperforms DeepSeek‑R1 in Speed and Accuracy

The article details the development, training, and evaluation of MaFengWo's 32‑billion‑parameter travel large language model (mfw‑32B), highlighting its superior itinerary planning, personalized demand capture, budget management, and resource efficiency compared to DeepSeek‑R1, and describing the SFT and reinforcement‑learning stages that enabled these gains.

AI OptimizationLoRAModel Evaluation
0 likes · 14 min read
How MaFengWo’s mfw-32B Travel LLM Outperforms DeepSeek‑R1 in Speed and Accuracy
Ops Development & AI Practice
Ops Development & AI Practice
Apr 6, 2025 · Artificial Intelligence

Mastering Ollama Modelfile: Build and Customize Your Own LLM

This guide explains how to retrieve, analyze, and modify an Ollama Modelfile—using commands like `ollama show --modelfile`, dissecting key directives such as FROM, TEMPLATE, LICENSE, PARAMETER, SYSTEM, and ADAPTER—and walks through step‑by‑step creation of a custom model.

AI modelLLM customizationLoRA
0 likes · 9 min read
Mastering Ollama Modelfile: Build and Customize Your Own LLM
AIWalker
AIWalker
Apr 2, 2025 · Artificial Intelligence

EasyControl: Plug‑and‑Play DiT Control with Arbitrary Aspect Ratios and Accelerated Inference

EasyControl introduces a lightweight condition‑injection LoRA module, a position‑aware training paradigm, and causal attention with KV‑cache to enable plug‑and‑play multi‑condition control for DiT models, supporting arbitrary image resolutions while cutting inference latency by up to 30% and preserving high‑quality generation.

Conditional GenerationDiTEasyControl
0 likes · 17 min read
EasyControl: Plug‑and‑Play DiT Control with Arbitrary Aspect Ratios and Accelerated Inference
Architect
Architect
Apr 1, 2025 · Artificial Intelligence

When to Fine‑Tune Large Language Models vs. Relying on Prompting and RAG

The article explains why most projects should start with prompt engineering or simple agent workflows, outlines the scenarios where model fine‑tuning adds real value, compares fine‑tuning with Retrieval‑Augmented Generation, and offers practical criteria for deciding which approach to adopt.

AI deploymentLoRARAG
0 likes · 9 min read
When to Fine‑Tune Large Language Models vs. Relying on Prompting and RAG
Baidu MEUX
Baidu MEUX
Mar 27, 2025 · Artificial Intelligence

How LoRA Supercharges AI‑Generated Seasonal Poetry Posters

This article details how the LoRA model was employed to enhance AI-generated seasonal poetry posters, covering project background, innovative gameplay, training methodology, dataset preparation, and the resulting benefits of fully automated visual creation that boosts user engagement and product AI capabilities.

AI creativityAI image generationLoRA
0 likes · 8 min read
How LoRA Supercharges AI‑Generated Seasonal Poetry Posters
Fun with Large Models
Fun with Large Models
Mar 20, 2025 · Artificial Intelligence

Fine‑Tune DeepSeek‑R1 with Just a Few Lines of Code Using Unsloth

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.

DeepSeekFine-tuningLoRA
0 likes · 18 min read
Fine‑Tune DeepSeek‑R1 with Just a Few Lines of Code Using Unsloth
Alibaba Cloud Infrastructure
Alibaba Cloud Infrastructure
Mar 18, 2025 · Cloud Native

Gray Release of LoRA and Base Models Using ACK Gateway with AI Extension on Kubernetes

This guide explains how to deploy large language model inference services on a GPU-enabled Kubernetes cluster, configure ACK Gateway with AI Extension for intelligent routing and load balancing, and perform gray releases for both LoRA fine‑tuned models and base models such as QwQ‑32B and DeepSeek‑R1, including step‑by‑step commands and validation procedures.

ACK GatewayAI inferenceCloud Native
0 likes · 25 min read
Gray Release of LoRA and Base Models Using ACK Gateway with AI Extension on Kubernetes
NewBeeNLP
NewBeeNLP
Mar 18, 2025 · Interview Experience

How to Ace Multimodal Model Interviews at Taobao's Search AI Division

This article recounts a three‑stage interview for a multimodal large‑model position at Taobao's Search AI division, detailing typical questions on CLIP, LoRA, BLIP, Qwen‑VL, Transformer fundamentals, RLHF, and coding challenges, and offers insights on what interviewers focus on.

