lora

Third-party ComfyUI-compatible LoRA conversions for MiniMax-H3 Turbo 4-step audio-video generation, including further-trained checkpoint-500 variants and an example workflow.

The paper compares five parameter-efficient fine-tuning methods on four small language models for on-device personalization, finding LoRA+ best for energy efficiency and QLoRA best for memory-limited deployment.

This paper introduces MERaLiON-GR, a speech gender recognition model for English and Southeast Asian languages, fine-tuned from MERaLiON-SpeechEncoder-2 with LoRA and an ECAPA-TDNN head, achieving state-of-the-art performance across multilingual benchmarks.

An early-preview LoRA for MiniMax-H3 that enables joint video and synchronized audio generation in 4 sampling steps instead of ~20, offering roughly 5x faster sampling, with ComfyUI custom nodes and three bf16 checkpoints.

NVIDIA shares five lessons from over 5,000 Kagglers who fine-tuned reasoning models using LoRA adapters and synthetic chain-of-thought data in the Nemotron Model Reasoning Challenge, focusing on verifiable data, token budget, and infrastructure.

A Chinese user provides context on Chinese laws regarding pornography and censorship, explaining why MiniMax and other Chinese AI labs restrict LoRAs and content generation to comply with strict legal enforcement.

This paper proposes MoEGen, a parameter-efficient fine-tuning framework that uses mixture-of-experts to generate instance-adaptive LoRA updates via expert codes and a lightweight hypernetwork, improving performance on commonsense reasoning benchmarks without storing separate adapters per expert.

Introduces LoCA, a two-stage backpropagation-free method for small-shift adaptation of LLMs, using one-shot calibration to fit local credit assignment maps and closed-form ridge solves for low-rank adapters, achieving lower memory and time than LoRA with competitive cross-entropy on multiple benchmarks.

This paper introduces Omega-S, a lightweight, data-free regularization penalty for low-rank fine-tuning that improves retention of original model capabilities by penalizing variance in weight-matrix node degrees. Experiments on Llama-3-8B with LoRA show it retains more original capability than no regularization or tuned baselines.

Mind Lab claims its Macaron-V1 model surpasses GLM-5.2 in benchmarks, using five LoRA expert modules attached to GLM-5.1 with dynamic expert switching and continual learning via distilled LoRA adapters.

Athena, a 4B-parameter LoRA fine-tune of Qwen3-4B from markopolo_ai, beats GPT-5.6 and Claude Opus 4.8 on the OPeRA shopping behavior prediction benchmark, offering a self-hostable specialist model for predicting shopper actions.

This paper introduces InMyStyle, a privacy-first system that uses LoRA adapters on small language models (0.5B–7B) to rewrite AI-edited text toward an individual user's writing style without explicit prompts. Evaluations show quality plateaus across model sizes, suggesting compact models suffice for this task.

LARA is a method for efficient adaptation that adds low-rank corrections to a frozen model's residual stream instead of modifying weights, matching LoRA's performance while enabling composable behaviors and inference-time steering.

This paper proposes LSPO (LoRA Scaffolded Policy Optimization) to solve the vanishing-gradient problem of GRPO on zero-reward "cliff problems": a temporary LoRA adapter is used for a short SFT, successful trajectories are sampled, corrected via importance sampling, and then fed back into the RL batch; ultimately, only the base model is updated. Experiments on DeepMath-103K show it outperforms the DAPO baseline by an average of 3.8 points.

Kroma v0.1 is a LoRA fine-tune for Krea 2, released as a ComfyUI-compatible safetensors file with rank 256 and weight deltas for RMSNorm/modulation tensors, under an MIT license.

Introduces CACHE-UK, a stability-aware memory editing framework for sequentially updating quantized LLMs in finance, reducing knowledge degradation on 4-bit OpenLLaMA-3B while improving generalization rates on a UK financial corpus.

Compliance2LoRA proposes a hypernetwork-based framework that generates policy-compliant LoRA adapters on demand for large reasoning models, enabling adjustable safety alignment across arbitrary policy subsets without retraining separate models.

FedWeave proposes asymmetric aggregation for federated MoE-LoRA to handle task heterogeneity by separating expert aggregation from router optimization, achieving better specialization and performance.

The paper presents GrocLM, a fine-tuned language model for grocery category recommendation using a two-stage LoRA-based training strategy and trie-based constrained decoding. It achieves a 7.5% relative improvement in cart-adds per impression in a live production restocking task.

This paper identifies group preference collapse in personalized multimodal large language models and proposes PrefMoE, a preference-centric framework that separates profile information from preferences to improve personalization and reduce collapse.