Summary ModuLoRA Finetuning LLMs with Modular Quantizers arxiv.org
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ModuLoRA enables efficient finetuning of large language models with high performance on downstream tasks using low-precision 3-4 bit parameters.
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Key Points
- ModuLoRA is a memory-efficient finetuning algorithm that enables finetuning large language models (LLMs) with up to 65 billion parameters in 3-bit or 4-bit precision on a single consumer-grade GPU
- ModuLoRA integrates any user-specified weight quantizer with finetuning via low-rank adapters (LoRAs)
- ModuLoRA achieves competitive performance on text classification, natural language inference, and instruction following tasks using significantly less memory than existing approaches
- ModuLoRA surpasses the state-of-the-art ROUGE score on a popular summarization task
- ModuLoRA enables the release of the first family of 3-bit instruction-following Alpaca LLMs, which demonstrate strong performance on the challenging BigBenchHard benchmark
Summaries
20 word summary
ModuLoRA enables finetuning of large language models using 3-4 bit precision, outperforming other parameter-efficient methods with high downstream task performance.
43 word summary
ModuLoRA enables finetuning of large language models up to 65B parameters on consumer GPUs using 3-4 bit precision. It integrates LoRAs with quantizers, outperforming other parameter-efficient methods. Experiments show high downstream task performance with smaller quantized models, democratizing access to large language models.
126 word summary
ModuLoRA is a memory-efficient finetuning algorithm that enables large language models (LLMs) with up to 65 billion parameters to be finetuned in 3-bit or 4-bit precision on a single consumer-grade GPU. It integrates low-rank adapters (LoRAs) with a user-specified weight quantizer, allowing it to leverage state-of-the-art quantization techniques. ModuLoRA's flexibility in integrating with different quantizers sets it apart from concurrent work. Compared to other parameter-efficient finetuning methods, ModuLoRA's ability to finetune the largest LLMs on consumer GPUs is a significant advantage. The authors' experiments demonstrate that high performance on downstream tasks can be achieved with smaller quantized LLMs than previously thought. ModuLoRA and the accompanying LLMTools library aim to democratize access to large language models and enable wider accessibility and adoption of these powerful AI systems.
416 word summary
ModuLoRA: Finetuning LLMs with Modular Quantizers
ModuLoRA is a memory-efficient finetuning algorithm that enables large language models (LLMs) with up to 65 billion parameters to be finetuned in 3-bit or 4-bit precision on a single consumer-grade GPU. The key innovation is a quantization-agnostic backward pass that integrates low-rank adapters (LoRAs) with a user-specified weight quantizer.
This approach allows ModuLoRA to leverage state-of-the-art quantization techniques like OPTQ, which often outperform simpler 4-bit and 8-bit methods. ModuLoRA's flexibility in integrating with different quantizers sets it apart from concurrent work like QLoRA, which is limited to specific quantization strategies.
Compared to other parameter-efficient finetuning methods, ModuLoRA's ability to finetune the largest LLMs on consumer GPUs is a significant advantage. Previous 8-bit quantization techniques required a 96GB GPU to fully fit a 65B model, but ModuLoRA's 3-bit and 4-bit methods enable finetuning on 48GB and 24GB GPUs, respectively. This unlocks the benefits of data parallelism, which is more efficient than model parallelism.
The authors' experiments demonstrate that high performance on downstream tasks can be achieved with smaller quantized LLMs than previously thought. For example, their 4-bit and 3-bit 65B models outperform 8-bit and 16-bit 30B models on instruction following tasks, and a 3-bit model achieves a new state-of-the-art ROUGE score on the SamSum summarization task.
The authors also release the first family of 3-bit instruction-following Alpaca LLMs, which demonstrate strong performance on the challenging BigBenchHard benchmark. This highlights the potential of ModuLoRA to enable efficient finetuning and deployment of large language models on consumer hardware.
While ModuLoRA offers significant advantages, it also has some limitations. The low-rank adaptor cannot be trivially added to the quantized weight matrix at inference time, unlike the low overhead of LoRA. Additionally, making finetuning too easy on consumer hardware could present potential safety issues with LLMs. The largest models today, like GPT-4, may still exceed the memory capacity of commodity GPUs even with ModuLoRA.
In summary, the key contributions of this work are:
1. ModuLoRA, a memory-efficient finetuning method that integrates low-rank adapters with a user-specified black-box quantization module. 2. LLMTools, a user-friendly Python library that features an implementation of ModuLoRA and enables finetuning the largest LLMs on consumer GPUs. 3. Empirical evidence that high performance on downstream tasks can be achieved with smaller quantized LLMs than previously thought.
The authors believe that ModuLoRA and LLMTools will help democratize access to large language models and make them available to a broader audience, paving the way for wider accessibility and adoption of these powerful AI systems.
