REVIEW 3 major objections 6 minor 1 cited by
Adaptive Pruning of Pretrained Transformer via Differential Inclusions
T0 review · 3 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash
Pith's one-line read One run of a mask-dynamics trajectory can produce pruned transformer weight families at every sparsity level, replacing the usual practice of restarting pruning for each compression ratio.
desk verdict Solid incremental pruning paper with a real one-pass multi-ratio claim; DeiT/Swin support it, but the CLIP table is misleading and the theory is oversold. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The central object is the differential inclusion for the mask parameter, a mirror-descent flow regularized by the $\ell^1$ penalty and the $[0,1]$ constraint, discretized by a proximal (linearized Bregman) update. Its load-bearing companion is the inverse scale space property: the support set of the sparse proxy $\Gamma_t$ grows over time by adding important weights before unimportant ones, so early stopping at different times yields masks of different sparsity. The structural device that makes the pruning fine-grained is the paired-module shared mask: query-key pairs, value-output pairs, and adjacent linear layers share one mask per pair, preserving the network's output structure while permitting asymmetric dimensional reductions inside attention and MLP blocks.
What would settle it
Record the mask support sets at consecutive early-stopping checkpoints of one SPP run on a pretrained transformer: the central claim predicts they are nested (columns that turn on later are a superset of earlier ones) and that each fine-tuned family member keeps accuracy near the unpruned model. An observation that later masks drop columns the path had already selected, or that the best early-stopped mask does not correspond to the top-magnitude columns of the dense model, would contradict the inverse scale space assumption the whole method rests on.
Extended reading notes
Core claim
The central claim is that the pruned structure of a pretrained transformer can be explored in order of importance by a single trajectory of a mask parameter, rather than optimized separately for each fixed sparsity. Specifically, the mask $M_t$ and an auxiliary sparse proxy $\Gamma_t$ follow the differential inclusion $\dot M_t/\kappa = -\nabla_M L_\rho(M_t,\Gamma_t)$, $\dot V_t = -\nabla_\Gamma L_\rho(M_t,\Gamma_t)$ with $V_t \in \partial\Omega(\Gamma_t)$, where $\Omega$ is the $\ell^1$ penalty plus the indicator of $[0,1]$. The inverse scale space property makes important weights become nonzero in $\Gamma_t$ earlier than unimportant ones, so saving $\{W_0 \odot \Gamma_k\}$ at different times yields a weight family with increasing sparsity. Theorem 1 states that the discretized updates (Eq. 10) converge globally to a critical point of the masked loss from any finite initialization, under a Kurdyka-Łojasiewicz condition on a constructed Lyapunov function.
Load-bearing premise
The method assumes the inverse scale space property holds for pretrained transformer weights—that the mask dynamics learns important weights earlier than unimportant ones along the path—a property cited from compressed sensing and from-scratch networks but not proved for fine-tuned transformers, so if the supports along the path are not nested or not accurate, the early-stopped masks will not yield good pruned models.
Editorial extensions
If this is right
- A single search stage yields pruned models at many compression ratios, so a practitioner can pick the sparsity level after searching, without rerunning the pruning process.
- Paired masks allow asymmetric dimensionality between query, key, value, and output projections in the same attention layer, enabling finer structure than uniform head pruning.
- Because the iterates converge to a critical point from arbitrary initialization, the mask path needs no special schedule or restarts to reach a meaningful sparse structure.
- The same solution path idea carries over to one-shot post-training pruning of large language models, with results comparable to dedicated fixed-ratio methods at 50% sparsity in the reported benchmarks.
Reading between the lines
- If the inverse scale space property holds on fine-tuned checkpoints, the same trajectory could be reused across downstream tasks, so one search could serve as a reusable pruning asset; the paper itself does not test this reuse.
- The order in which columns become nonzero along the path is a data-driven saliency ranking that could inform other compression tools such as quantization or distillation, not just pruning.
- A cheap diagnostic for the method's key assumption would be measuring the nesting of support sets (for example, Jaccard overlap between consecutive masks); the paper plots the growth of the $\ell^1$ norm of $\Gamma$ but does not quantify nesting.
