Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
QuIP$#$: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks
Authors: Albert Tseng, Jerry Chee, Qingyao Sun, Volodymyr Kuleshov, Christopher De Sa
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that Qu IP# outperforms existing PTQ methods, enables new behaviors in PTQ scaling, and supports fast inference. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Cornell University 2Department of Operations Research and Information Engineering, Cornell University. |
| Pseudocode | Yes | Algorithm 1 Qu IP# without Fine-Tuning (Qu IP#-No FT) input Weight W Rm n, hessians H Rn n, g-dim. k-bit codebook C ... Algorithm 2 Qu IP# Inference (for a Linear Layer) ... Algorithm 3 Incoherence Processing with RHT (IP-RHT) ... Algorithm 4 Incoherence Processing with RFFT (IP-RFFT) ... Algorithm 5 Qu IP# with Fine-Tuning |
| Open Source Code | Yes | Our code can be found at https://github.com/ Cornell-Relax ML/quip-sharp. |
| Open Datasets | Yes | Hessian matrices H were generated with 6144 sequences of a model s native context length (2048 for Llama 1, 4096 for Llama 2) from the Red Pajama 1T (Computer, 2023) dataset. |
| Dataset Splits | Yes | We train on small development dataset of 256 sequences from Red Pajama 1T and validate on 128 sequences. |
| Hardware Specification | Yes | All experiments were run on NVIDIA A100 GPUs except for the timing numbers, which were measured on a NVIDIA RTX 4090 |
| Software Dependencies | No | The paper mentions software components like "Flash Attention library", "Hugging Face library", and "CUDA kernel" but does not specify their version numbers for reproducibility. |
| Experiment Setup | Yes | For the within-transformer block section of fine-tuning, we use the Adam optimizer (Kingma & Ba, 2017), a learning rate of 5 10 5, batch size of 8, and sequence length equal to the model s native context length. We train on small development dataset of 256 sequences from Red Pajama 1T and validate on 128 sequences. We train for 5 epochs (160 steps) and keep the best model parameters based on the validation set. |