826 word summary
ModuLoRA: Finetuning 3-Bit LLMs on Consumer GPUs with Modular Quantizers
We propose ModuLoRA, a memory-efficient finetuning algorithm for large language models (LLMs) that supports finetuning LLMs with up to 65 billion parameters in 3-bit or 4-bit precision on a single consumer-grade GPU. ModuLoRA integrates any user-specified weight quantizer with finetuning via low-rank adapters (LoRAs).
The key innovation is a simple quantization-agnostic backward pass that adaptively materializes low-precision LLM weights from a custom black-box quantization module. This enables finetuning 3-bit LLMs for the first time, leveraging state-of-the-art 3-bit OPTQ quantization, which often outperforms finetuning that relies on less sophisticated 4-bit and 8-bit methods.
In our experiments, ModuLoRA achieves competitive performance on text classification, natural language inference, and instruction following tasks using significantly less memory than existing approaches. We also surpass the state-of-the-art ROUGE score on a popular summarization task.
We release ModuLoRA as part of LLMTools, a user-friendly library that enables finetuning LLMs on consumer GPUs. LLMTools supports quantization, inference, and finetuning of large models like LLAMA, BLOOM, and OPT, and provides modular support for multiple quantizers and optimization algorithms.
Our findings reveal that high performance can be achieved using smaller quantized LLMs than previously thought. For example, our 3-bit and 4-bit 65B LLAMA models match or outperform 8-bit and even full-precision baselines on several tasks, while using significantly less memory.
We also release the first family of 3-bit instruction-following Alpaca LLMs, which demonstrate strong performance on the challenging BigBenchHard benchmark.
In summary, the key contributions of this work are:
1. ModuLoRA, a memory-efficient finetuning method that operates over low-precision weights obtained via a user-specified black-box quantization module. 2. LLMTools, a user-friendly Python library that features an implementation of ModuLoRA and enables finetuning the largest LLMs on consumer GPUs. 3. Empirical evidence that high performance on downstream tasks can be achieved with smaller quantized LLMs than previously thought.
The paper first provides background on large language model finetuning and low-precision machine learning techniques like quantization. It then describes the ModuLoRA algorithm in detail, focusing on the key challenge of efficiently computing the backward pass with mixed-precision tensors.
The experimental section evaluates ModuLoRA on a range of tasks, including text classification, natural language inference, and abstractive summarization. We show that 3-bit and 4-bit ModuLoRA models achieve competitive performance using significantly less memory than existing approaches, and in some cases even surpass the state of the art.
We also present results on instruction following tasks, where we release the first family of 3-bit Alpaca models that demonstrate strong performance at multiple model sizes.
Overall, this work demonstrates the potential of ModuLoRA and LLMTools to enable efficient finetuning and deployment of large language models on consumer hardware, paving the way for wider accessibility and adoption of these powerful AI systems.
ModuLoRA: Finetuning LLMs with Modular Quantizers
ModuLoRA is a method that enables efficient finetuning of large language models (LLMs) on consumer-grade GPUs. The key innovation is a quantization-agnostic backward pass that integrates low-rank adapters with frozen, quantized LLM weights.
Comparison to Related Work ModuLoRA differs from concurrent work on QLoRA in several ways. ModuLoRA integrates with a user-specified black-box quantization module, allowing it to leverage sophisticated data-driven quantizers like OPTQ for improved performance over simpler quantization strategies. Unlike QLoRA, ModuLoRA can finetune 3-bit models, which QLoRA cannot do. The authors anticipate ModuLoRA will enable finetuning 2-bit LLMs by integrating with new quantizers.
Compared to other parameter-efficient finetuning methods, a key advantage of ModuLoRA is its ability to finetune the largest LLMs on consumer GPUs, addressing a limitation of methods that require storing significant amounts of frozen base model parameters.
Running LLMs on Consumer GPUs ModuLoRA's 3-bit and 4-bit methods enable finetuning a 65B LLM on a 48GB GPU and a 30B LLM on a 24GB GPU, bringing LLM finetuning to consumer hardware. This unlocks data parallelism, which is more efficient than model parallelism. Previous 8-bit quantization methods required a 96GB GPU to fully fit a 65B model.
What is a Good Base LLM for Finetuning? The authors find that high performance on downstream tasks can be achieved with smaller quantized LLMs than previously thought. They show that 4-bit and 3-bit 65B models outperform 8-bit and 16-bit 30B models on instruction following tasks, and that 3-bit models can attain a new state-of-the-art ROUGE score on the SamSum summarization task.
Limitations One limitation of ModuLoRA is that the low-rank adaptor cannot be trivially added to the quantized weight matrix at inference time, unlike the low overhead of LoRA. Another limitation is that making finetuning too easy on consumer hardware could present potential safety issues with LLMs. The largest models today, like GPT-4, may still exceed the memory capacity of commodity GPUs even with ModuLoRA.
Conclusion ModuLoRA enables finetuning 65B LLMs on 48GB consumer GPUs by integrating low-rank adapters with quantized weights. Its flexibility and competitive performance make finetuning more accessible and cost-effective. The authors believe ModuLoRA will help democratize access to large language models and make them available to a broader audience.