9 stories
Low-rank adaptation (LoRA) applies every rank-one component of its update to every token, and mixture-of-experts variants replace this with a learned router that activates a fixed number of components.
Personalizing text-to-image diffusion models from a few reference images requires preserving subject identity while following prompts that describe new contexts.
A practical guide to fine-tuning EmbeddingGemma with LoRA for custom retrieval, covering training pairs, negative sampling, and pitfalls.
We propose a fine-tuning method for flow-matching diffusion models aimed at realistic artificial light modeling without the need for a large training dataset.
Despite the widespread use of Low-Rank Adaptation (LoRA), little is known about its dynamics in continual learning and the mechanisms by which low-rank updates affect catastrophic forgetting.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch. - Release Fixes for AutoModel type hints in Modular Pipelines and Flux Klein LoRA loading · huggingface/diffusers
How LoRA matches full training performance more broadly than expected.
LoRA (Low Rank Adaptation) is a new technique for fine-tuning deep learning models that works by reducing the number of trainable parameters and enables efficient task switching. In this blog post we will talk about the key ideas behind LoRA in a very minimal torch example.
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