LoRA

Headlines · 4

1. 4d
   olmOCR 2 brings local, searchable transcription to archives

   Ai2 reports that researchers are adapting the open olmOCR 2 model to extract searchable text from historical and archaeological document images, including handwriting and complex layouts. In the CENTURIA evaluation, LoRA fine-tuning on 57 records reduced transcription error from 15.58% to 1.11% across 450 test records, while ArtificeOCR uses the model locally with Tropy.

   Ai2 Blog · 🔥 0
2. 4d
   Developer releases sdft_zero3 for self-distillation under ZeRO-3

   Developer ifiaposto released sdft_zero3, a plug-and-play GitHub repository combining self-distillation with an EMA teacher, ZeRO-3, and PEFT. The author says it includes LoRA adapter switching for teacher and student passes, resumable EMA teacher checkpoints, and DeepSpeed configurations intended to address memory spikes. The author reports testing the method with Qwen3.5 and ToolAlpaca.

   r/MachineLearning · 🔥 0
3. Oct 18
   BrainLoRA adapts 100B+ multimodal backbones for brain encoding via PEFT

   An arXiv paper submitted on 19 October 2026, "Scaling Parameter-Efficient Fine-Tuning for Brain Encoding" by Zhuofan Josh Ying, Nikolaus Kriegeskorte, Hossein Adeli and six co-authors, proposes adapting very large pretrained multimodal backbones to brain encoding with low-rank adapters (LoRA) plus a lightweight readout. The authors say the approach scales to backbones of up to 104B parameters, which they describe as the largest model adapted for brain encoding to their knowledge, and that it consistently improves performance across nine multimodal backbones. They report that BrainLoRA achieves the best published result on the Algonauts 2025 Challenge on the same Qwen3-Omni backbone used by the prior state of the art, and that an ensemble of 200+ adapted models built with a multi-LoRA kernel ranks first on the public Algonauts post-challenge leaderboard at the time of writing. These benchmark claims are self-reported and not independently verified in the supplied material.

   Hugging Face · Papers · 🔥 0
4. Oct 18
   asmsense offers 228k stripped x86-64 functions and a Qwen3-8B naming model

   Posted on r/MachineLearning by /u/luxurycashew, asmsense is a dataset plus small model (Qwen3-8B with LoRA) for naming and describing functions in stripped binaries. It currently holds 228,177 functions from 341 projects compiled from -O0 through -Os, with English and Turkish descriptions and Chinese planned. The author compiled permissively licensed C projects so each function retains its real name and source, and lists remaining gaps: other architectures such as ARM64, RISC-V and MIPS, GCC/ELF builds (everything so far is clang/Mach-O on macOS), human-checked labels, and runs on public benchmarks including BinMetric, REBench, BinSum and DeBinVul.

   r/MachineLearning · 🔥 0

Experience and discussion from the community

Share my LoRA experienceAsk about LoRA