AICLIPLoRA
0 likes · 5 min read
How to Ace Multimodal Model Interviews at Taobao's Search AI Division
Ops Development & AI Practice
Ops Development & AI Practice
Feb 15, 2025 · Artificial Intelligence

How to Efficiently Fine‑Tune Llama 3 on a Free Colab T4 GPU with Unsloth

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.

AIColabFine-tuning
0 likes · 13 min read
How to Efficiently Fine‑Tune Llama 3 on a Free Colab T4 GPU with Unsloth
vivo Internet Technology
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
DataFunSummit
DataFunSummit
Jan 6, 2025 · Artificial Intelligence

Efficient Large‑Model Training with LLaMA‑Factory: Overview, Techniques, and Applications

This article explains how to train large language models efficiently using LLaMA‑Factory, covering low‑resource training challenges, memory‑saving optimizations for parameters, gradients and activations, framework features, quick‑start guidance, performance tuning, real‑world case studies, and a detailed Q&A.

AIDeepSpeedLLaMA-Factory
0 likes · 10 min read
Efficient Large‑Model Training with LLaMA‑Factory: Overview, Techniques, and Applications
Architecture and Beyond
Architecture and Beyond
Nov 2, 2024 · Artificial Intelligence

Step-by-Step Guide to Training a LoRA Model with Flux1_dev on ComfyUI

This tutorial walks programmers through preparing a GPU cloud environment, installing ComfyUI, downloading Flux1_dev models, integrating a custom LoRA, labeling generated images, and finally training the LoRA using ai‑toolkit, providing detailed commands, configuration tips, and practical cost estimates.

AI image generationComfyUIFlux
0 likes · 12 min read
Step-by-Step Guide to Training a LoRA Model with Flux1_dev on ComfyUI
System Architect Go
System Architect Go
Oct 24, 2024 · Artificial Intelligence

How to Fine‑Tune Translation Models on Kubernetes Docs with LoRA

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.

AIFine-tuningLLM
0 likes · 7 min read
How to Fine‑Tune Translation Models on Kubernetes Docs with LoRA
Baobao Algorithm Notes
Baobao Algorithm Notes
Oct 16, 2024 · Artificial Intelligence

How the DB3 Team Won the Meta CRAG RAG Challenge: Prompts, Retrieval, and LoRA Fine‑Tuning

This article analyzes the Meta Comprehensive RAG (CRAG) benchmark, detailing its three tasks, evaluation metrics, and the champion DB3 team's end‑to‑end solution that combines data preprocessing, dual‑stage retrieval, prompt engineering, LoRA‑based fine‑tuning, and public data augmentation to achieve top scores across all tasks.

BenchmarkKnowledge GraphLLM
0 likes · 17 min read
How the DB3 Team Won the Meta CRAG RAG Challenge: Prompts, Retrieval, and LoRA Fine‑Tuning
DataFunSummit
DataFunSummit
Sep 23, 2024 · Artificial Intelligence

TransLLM: A Framework for Cross‑Language Transfer of Conversational Large Language Models

This article presents TransLLM, a cross‑language migration framework that enables high‑quality conversational LLMs to be transferred to low‑resource languages by preserving advanced capabilities through Recovery KD, LoRA‑based continual pre‑training, and a translation‑thinking‑chain, with extensive experiments showing superior performance and safety over ChatGPT and GPT‑4.

LoRASafetyconversation LLM
0 likes · 22 min read
TransLLM: A Framework for Cross‑Language Transfer of Conversational Large Language Models
Baobao Algorithm Notes
Baobao Algorithm Notes
Sep 9, 2024 · Artificial Intelligence

How MoSLoRA Reinvents Low‑Rank Adaptation with Mixer Matrices

This article analyzes the Mixture‑of‑Subspaces in Low‑Rank Adaptation (MoSLoRA) paper, explaining its motivation, design choices that replace LoRA's gate with a mixer matrix, connections to multi‑head attention, experimental findings on LLaMA‑3 fine‑tuning, and theoretical proofs of its re‑parameterization properties.