- Monitoring the step-size condition from Theorem 1 during training could reveal how close practical runs are to the theoretical regime; the paper does not report this distance.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. This paper proposes Solution Path Pruning (SPP), a mask-based structured pruning method for pretrained transformers. The method introduces a continuous-time differential inclusion over a mask variable M and an auxiliary sparse variable Γ, discretized as a linearized Bregman iteration (Eq. (10), Algorithm 1). The central claim is that one run produces a 'Transformer Weight Family' {W0 ⊙ Γ_k} covering multiple sparsity levels, so that any desired compression ratio can be obtained by early stopping without rerunning the search. The paper introduces pair-wise shared masks for query-key, value-output, and MLP input-output pairs, proves a global convergence result (Theorem 1) under a Kurdyka-Łojasiewicz condition, and reports experiments on DeiT, Swin, CLIP, and LLMs (Llama2-7B, OPT-6.7B).
Significance. If the central claim holds, SPP would be a practically useful contribution: a single search stage yielding multiple sparsity levels could substantially reduce the cost of exploring compression ratios, and the pair-wise mask scheme is a sensible way to keep transformer dimensions aligned. The paper has strengths that should be credited: the code is released, the convergence proof is a nontrivial extension of the Bregman-iteration framework to masked pruning, and the experimental coverage includes several architectures and tasks. The main caveat is that the practical value of the method depends on an inverse scale space property that is asserted rather than established for pretrained transformer masks, and the CLIP comparisons appear to mix fine-tuned and zero-shot baselines. The contribution is incremental relative to the LBI/DessiLBI line, but it is not circular.
major comments (3)
- [Section 3.2; Section 5.3; Theorem 1] The central claim of the paper is that one run of Algorithm 1 yields a Transformer Weight Family whose members are usable pruned models at different sparsity levels. This requires the inverse scale space property asserted in Section 3.2: 'important weights are learned earlier than non-important ones.' The paper cites prior work for compressed sensing and for networks trained from scratch, but it neither proves nor empirically tests this property for the nonconvex masked objective Lρ(M, Γ) in Eq. (9) with fixed pretrained weights W0. Theorem 1 only establishes that the full iterative sequence converges to a critical point; it says nothing about the quality or nesting of the supports of Γk at early stopping times. Figure 2 shows monotonically increasing L1 norms, which is consistent with many dynamics and does not establish that early-stopped masks are accurate pruning structures. If the inverse scale space property fails for pretrained transformers, the output of Algorithm 1 is an arbitrary collection of early-stopped masks, and the 'any desired ratio in one stage' claim is unsupported. I would like to see either a derivation of the property under explicit assumptions on L and W0, or a direct experiment that checks support nesting and per-checkpoint accuracy along the path, for example by comparing path-selected masks against masks of the same sparsity obtained by restarting the search.
- [Table 3; Section 5.1 (CLIP results)] The CLIP experiments compare SPP against an 'Uncompressed' baseline that is not described as fine-tuned, while the SPP rows are fine-tuned after pruning; the text states that each sparse model is retrained for 5 epochs. The reported gains are implausibly large: for CLIP-Base, SPP at 93.4% of parameters improves Image->Text R@1 from 52.5 to 69.0, and at 86.0% parameters from 52.5 to 65.9. A pruning method should not improve retrieval recall by 12-16 points over the uncompressed model unless the baseline is zero-shot and the pruned models are fine-tuned. Please report a fine-tuned uncompressed baseline with the same 5-epoch protocol as the proper reference, or explicitly label the baseline as zero-shot and temper the claims accordingly. Also report standard deviations over seeds for both baselines and pruned models.
- [Appendix B, Eqs. (16) and (24)] The step-size condition in Theorem 1 relies on a Lipschitz constant that is not derived correctly as written. Eq. (24) states ∇\hat L(M)=Σ∇L(\hat W)*W0, and the text concludes that ∇\bar L is Lipschitz with constant Lip*C+ν^{-1}, where C=max|W0|. Since \hat L(M)=L(W0⊙M), the chain rule introduces a factor of the matrix norm of W0, not the entrywise maximum, so the displayed bound Lip*C (rather than Lip times an appropriate W0 norm, such as Lip*||W0||^2 or Lip*||W0||_{op}^2) is not justified. Please state the norm convention and provide the complete Lipschitz estimate; as written, the step-size threshold in Eq. (16) and the subsequent sufficient-descent lemma are not self-contained.
minor comments (6)
- [Table 3] In Table 3, the parameter and FLOP columns run together, for example '80794.3%' should be '807 (94.3%)'; this makes the table hard to read and should be corrected.
