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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
AI Frontier Lectures
AI Frontier Lectures
Jan 27, 2026 · Artificial Intelligence

A Unified Framework for Neural Network Reprogrammability: From Model Reprogramming to Prompt Tuning

This article surveys recent advances in neural network reprogrammability, presenting a unified framework that categorizes model reprogramming, prompt tuning, prompt instruction, and in‑context learning, highlights the shift from parameter‑centric to reprogrammability‑centric adaptation, and provides efficiency analyses, taxonomy, and practical case studies.

Model AdaptationNeural Network ReprogrammabilityPrompt Tuning
0 likes · 16 min read
A Unified Framework for Neural Network Reprogrammability: From Model Reprogramming to Prompt Tuning
PMTalk Product Manager Community
PMTalk Product Manager Community
Jan 8, 2026 · Artificial Intelligence

Understanding Fine‑Tuning: A Primer for AI Product Managers

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.

AI product managementFine-tuningModel Adaptation
0 likes · 6 min read
Understanding Fine‑Tuning: A Primer for AI Product Managers
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
Ops Development & AI Practice
Ops Development & AI Practice
Mar 19, 2025 · Artificial Intelligence

How to Fine‑Tune Large Language Models: From PEFT to Knowledge Injection

This article provides a comprehensive guide to customizing pre‑trained large language models through fine‑tuning techniques—including parameter‑efficient methods, data preparation, knowledge injection, and robust evaluation—offering practical steps, best practices, and domain‑specific considerations for achieving superior task performance.

LLM fine-tuningdata preparationknowledge injection
0 likes · 18 min read
How to Fine‑Tune Large Language Models: From PEFT to Knowledge Injection
AntTech
AntTech
Oct 29, 2024 · Artificial Intelligence

Three Ant Group Papers Featured at EMNLP 2024: Dynamic Transformers, Plug‑and‑Play Visual Reasoner, and Efficient Fine‑Tuning of Large Language Models

This announcement introduces three Ant Group papers accepted at EMNLP 2024—Mixture‑of‑Modules for dynamic Transformer assembly, a plug‑and‑play visual reasoning framework built via data synthesis, and a layer‑wise importance‑aware efficient fine‑tuning method for large language models—highlighting their innovations and upcoming live presentations.

AI researchEMNLP 2024Visual Reasoning
0 likes · 6 min read
Three Ant Group Papers Featured at EMNLP 2024: Dynamic Transformers, Plug‑and‑Play Visual Reasoner, and Efficient Fine‑Tuning of Large Language Models
UCloud Tech
UCloud Tech
Oct 13, 2023 · Artificial Intelligence

How PEFT Transforms Large Model Fine‑Tuning: Additive, Prompt & LoRA Methods Explained

This article introduces parameter‑efficient fine‑tuning (PEFT) techniques—including additive adapters, soft‑prompt methods, selection‑based BitFit, and re‑parameterization approaches like LoRA and AdaLoRA—explains their architectures, experimental results, and provides end‑to‑end code for fine‑tuning ChatGLM2‑6B on a Chinese medical QA dataset.

AdaLoRAAdapterLoRA
0 likes · 22 min read
How PEFT Transforms Large Model Fine‑Tuning: Additive, Prompt & LoRA Methods Explained