lora

TensorSharp, an open-source inference engine, now supports Qwen-Image 2.1 for local text-to-image generation and editing with LoRA adapters, including accelerated variants like Pruna and Viggle.

The paper evaluates LLMs as graders for computer-science exams, finding that prompt design significantly affects grading accuracy, and shows that LoRA fine-tuning can mitigate issues to match human grader performance.

The paper investigates fine-tuning strategies for customer support LLMs, comparing multi-task training, sequential updates, and model merging across multiple model families. It concludes that multi-task full fine-tuning is the most robust default, while specialist models degrade off-task and require reliable routing.

This paper introduces Modl, a technique for creating cross-lingual remote-sensing multimodal large language models by composing domain and language LoRAs with mutual orthogonality, achieving superior performance without paired multilingual data.

The paper demonstrates that using a shared learning rate as a control in selective on-policy distillation experiments is not neutral, leading to varying performance and conclusions, and advocates for reporting full learning-rate matrix comparisons for fair evaluation.

This paper introduces Stiefel-AdamW, a geometry-aware optimizer for linear factorization blocks in deep learning that enhances stability and performance, validated on models like GPT2, ViT, and Mistral 7B.

HyperFlow introduces a new MiniMax-H3 variant with open-weight LoRA that enables 3× faster inference through data-free self-distillation while preserving video and stereo audio outputs.

The tweet discusses effective methods for AI attribution using Shapley values for inference, LoRA for post-training, and hierarchical approaches for pre-training, proposing a future of routed general intelligence.

HyperFlow is an open-source 8-step LoRA that uses data-free flow self-distillation to reduce video generation steps from 49 to 8 in MiniMax-H3, achieving significant speedup while maintaining quality.

A discussion on how fine-tuning on an abliterated base model inherits safety behaviors, with eval results showing mixed outcomes and comparisons to Claude models, highlighting the need for auditing.

A LoRA adapter was trained on an abliterated Qwen 3.8-27B model to enhance internal codebase recall, demonstrating superior performance over Claude models on private-repo-specific tasks in evaluations.

This controlled study compares supervised fine-tuning and reinforcement learning methods for training tool-calling agents across different datasets and model scales, finding that SFT with LoRA is strongest in-distribution while RL shows slight advantages in cross-dataset transfer.

This paper introduces an adaptive phase-switching method for communication-efficient federated LoRA fine-tuning, achieving up to 40.5% round-trip savings in communication costs while maintaining model performance on large language models.

This is an audio tokenizer and LoRA tool for the YuE2-3B model, enabling users to tokenize real audio recordings and generate new songs or covers.

This paper studies the trade-offs in LoRA rank selection for diffusion model fine-tuning, showing that small-to-moderate ranks like 4 and 8 optimize efficiency by balancing FID scores and computational costs.

CodeFinetuner is a complete pipeline for fine-tuning small code autocomplete models like Qwen2.5-Coder-3B on personal codebases using LoRA, with support for local inference via tools like llama.vim and llama.vscode and evaluation metrics.

This article presents Qwen3.8-27B-Humanlike-Chat, a model fine-tuned to reduce AI-like conversational habits and mimic realistic human-to-human chats. Trained on a dataset of real conversations, it aims to make responses more natural and less assistant-like.

The paper introduces α-split, a two-pool allocation method for differential privacy in federated learning, addressing cross-component budget collapse in speech-LLMs and improving utility and security against attacks.

MultiMatte is a promptable image background removal model fine-tuned from SAM 3 using LoRA, achieving higher accuracy on benchmarks by outputting alpha mattes for better handling of fuzzy boundaries.

ACE introduces a method to consolidate redundant adapters across experts in MoE large language models for more efficient parameter-efficient fine-tuning, achieving up to 1.48× training speedup without increasing peak memory.