AILoRAMixture of Experts
0 likes · 12 min read
How MoSLoRA Reinvents Low‑Rank Adaptation with Mixer Matrices
DaTaobao Tech
DaTaobao Tech
Aug 21, 2024 · Artificial Intelligence

Mastering Custom Large‑Model Training: Data Strategies, LoRA Tricks, and Resource Planning

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.

AI trainingFine-tuningGPU Memory
0 likes · 23 min read
Mastering Custom Large‑Model Training: Data Strategies, LoRA Tricks, and Resource Planning
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Jun 4, 2024 · Artificial Intelligence

EasyAnimate: High‑Resolution Video Generation via Diffusion Transformers

EasyAnimate, an open‑source DiT‑based video generation framework from Alibaba Cloud AI Platform PAI, offers a complete pipeline—including data preprocessing, VAE and DiT training, LoRA fine‑tuning, motion‑module integration, and scalable inference up to 768×768 resolution and 144 frames—leveraging Diffusion Transformers to produce longer, higher‑quality videos.

AI videoDiffusion TransformerLoRA
0 likes · 14 min read
EasyAnimate: High‑Resolution Video Generation via Diffusion Transformers
58 Tech
58 Tech
Jun 3, 2024 · Artificial Intelligence

Parameter-Efficient Fine-Tuning (PEFT) Methods for Large Language Models: LoRA, QLoRA, AdaLoRA, SoRA, and Training Acceleration with Unsloth

This article systematically analyzes popular parameter‑efficient fine‑tuning (PEFT) techniques for large language models—including Adapter Tuning, Prefix Tuning, LoRA, QLoRA, AdaLoRA, and SoRA—detailing their principles, implementation code, experimental results on NLU tasks, and practical acceleration using the Unsloth library.

AdaLoRALoRAPEFT
0 likes · 39 min read
Parameter-Efficient Fine-Tuning (PEFT) Methods for Large Language Models: LoRA, QLoRA, AdaLoRA, SoRA, and Training Acceleration with Unsloth
DaTaobao Tech
DaTaobao Tech
Jun 3, 2024 · Artificial Intelligence

Transforming Interior Design: AIGC’s Text‑to‑Image, Lora, and IP‑Adapter Techniques

This article explains how AI‑generated content (AIGC) technologies such as text‑to‑image diffusion models, Lora fine‑tuning, and IP‑Adapter style transfer are applied to interior design, dramatically reducing design time, cutting costs, and enabling personalized, high‑quality visualizations for both consumers and furniture merchants.

AIGCIP-AdapterLoRA
0 likes · 9 min read
Transforming Interior Design: AIGC’s Text‑to‑Image, Lora, and IP‑Adapter Techniques
Sohu Tech Products
Sohu Tech Products
May 21, 2024 · Artificial Intelligence

OPPO Multimodal Pretrained Model Deployment in Cloud-Edge Scenarios: Practices and Optimizations

OPPO details how it deploys multimodal pretrained models on resource‑constrained edge devices by compressing CLIP‑based image‑text retrieval, adapting Chinese text‑to‑image generation with LoRA and adapters, and lightweighting diffusion models through layer pruning and progressive distillation, achieving sub‑3‑second generation while preserving cloud‑level quality.

CLIPDistillationLoRA
0 likes · 18 min read
OPPO Multimodal Pretrained Model Deployment in Cloud-Edge Scenarios: Practices and Optimizations
Baobao Algorithm Notes
Baobao Algorithm Notes
May 5, 2024 · Artificial Intelligence

Deep Dive into Transformer Mechanics: Scaling, Q/K Projections, FFNs, and More

This article provides concise technical explanations for 25 common questions about Transformer models, covering scaled dot‑product attention scaling, separate Q/K projections, feed‑forward network design, attention variants, normalization, LoRA versus full‑parameter training, KV‑cache, pre‑ and post‑norm, computational cost analysis, and advanced position‑encoding techniques.

LLMLoRATransformer
0 likes · 25 min read
Deep Dive into Transformer Mechanics: Scaling, Q/K Projections, FFNs, and More
Baidu MEUX
Baidu MEUX
Apr 17, 2024 · Artificial Intelligence

How AI‑Powered Design Revives China’s 24 Solar Terms in a New‑Chinese Style

Leveraging AI image generation and LoRA model fine‑tuning, Baidu’s MEUX team reimagined the traditional 24 solar‑term cards in a fresh New‑Chinese aesthetic, blending cultural heritage, artistic composition, and modern visual effects to boost design efficiency and create a cohesive brand experience.

