Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
VeLoRA: Memory Efficient Training using Rank-1 Sub-Token Projections
Authors: Roy Miles, Pradyumna Reddy, Ismail Elezi, Jiankang Deng
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We confirm the effectiveness of our algorithm as being complimentary to many state-of-the-art PEFT methods on the VTAB-1k fine-tuning benchmark. Furthermore, we outperform QLo RA for fine-tuning LLa MA and show competitive performance against other memory-efficient pre-training methods on the large-scale C4 dataset. 4 Comparison with the state-of-the-art |
| Researcher Affiliation | Industry | Roy Miles Pradyumna Reddy Ismail Elezi Jiankang Deng Huawei Noah s Ark Lab Corresponding authors: EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Ve Lo RA, Pytorch-like |
| Open Source Code | Yes | Code: https://github.com/roymiles/Ve Lo RA |
| Open Datasets | Yes | VTAB-1k [53], GLUE [48], Alpaca dataset [36], C4 dataset [34] |
| Dataset Splits | No | The paper mentions using various benchmarks (VTAB-1k, GLUE, MMLU, C4) and reporting validation perplexity, but does not explicitly provide details about specific training, validation, and test dataset splits used for reproduction, nor does it cite standard splits being used. |
| Hardware Specification | Yes | All of the experiments in sections 4.2 and 4.5 were performed using 8 NVIDIA V100 GPUs with the fp16 data type. For the LLa MA experiments in section 4.4, we trained on 4 NVIDIA A100 GPUs |
| Software Dependencies | No | The paper mentions using PyTorch ("PyTorch-like pseudocode") and building upon other repositories (alpaca-lora, Ga Lore), but it does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | We finetuned a Vi T-B [12] model pre-trained on Image Net-21K using the Adam W optimizer with a learning rate of 5e-4 and a weight decay of 1e-4. All our models were trained using the Adam W optimizer with a learning rate of 1e-3 and a weight decay of 0. Table 8: Hyperparameters of fine-tuning Ro BERTa base. |