Are you ready for the next big thing in model quantization? A recent pull request to the popular LLaMA codebase indicates we may soon see support for true 2-bit quantization within the framework. Proposed by prolific LLaMA contributor Ikawrakow, the changes aim to bring LLaMA quantization more in line with state-of-the-art methods like QuIP# at the lowest precisions. This article will explore how SOTA 2-Bit Quants can elevate llama.cpp, unlocking new possibilities and enhancing its overall performance. So, let’s get started!
Table of Contents
What are SOTA 2-bit Quants?
SOTA 2-bit quants, short for State-of-the-Art 2-bit quants, are a cutting-edge approach to model quantization. This technique allows for the representation of model weights using only 2 bits, significantly reducing the memory footprint and computational requirements of deep learning models. By utilizing advanced quantization algorithms and clever encoding schemes, SOTA 2-bit quants achieve perplexities comparable to state-of-the-art quantization methods while maintaining reasonable inference performance.
The Motivation Behind SOTA 2-bit Quants
Model quantization plays a crucial role in optimizing deep learning models for deployment on resource-constrained devices. Traditional quantization techniques typically rely on higher precision representations, such as 8-bit or 16-bit, to strike a balance between model size and accuracy. However, advancements in hardware capabilities and algorithmic techniques have opened up the possibility of pushing the boundaries of quantization further.
SOTA 2-bit quants aim to achieve the best of both worlds – ultra-low precision representations for model weights without sacrificing model performance. The motivation behind this approach is to enable efficient deployment of deep learning models on a wide range of devices, including edge devices, mobile phones, and IoT devices, without compromising accuracy.
How it Works
Ikawrakow’s approach borrows some ideas from QuIP# to encode groups of 8 parameters into E8 lattice points while balancing positive and negative values through sign flips. Quantized values are selected from a codebook of 256 points optimized via calibration.
While still utilizing LLaMA’s existing block-wise framework, the quantization ends up using around 2.06 bits per parameter due to superblock overhead. However, models up to 70B parameters can still fit under 24GB, like a 70B model achieving a perplexity of 4.079.
Benefits of SOTA 2-bit Quants
The introduction of SOTA 2-bit quants brings forth a multitude of benefits for model quantization. Let’s explore some of the key advantages:
1. Reduced Memory Footprint
The use of 2-bit representations significantly reduces the memory requirements for storing model weights. This is especially crucial for resource-constrained devices with limited RAM or VRAM.
2. Improved Computational Efficiency
With fewer bits to process, SOTA 2-bit quants offer faster inference times compared to higher precision quantization methods. This allows for real-time inference on devices with limited computational power.
3. Maintained Model Performance
Despite the low precision representation, SOTA 2-bit quants achieve perplexities similar to state-of-the-art quantization methods. This ensures that the model’s accuracy is preserved even with the reduced precision.
4. Broader Device Compatibility
The lightweight nature of SOTA 2-bit quants enables the deployment of deep learning models on a wider range of devices, including those with low-power processors and limited resources.
Performance Outlook
Initial benchmarks show the quantized models can run reasonably fast but fall short of higher precision baselines. For example, when applying the technique to a 7B LLaMA model for TG-128, the measurements showed 155 t/s on an RTX-4080 using CUDA, 54 t/s on a 30-core M2 Max using Metal, and 24.3 t/s on a Ryzen 5975WX CPU. Comparing these numbers to the Q4_0 approach, we can see a slight decrease in performance.
TheCUDA, Metal, and CPU implementations provided should help expand performance on different platforms. More work optimizing matrix multiplication and leveraging hardware intrinsics could boost throughput further.
Perplexity Comparable to SOTA
In terms of perplexities, SOTA 2-bit quants demonstrate promising results. The table below showcases sample results for quantized model sizes and their corresponding perplexities:
These results highlight the potential of SOTA 2-bit quants in achieving competitive perplexities while significantly reducing the model’s size.
Room for Improvement
With the quantization code now available, the next steps will likely involve refining implementations and integrating support directly into LLaMA proper. Additional backends, quantized matrix multiply, and further tuning could all help maximize efficiency.
As one of the first to deliver real 2-bit quantization for LLaMA, this pull request represents exciting progress. With continued development, LLaMA may soon match the precision frontiers set by other frameworks.
Join the Discussion on GitHub
If you’re interested in learning more about SOTA 2-bit quants and its integration into llama.cpp, be sure to check out the pull request on GitHub. The discussion and feedback from the community play a crucial role in shaping the future of this feature. Join the conversation, share your thoughts, and contribute to the development of llama.cpp.
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