LRC introduces a novel method to fix quantization errors in LLMs by adding low-rank weight matrices that work on unquantized activations while keeping model accuracy.
https://arxiv.org/abs/2412.07902
🎯 Original Problem:
→ Current 4-bit quantization methods for LLMs struggle with accuracy loss, especially when quantizing both weights and activations to 4 bits (W4A4)
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🔧 Solution in this Paper:
→ Introduces LRC (Low-Rank Correction) that adds full-precision low-rank weight matrices to fix quantization errors
→ Uses joint optimization to tune both quantized weights and correction matrices
→ Processes unquantized activations with low-rank matrices while quantized weights handle quantized activations
→ Employs QuaRot procedure with Hadamard rotations to reduce incoherence
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💡 Key Insights:
→ Low-rank matrices operating on unquantized activations effectively correct quantization errors
→ Joint optimization of quantized weights and correction matrices is crucial
→ Method works across different model architectures and sizes
→ Composable with other quantization techniques
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📊 Results:
→ With 10% rank size: reduces accuracy gap by over 50%
→ With 30% rank size: completely closes accuracy gap
→ Demonstrated on Llama-2, Llama-3, Phi-3 and Mixtral models
→ Works effectively at W4A4 quantization level
First Set:
LRC fixes LLM quantization errors by cleverly using low-rank matrices on unquantized data
Adding low-rank matrices to handle raw data helps LLMs stay smart even after heavy compression
Smart math trick keeps LLMs accurate while shrinking them down to 4-bits
Low-rank matrices save the day when squeezing LLMs into tiny spaces
Second Set:
Think of it as giving LLMs a smart backup brain that remembers the important stuff
It's like having a cheat sheet that helps LLMs stay sharp after extreme diet
Imagine keeping your LLM's wisdom while squeezing it into your phone
Like having a mini-translator that helps compressed LLMs speak clearly
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