QuIP# relies on two main components: incoherence processing and lattice codebooks. Incoherence processing makes the weights of the model more suitable for quantization by making them approximately Gaussian-distributed. Lattice codebooks, on the other hand, are used to quantize these weights. The researchers used a new lattice codebook based on the E_8 lattice for this purpose. The results showed that QuIP# achieved near-native performance at 2 bits across all Llama 1 and 2 models on both language modeling and zero-shot tasks.
Key takeaways:
- The researchers introduced QuIP#, a method that combines lattice codebooks with incoherence processing to create state-of-the-art 2 bit quantized models for large language models (LLMs).
- QuIP# significantly closes the gap between 2 bit quantized LLMs and unquantized 16 bit models, allowing even large models like Llama 2 70B to fit on a single 24GB GPU.
- The method relies on two main components: incoherence processing and lattice codebooks. Incoherence processing makes the weights and hessian matrices incoherent, improving quantization performance, while lattice codebooks take advantage of the "roundness" of incoherence processed weights for efficient quantization.
- QuIP# achieves near-native performance at 2 bits on language modeling and zero shot tasks across all Llama 1 and 2 models, providing a solution for the challenges of storing and serving large language models.