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.
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.
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.
In just two days, Baidu Baige and Kunlun's engineers extended the vLLM‑Kunlun Plugin to overcome asymmetric KV dimensions and integrate SWA+Sink attention, achieving lossless, high‑performance inference of the MiMo Flash V2 model on the Kunlun P800 XPU.
This study introduces EvoVLMA, an evolutionary vision-language model adaptation framework that automatically searches training-free VLM adaptation algorithms using a two-stage LLM-guided evolution, demonstrating superior performance—such as a 1.91 % accuracy gain on 8-shot image classification—and releasing the code publicly.
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.
This article presents a comprehensive overview of lightweight adaptation methods—including language, domain, and optimization‑goal adapters and structured prompts—to overcome language mismatch, low domain fit, and objective differences when deploying open‑source multimodal large models in real‑world AI applications.
