lora

This paper investigates singular components in LoRA and proposes SCLoRA, a method that uses spectral clipping to improve task adaptation while reducing catastrophic forgetting of pre-trained knowledge.

LoRA-Diffusion proposes a parameter-efficient fine-tuning method for diffusion-based language models by applying low-rank decomposition to the denoising trajectory rather than model weights, achieving competitive performance with only 1.2% trajectory adapter parameters.

A developer claims to have built a real continual learning model prototype using LoRA to give Qwen4B instant, generalizable memory without retraining, and is inviting independent researchers to validate the mechanism.

This paper proposes a market-information-aware gated LoRA framework to adapt the Chronos-2 time-series foundation model for day-ahead electricity price forecasting, improving cross-market transferability on Chinese provincial markets.

CoAdapt-GUI is a test-time adaptation framework for mobile GUI agents that jointly adapts workflow context and policy, improving performance on unseen-app benchmarks like AndroidWorld-Generalization and AndroidWorld Plus.

This arXiv paper benchmarks eight open-source small language models under different fine-tuning strategies for emergency department decision support, finding that LoRA-tuned SLMs can outperform commercial baselines on triage and referral tasks while remaining locally deployable.

SeFoRA is a proposed federated LoRA algorithm that uses sketch aggregation to handle heterogeneous client ranks and alleviate bilinear mismatch. It includes a rank-homogeneous variant with convergence guarantees and shows state-of-the-art performance on RoBERTa-Large fine-tuning.

This paper proposes using router weight sensitivity under lightweight fine-tuning (e.g., LoRA) to identify and prune experts in Mixture-of-Experts models, enabling significant memory and latency reductions with minimal accuracy loss.

Presents Self-Geometry, a plug-and-play test-time adaptation pipeline that enforces explicit multi-view geometric constraints using 2D pixel correspondences to improve geometrically consistent 3D vision foundation models.

A developer trained a 1.1B-parameter LLM from scratch on 20B tokens for about $200, using fineweb-edu for pretraining and LoRA finetuning on OpenHermes. The project includes open-source code, model weights, and a demo website.

TwiL-LM is a parameter-efficient LoRA adapter for SmolLM2-1.7B-Instruct, designed for formal-logic and reasoning tasks, achieving a macro-primary score of 0.361 on a formal-logic suite.

fal releases a new LoRA adapter, MiniMax-H3-Realism-People-LoRA, that enhances the MiniMax H3 video model for realistic human portraits, faces, and everyday scenes. It includes training details, usage via the fal.ai LoRA endpoint, and before/after comparisons.

This paper investigates the warm-up stage for on-policy distillation (OPD), showing that teacher-compatible chain-of-thought supervision and LoRA-based training with near-saturation duration improve OPD effectiveness. It introduces Simple-OPD, a plug-and-play initialization method that boosts OPD performance across diverse settings.

A Hugging Face repository hosting MiniMax-H3 models converted for ComfyUI usage, along with a Lightx2v distill LoRA for faster inference.

SemiAdapt-Instruct proposes a modular framework that discovers latent instruction domains, trains per-domain LoRA adapters in parallel, and routes among them without extra parameters, enabling extensible instruction tuning where new domains require only single-adapter updates instead of full retraining.

This paper proposes DiSR, a framework that separates 3D perception from reasoning by using off-the-shelf perception models to reconstruct explicit 3D evidence and fine-tuning an LLM with LoRA for spatial reasoning, achieving competitive performance with improved interpretability and efficiency.

LightX2V releases an open, local LoRA prompt rewriter for MiniMax-H3 text-to-audio-video generation, fine-tuned on Qwen3.6-27B to expand short prompts into structured audio-video descriptions.

YOLO-PEFT is a structure-aware framework that formulates adapter placement as constraint planning for parameter-efficient fine-tuning of YOLO detectors, achieving better mAP than full fine-tuning with reduced memory.

A tweet sharing a Hugging Face Space for MiniMax-H3-Turbo-Lora, a LoRA fine-tuned variant of the MiniMax H3 Turbo model.

An early-preview LoRA for MiniMax-H3 enables 4-step audio-video generation instead of ~20 steps, yielding roughly 5x faster sampling, though quality is still immature.