- [Section 5.1] The text says 'we surpassed SSP (Chen et al., 2021)', but the reference list contains S2ViTE (Chen et al., 2021) and no SSP; this appears to be a typo.
- [Algorithm 1] Algorithm 1 saves \bar W_{k+1}=W_{k+1}⊙M_{k+1}, but W is never updated in the search loop; it should say W0, and the relation between the saved checkpoints and Eq. (12) should be clarified.
- [Appendix B.4, Corollary 1 proof] The proof of Corollary 1 states that the conjugate of the group-Lasso penalty is the maximum of group l2-norms; the correct conjugate of Ω(Γ)=Σ_g ||Γ_g||_2 is the indicator of the intersection of unit balls {||g_g||_2≤1}. The definability argument survives because the indicator is semialgebraic, but the displayed formula is incorrect.
- [Condition 1 / Theorem 1] The labels 'Condition 1' and 'Assumption 1' are used interchangeably around Theorem 1; align the terminology.
- [Tables 1, 2, 4] The main accuracy tables report single runs without error bars; for comparisons where differences are 0.1-0.5 points, seed variance should be reported.
Circularity Check
No significant circularity: the SPP weight family is directly defined as the iterate path W0 ⊙ Γk; Theorem 1's convergence guarantee is proven under KL assumptions independent of mask quality; and the pruned-family accuracy is validated on external benchmarks rather than derived from fitted constants.
full rationale
The central derivation chain is: Eq. (8)–(10) define a differential-inclusion / linearized-Bregman iteration on (M, V, Γ); the family is defined in Eq. (12) as Wk = W0 ⊙ Γk, i.e., it is literally the iterate path, not a quantity fitted to accuracy targets; Theorem 1 (proved as Theorem 2 in Appendix B) shows convergence to a critical point under Lipschitz/KL assumptions, which does not presuppose that early Γk are good masks; and the usefulness of early-stopped Γk as pruning masks is an empirical claim, tested on ImageNet, COCO, and LLM benchmarks after fine-tuning. The inverse scale space property ('important weights are learned earlier than non-important ones') is asserted in Section 3.2 and supported by citations to Burger, Osher, Fu et al., and Bungert et al.; it is an unproven transfer assumption for pretrained transformers, but it is not definitionally equivalent to the paper's output, and the paper's own external experiments provide independent evidence. Self-citations to DessiLBI appear in related work and as a baseline (Table 4), not as the load-bearing justification of the weight-family claim. No equation reduces by construction to its inputs, and no fitted parameter is renamed as a prediction.
Assumptions & free parameters
free parameters (5)
- lambda (L1 penalty weight) =
15 for DeiT/Swin, 3 for CLIP (Table 8)
- kappa (damping factor) =
1 for DeiT/Swin, 100 for CLIP (Table 8)
- nu (coupling coefficient in L_rho) =
not reported
- step size alpha =
not reported
- RIA scaling lambda0 for LLM pruning =
RIA metric per-weight
assumptions (3)
- standard math L is continuously differentiable with Lipschitz continuous gradient and bounded level sets, and the Lyapunov function F is Kurdyka-Lojasiewicz on bounded sets (Assumption 1).
- domain assumption The inverse scale space property holds for the mask dynamics on pretrained transformers.
- ad hoc to paper Pairwise shared masks (QK, V-proj, MLP input-output) preserve the forward output structure after pruning.
