IEEE

ARD-LoRA: Dynamic Rank Allocation for Parameter-Efficient Fine-Tuning of Foundation Models with Heterogeneous Adaptation Needs

Abstract: Conventional Low-Rank Adaptation (LoRA) methods employ a fixed rank, imposing uniform adaptation across transformer layers and attention heads despite their heterogeneous learning dynamics.

RapidFire AI Celebrates Winners Showcasing How to Build Better LLM Applications, Faster

SAN DIEGO, CA, UNITED STATES, February 5, 2026 /EINPresswire.com/ -- RapidFire AI today announced the winners of the ...
XDA Developers on MSN

I served a 200 billion parameter LLM from a Lenovo workstation the size of a Mac Mini

This mini PC is small and ridiculously powerful.
IEEE

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning

Abstract: Federated fine-tuning (FedFT) provides an effective paradigm for fine-tuning large language models (LLMs) in privacy-sensitive scenarios. However, practical deployment remains challenging ...
marktechpost

How to Build a Privacy-Preserving Federated Pipeline to Fine-Tune Large Language Models with LoRA Using Flower and PEFT

In this tutorial, we demonstrate how to federate fine-tuning of a large language model using LoRA without ever centralizing private text data. We simulate multiple organizations as virtual clients and ...
GitHub

Local-first LoRA fine-tuning Studio with web UI

NamelyCorp LLM Studio is an end-to-end system for building document-grounded fine-tuned language models using Low-Rank Adaptation (LoRA). It provides a complete workflow from document ingestion to ...
GitHub

HF-Advanced-Fine-Tuning

Fine-tune Qwen2.5-Coder models using the Sovereign AI Stack — pure Rust tools for privacy-preserving ML with no Python dependencies.