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LoRA Land: 310 Fine-tuned LLMs that Rival GPT-4, A Technical Report

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arxiv 2405.00732 v1 pith:TWJKJQ34 submitted 2024-04-29 cs.CL cs.AIcs.LG

Justin Zhao , Timothy Wang , Wael Abid , Geoffrey Angus , Arnav Garg , Jeffery Kinnison , Alex Sherstinsky , Piero Molino
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Travis Addair Devvret Rishi
This is my paper · ORCID
classification cs.CLcs.AIcs.LG
keywords loramodelsfine-tunedllmsbasefine-tuninglandsingle
verification ladder T0 review T1 audit T2 compute T3 formal
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Low Rank Adaptation (LoRA) has emerged as one of the most widely adopted methods for Parameter Efficient Fine-Tuning (PEFT) of Large Language Models (LLMs). LoRA reduces the number of trainable parameters and memory usage while achieving comparable performance to full fine-tuning. We aim to assess the viability of training and serving LLMs fine-tuned with LoRA in real-world applications. First, we measure the quality of LLMs fine-tuned with quantized low rank adapters across 10 base models and 31 tasks for a total of 310 models. We find that 4-bit LoRA fine-tuned models outperform base models by 34 points and GPT-4 by 10 points on average. Second, we investigate the most effective base models for fine-tuning and assess the correlative and predictive capacities of task complexity heuristics in forecasting the outcomes of fine-tuning. Finally, we evaluate the latency and concurrency capabilities of LoRAX, an open-source Multi-LoRA inference server that facilitates the deployment of multiple LoRA fine-tuned models on a single GPU using shared base model weights and dynamic adapter loading. LoRAX powers LoRA Land, a web application that hosts 25 LoRA fine-tuned Mistral-7B LLMs on a single NVIDIA A100 GPU with 80GB memory. LoRA Land highlights the quality and cost-effectiveness of employing multiple specialized LLMs over a single, general-purpose LLM.

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Forward citations

Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Improved Representation Steering for Language Models

    cs.CL 2025-05 conditional novelty 6.0 of 10

    RePS, a reference-free bidirectional preference optimization objective, improves representation steering and suppression for Gemma models, outperforming language-modeling objectives and approaching prompting performance.

  2. S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity

    cs.LG 2024-12 conditional novelty 6.0 of 10

    S2FT selects a few attention heads and FFN channels, permutes the neighboring weight matrices so the selected parts form dense blocks, and fine-tunes only those blocks, reporting better generalization and efficiency t...

  3. Scaling Point-in-Time Language Models

    cs.CL 2026-04 conditional novelty 5.5 of 10

    Scaling point-in-time LLMs to 4B parameters and 1T temporally filtered tokens narrows the gap to unrestricted models to about 8–11 average points and yields positive out-of-sample Sharpe ratios from news embeddings.

  4. SOFT: Selective Data Obfuscation for Protecting LLM Fine-tuning against Membership Inference Attacks

    cs.CR 2025-06 conditional novelty 5.0 of 10

    SOFT paraphrases low-loss fine-tuning samples before training, reducing MIA AUC from about 0.82 to about 0.54 across six datasets at roughly 7% perplexity cost.

  5. Text-to-LoRA: Instant Transformer Adaption

    cs.LG 2025-06 conditional novelty 5.0 of 10

    A hypernetwork can generate task-specific LoRA adapters from a text description, and when trained with supervised fine-tuning it zero-shot outperforms a multi-task LoRA baseline on ten benchmarks.

  6. Learning Beyond the Surface: How Far Can Continual Pre-Training with LoRA Enhance LLMs' Domain-Specific Insight Learning?

    cs.CL 2025-01 reject novelty 5.0 of 10

    Continued pre-training with LoRA on simplified triple-only documents improves LLM performance on the paper's own insight-extraction benchmarks, but those benchmarks are built from the same documents used for training.

  7. SEAL: Entangled White-box Watermarks on Low-Rank Adaptation

    cs.AI 2025-01 reject novelty 5.0 of 10

    SEAL embeds a secret matrix between LoRA's low-rank factors, then decomposes and hides it in the released weights, but its ownership verification can be gamed by claiming the identity matrix as the passport.

  8. AIx4Soccer: A Unified Platform Architecture for Football Club Management and Structured Athlete Development

    cs.CY 2026-07 conditional novelty 4.0 of 10

    A conceptual multi-tenant football-club SaaS design embeds a PDI development cycle and a 75/25 certified video-analyst marketplace on a proposed event-sourced knowledge-graph substrate.

  9. DP-FedLoRA: Privacy-Enhanced Federated Fine-Tuning for On-Device Large Language Models

    cs.CR 2025-09 reject novelty 3.0 of 10

    DP-FedLoRA clips and adds Gaussian noise to per-client LoRA matrices in federated LLM fine-tuning, claiming unbiased updates and bounded variance, but the privacy calibration and experiments have significant gaps.

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