Cite this review
Pith. "Pith review of Adaptive Pruning of Pretrained Transformer via Differential Inclusions." pith.science (2026). https://pith.science/paper/IIPVSMB3
@misc{pith2026250103289,
author = {Pith},
title = {Pith review of: Adaptive Pruning of Pretrained Transformer via Differential Inclusions},
year = {2026},
howpublished = {\url{https://pith.science/paper/IIPVSMB3}},
note = {Machine review of arXiv:2501.03289}
}
read the original abstract
Large transformers have demonstrated remarkable success, making it necessary to compress these models to reduce inference costs while preserving their perfor-mance. Current compression algorithms prune transformers at fixed compression ratios, requiring a unique pruning process for each ratio, which results in high computational costs. In contrast, we propose pruning of pretrained transformers at any desired ratio within a single pruning stage, based on a differential inclusion for a mask parameter. This dynamic can generate the whole regularization solution path of the mask parameter, whose support set identifies the network structure. Therefore, the solution path identifies a Transformer weight family with various sparsity levels, offering greater flexibility and customization. In this paper, we introduce such an effective pruning method, termed SPP (Solution Path Pruning). To achieve effective pruning, we segment the transformers into paired modules, including query-key pairs, value-projection pairs, and sequential linear layers, and apply low-rank compression to these pairs, maintaining the output structure while enabling structural compression within the inner states. Extensive experiments conducted on various well-known transformer backbones have demonstrated the efficacy of SPP.
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Forward citations
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Reference graph
Works this paper leans on
-
[1]
Ts Tp ) # Update and save the sparse model weights ¯Wk+1 = Wk+1 ⊙ Mk+1 #Save the checkpoint of ¯Wk+1 as the pruned model end for # Return Weight Family for the model Output: LLM Weight Family:{ ¯Wi|i ∈ [0, Tp]} 15 Published as a conference paper at ICLR 2025 Model Method Latency Time Params FLOPS(B) CLip-Base Uncompressed 9.273s 299100% 41.2100% SPP 8.675...
work page 2021
-
[3]
Assumption 1 (a)-(c) are regular in the analysis of nonconvex algorithm (see, Attouch et al. (2013) for instance), while Assumption 1 (d) is also mild including all Lipschitz continuous convex function over a compact set. Some typical examples satisfying Assumption 1(d) are the ℓ1 norm, group ℓ1 norm, and every continuously differentiable penalties. By Eq...
work page 2013
-
[4]
[o-minimal structure] An o-minimal structure on (R, +, ·) is a sequence of boolean algebras On of “definable” subsets of Rn, such that for each n ∈ N 1To simplify notations, we regard the input and output layers as the 0-th and the l-th layers, respectively, and absorb the bias of each layer into W i. 22 Published as a conference paper at ICLR 2025 (i) th...
work page 2025
-
[5]
According to van den Dries & Miller (1996); Bolte et al
[Definable function] Given an o-minimal structure O (over (R, +, ·)), a function f : Rn → R is said to be definable in O if its graph belongs to On+1. According to van den Dries & Miller (1996); Bolte et al. (2007b), there are some important facts of the o-minimal structure, shown as follows. (i) The o-minimal structure is stable under the sum, compositio...
work page 1996
-
[6]
Compressing deep convolutional networks using vector quantization
Yunchao Gong, Liu Liu, Ming Yang, and Lubomir Bourdev. Compressing deep convolutional networks using vector quantization. arXiv preprint arXiv:1412.6115,
-
[11]
Problem complexity and method efficiency in optimization
12 Published as a conference paper at ICLR 2025 Arkadij Semenoviˇc Nemirovskij and David Borisovich Yudin. Problem complexity and method efficiency in optimization
work page 2025
-
[14]
Upop: Uni- fied and progressive pruning for compressing vision-language transformers
Dachuan Shi, Chaofan Tao, Ying Jin, Zhendong Yang, Chun Yuan, and Jiaqi Wang. Upop: Uni- fied and progressive pruning for compressing vision-language transformers. arXiv preprint arXiv:2301.13741,
-
[16]
Compression of generative pre-trained language models via quantization
Chaofan Tao, Lu Hou, Wei Zhang, Lifeng Shang, Xin Jiang, Qun Liu, Ping Luo, and Ngai Wong. Compression of generative pre-trained language models via quantization. arXiv preprint arXiv:2203.10705,
Show all 34 references
-
[17]
Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Herve Jegou. Training data-efficient image transformers & distillation through attention. In International Conference on Machine Learning, volume 139, pp. 10347–10357, July 2021a. 13 ...
