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Found 13 papers, 7 papers with code
Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment
We achieve this for the LLaMA-2 7B model by combining the SparseGPT one-shot pruning method and sparse pretraining of those models on a subset of the SlimPajama dataset mixed with a Python subset of The Stack dataset.
How to Prune Your Language Model: Recovering Accuracy on the "Sparsity May Cry'' Benchmark
Pruning large language models (LLMs) from the BERT family has emerged as a standard compression benchmark, and several pruning methods have been proposed for this task.
Sparse Fine-tuning for Inference Acceleration of Large Language Models
While the standard approach is to leverage sparsity for computational reduction, we observe that in the case of memory-bound LLMs sparsity can also be leveraged for reducing memory bandwidth.
Accurate Neural Network Pruning Requires Rethinking Sparse Optimization
Obtaining versions of deep neural networks that are both highly-accurate and highly-sparse is one of the main challenges in the area of model compression, and several high-performance pruning techniques have been investigated by the community.
Error Feedback Can Accurately Compress Preconditioners
Experiments on deep neural networks show that this approach can compress full-matrix preconditioners to up to 99\% sparsity without accuracy loss, effectively removing the memory overhead of full-matrix preconditioners such as GGT and M-FAC.
Vision Models Can Be Efficiently Specialized via Few-Shot Task-Aware Compression
To address this, we ask: can we quickly compress large generalist models into accurate and efficient specialists?
SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks
We provide a new efficient version of the backpropagation algorithm, specialized to the case where the weights of the neural network being trained are sparse.
ZipLM: Inference-Aware Structured Pruning of Language Models
Furthermore, ZipLM achieves superior results for a fraction of the computational cost relative to prior distillation and pruning techniques, making it a cost-effective approach for generating an entire family of smaller, faster, and highly accurate models, guaranteed to meet the desired inference specifications.
CAP: Correlation-Aware Pruning for Highly-Accurate Sparse Vision Models
To further showcase CAP's accuracy and scalability, we use it to show for the first time that extremely-accurate large vision models, trained via self-supervised techniques, can also be pruned to moderate sparsities, with negligible accuracy loss.
GMP*: Well-Tuned Gradual Magnitude Pruning Can Outperform Most BERT-Pruning Methods
We revisit the performance of the classic gradual magnitude pruning (GMP) baseline for large language models, focusing on the classic BERT benchmark on various popular tasks.
CrAM: A Compression-Aware Minimizer
In this work we propose a new compression-aware minimizer dubbed CrAM that modifies the optimization step in a principled way, in order to produce models whose local loss behavior is stable under compression operations such as pruning.
The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models
We perform an in-depth study of the accuracy-compression trade-off for unstructured weight pruning of BERT models.
M-FAC: Efficient Matrix-Free Approximations of Second-Order Information
We propose two new algorithms as part of a framework called M-FAC: the first algorithm is tailored towards network compression and can compute the IHVP for dimension $d$, if the Hessian is given as a sum of $m$ rank-one matrices, using $O(dm^2)$ precomputation, $O(dm)$ cost for computing the IHVP, and query cost $O(m)$ for any single element of the inverse Hessian.