24 solar termsAI designBaidu
0 likes · 9 min read
How AI‑Powered Design Revives China’s 24 Solar Terms in a New‑Chinese Style
OPPO Kernel Craftsman
OPPO Kernel Craftsman
Mar 29, 2024 · Artificial Intelligence

InternLM Model Research and XTuner Practical Guide (Part 1): DataLoader, Model Conversion, Merging, and Inference

The guide walks through fine‑tuning InternLM‑Chat‑7B with XTuner, showing how to build a DataLoader from a HuggingFace Dataset, convert a LoRA .pth checkpoint to HuggingFace format, merge the adapter into the base model, run inference, and adapt the process for custom datasets and 4‑bit quantization experiments.

DataLoaderFineTuningInternLM
0 likes · 27 min read
InternLM Model Research and XTuner Practical Guide (Part 1): DataLoader, Model Conversion, Merging, and Inference
DaTaobao Tech
DaTaobao Tech
Mar 6, 2024 · Artificial Intelligence

AI Clothing Graffiti Project: Implementation and Optimization of AIGC Technology in Taobao Life 2

The AI Clothing Graffiti Project in Taobao Life 2 leverages Stable Diffusion, ControlNet, and LoRA to let users generate and stylize clothing designs via text‑image prompts, employing parallel processing, face repair, and content filtering, and has launched successfully, inviting algorithm engineers to join the team.

AIAIGCComputer Vision
0 likes · 14 min read
AI Clothing Graffiti Project: Implementation and Optimization of AIGC Technology in Taobao Life 2
NewBeeNLP
NewBeeNLP
Feb 22, 2024 · Artificial Intelligence

Practical Tips for CPT, SFT, and LoRA in Large Language Model Fine‑Tuning

This article shares hands‑on guidance on using continual pre‑training (CPT), supervised fine‑tuning (SFT), and LoRA adapters for large language models, covering dataset size requirements, learning‑rate scheduling, warm‑up ratios, epoch strategies, and practical routing choices based on real‑world experiments.

CPTLLM fine-tuningLoRA
0 likes · 12 min read
Practical Tips for CPT, SFT, and LoRA in Large Language Model Fine‑Tuning
Ximalaya Technology Team
Ximalaya Technology Team
Feb 1, 2024 · Artificial Intelligence

Understanding AI Image Generation: Diffusion Models, CLIP, and Control Techniques

This guide explains how AI image generators such as Stable Diffusion and DALL·E 3 turn text prompts into pictures by using diffusion models, CLIP‑aligned embeddings, and optional controls like negative prompts, fine‑tuned LoRA checkpoints and ControlNet conditioning, highlighting their differences, workflow, and practical customization.

AI image generationCLIPControlNet
0 likes · 18 min read
Understanding AI Image Generation: Diffusion Models, CLIP, and Control Techniques
Open Source Tech Hub
Open Source Tech Hub
Jan 26, 2024 · Fundamentals

How Smaz2 Compresses LoRa Messages on Tiny Devices

This article explains the motivation, dictionary design, bigram table, encoding rules, and real‑world compression results of the Smaz2 library, a space‑optimized C/Python compressor for short LoRa messages on microcontrollers with less than 2 KB RAM.

CLoRAPython
0 likes · 8 min read
How Smaz2 Compresses LoRa Messages on Tiny Devices
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Jan 21, 2024 · Artificial Intelligence

Understanding Pretraining and Fine‑Tuning of Large Language Models: Methods, Resources, and Practical Applications

This article explains the concepts of pretraining and fine‑tuning for large language models, compares full‑parameter, LoRA and QLoRA approaches, discusses resource consumption, introduces the ModelScope SWIFT framework with code examples, and shows how fine‑tuning can improve data‑visualisation tasks while reducing token usage.

Data visualizationLLMLoRA
0 likes · 24 min read
Understanding Pretraining and Fine‑Tuning of Large Language Models: Methods, Resources, and Practical Applications