2025
-
[20]
Set λ = λ0 |Wij |P |W∗j | + |Wij |P |Wi∗| × (∥Xi∥2),which is the pruning metric of RIA Zhang et al. (2024) for k = 0 to Ts do # Calculate the loss ˆL = L(W0 ⊙ Mk) + 1 2ν ∥Mk − Γk∥2 2 # update Vk and mask Mk according to sub-gradient Mk+1 = Mk − καk∇Mk ˆL Vk+1 = Vk − αk∇Γk ˆL #...
2024
-
[23]
In the following, we present the sufficient descent property of Qk along the Lyapunov function F
B.1 S UFFICIENT DESCENT PROPERTY ALONG LYAPUNOV FUNCTION Let Pk := ( Mk, Γk), and Qk := ( Pk, gk−1), k∈ N. In the following, we present the sufficient descent property of Qk along the Lyapunov function F . Lemma. Suppose that L is continuously differentiable and ∇L is Lipschit...
2025
-
[24]
By the lower boundedness assumption of L(W ), both ¯L(P ) and F (Q) are lower bounded by their definitions, i.e., (9) and (15), respectively
Similarly, by (28), F (Qk) is also monotonically decreasing. By the lower boundedness assumption of L(W ), both ¯L(P ) and F (Q) are lower bounded by their definitions, i.e., (9) and (15), respectively. Therefore, both { ¯L(Pk)} and {F (Qk)} converge, and it is obvious that li...
2025
-
[25]
B.2 R ELATIVE ERROR PROPERTY In this subsection, we provide the bound of subgradient by the discrepancy of two successive iterates
Again by (29), 1 K KX k=0 ρ∥Pk+1 − Pk∥2 + D(Γk+1, Γk) < K−1α ¯L(P0), which implies 1 K PK k=0 ∥Pk+1 − Pk∥2 → 0 at a rate of O(1/K). B.2 R ELATIVE ERROR PROPERTY In this subsection, we provide the bound of subgradient by the discrepancy of two successive iterates. By the defini...
1998
-
[26]
When x /∈ dom(h), we set b∂h(x) = ∅. The limiting-subdifferential (or simply subdifferential) of h introduced in Mordukhovich (2006), written ∂h(x) at x ∈ dom(h), is defined by ∂h(x) := {v ∈ Rp : ∃xk → x, h(xk) → h(x), vk ∈ b∂h(xk) → v}. (35) A necessary (but not sufficient) c...
2006
-
[27]
(36) If h satisfies the KL property at each point of dom(∂h), h is called a KL function. KL functions include semialgebraic functions, real analytic functions, continuous subanalytic func- tions (Bolte et al., 2007a) and locally strongly convex functions, tame functions define...
1998
-
[28]
According to (Łojasiewicz, 1965; Bochnak et al.,
if it can be represented as D = s[ i=1 t\ j=1 {x ∈ Rp : Pij(x) = 0, Qij(x) > 0} , where Pij, Qij are real polynomial functions for 1 ≤ i ≤ s, 1 ≤ j ≤ t. According to (Łojasiewicz, 1965; Bochnak et al.,
1965
-
[29]
Some typical examples include polynomial functions, the indicator function of a semialgebraic set, and the Euclidean norm (Bochnak et al., 1998, page 26)
and (Shiota, 1997, I.2.9, page 52), the class of semialgebraic sets are stable under the operation of finite union, finite intersection, Cartesian product or complementation. Some typical examples include polynomial functions, the indicator function of a semialgebraic set, and...
1997
-
[30]
The function is said to be real analytic on V ⊂ U if it is real analytic at each u ∈ V (Krantz & Parks, 2002, Definition 1.1.5)
[Real analytic] A function h with domain an open setU ⊂ R and range the set of either all real or complex numbers, is said to be real analytic at u if the function h may be represented by a convergent power series on some interval of positive radius centered atu: h(x) = P∞ j=0...
2002
-
[31]
Let W i ∈ Rdi×di−1 be the weight matrix between the (i − 1)-th layer and the i-th layer for any i = 1,
Let d0 and dl be the number of units of input and output layers, respectively. Let W i ∈ Rdi×di−1 be the weight matrix between the (i − 1)-th layer and the i-th layer for any i = 1, . . . l1. According to Theorem 2, one major condition is to verify the introduced Lyapunov func...
