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Boost 2-Bit LLM Accuracy with EoRA
- Quantization reduces memory footprint of large language models by converting parameters to lower-precision integer formats like INT8 or INT4, achieving significant size reduction.
- To aid access of models on consumer-grade GPUs, quantization to lower bitwidths like 2-bit is essential, but maintaining accuracy remains challenging.
- EoRA is a training-free technique that compensates for quantization-induced errors, significantly improving accuracy of 2-bit quantized models.
- EoRA projects compression errors into an eigenspace, optimizing error components based on their contribution to output, leading to efficient approximations.
- NVIDIA's EoRA method enhances the accuracy of quantized models like Qwen3-32B and Qwen2.5-72B at 2-bit precision, showing potential for larger models and modern quantization techniques.
- Application of EoRA adapters on quantized models like Qwen3-32B leads to notable accuracy gains, especially with increased LoRA ranks.
- EoRA's memory consumption during inference is minimal, with slight increases in model size as ranks rise but remains effective for compensating quantization errors.
- Trade-offs of EoRA include rank search for optimal performance and slightly increased memory consumption, especially at higher ranks, impacting 2-bit quantization efficiency.
- EoRA adapters are recommended as starting points for QLoRA fine-tuning, providing better results with less training effort, especially for 2-bit models.
- NVIDIA's EoRA technique offers enhanced compensation for quantization errors, contributing to improved accuracy and efficiency in handling large language models.
- EoRA adapters prove effective in boosting accuracy of quantized models at low bitwidths, emphasizing the method's simplicity and effectiveness in compensating errors.
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