1999
-
[34]
[Proof of Corollary 1] To justify this corollary, we only need to verify the associated Lyapunov function F satisfies Kurdyka-Łojasiewicz inequality
Proof. [Proof of Corollary 1] To justify this corollary, we only need to verify the associated Lyapunov function F satisfies Kurdyka-Łojasiewicz inequality. In this case and by (22), F can be rewritten as follows F (M, Γ, G) = α L(M, Γ) + 1 2ν ∥M − Γ∥2 + Ω(Γ) + Ω∗(g) − ⟨Γ, g⟩....
2002
-
[1983]
Sparse recovery via differential inclusions
Stanley Osher, Feng Ruan, Jiechao Xiong, Yuan Yao, and Wotao Yin. Sparse recovery via differential inclusions. Applied and Computational Harmonic Analysis , 41(2):436–469, 2016a. arXiv: 1406.7728. Stanley Osher, Feng Ruan, Jiechao Xiong, Yuan Yao, and Wotao Yin. Sparse recover...
-
[1998]
Albert: A lite bert for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. Albert: A lite bert for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942,
1909 arXiv
-
[1999]
Imagenet: A large-scale hier- archical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hier- archical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255,
2009
-
[2003]
Betcke, Matthias J
Martin Benning, Marta M. Betcke, Matthias J. Ehrhardt, and Carola-Bibiane Sch¨onlieB. Choose your path wisely: gradient descent in a bregman distance framework. arXiv preprint arXiv:1712.04045,
-
[2006]
Once-for-all: Train one network and specialize it for efficient deployment
Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, and Song Han. Once-for-all: Train one network and specialize it for efficient deployment. arXiv preprint arXiv:1908.09791,
1908 arXiv
-
[2009]
11 Published as a conference paper at ICLR 2025 Yanwei Fu, Chen Liu, Donghao Li, Xinwei Sun, Jinshan Zeng, and Yuan Yao
doi: 10.1109/CVPR.2009.5206848. 11 Published as a conference paper at ICLR 2025 Yanwei Fu, Chen Liu, Donghao Li, Xinwei Sun, Jinshan Zeng, and Yuan Yao. Dessilbi: Exploring structural sparsity of deep networks via differential inclusion paths. In International Conference on Ma...
2009
-
[2013]
Stochastic mirror descent on overparameterized nonlinear models: Convergence, implicit regularization, and generalization
Navid Azizan, Sahin Lale, and Babak Hassibi. Stochastic mirror descent on overparameterized nonlinear models: Convergence, implicit regularization, and generalization. arXiv preprint arXiv:1906.03830,
1906 arXiv
-
[2018]
The need for speed: Pruning transformers with one recipe
Samir Khaki and Konstantinos N Plataniotis. The need for speed: Pruning transformers with one recipe. arXiv preprint arXiv:2403.17921,
-
[2019]
Metadistiller: Network self- boosting via meta-learned top-down distillation
Benlin Liu, Yongming Rao, Jiwen Lu, Jie Zhou, and Cho-Jui Hsieh. Metadistiller: Network self- boosting via meta-learned top-down distillation. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XIV 16, pp. 694–709. Springer,
2020
-
[2020]
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531,
-
[2021]
Model compression via distillation and quantiza- tion
Antonio Polino, Razvan Pascanu, and Dan Alistarh. Model compression via distillation and quantiza- tion. arXiv preprint arXiv:1802.05668,
-
[2022]
A simple and effective pruning approach for large language models
Mingjie Sun, Zhuang Liu, Anna Bair, and J Zico Kolter. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695,
-
[2023]
Gohsp: A unified framework of graph and optimization-based heterogeneous structured pruning for vision transformer
Miao Yin, Burak Uzkent, Yilin Shen, Hongxia Jin, and Bo Yuan. Gohsp: A unified framework of graph and optimization-based heterogeneous structured pruning for vision transformer. arXiv preprint arXiv:2301.05345,
-
[2024]
(2024) pruning method
14 Published as a conference paper at ICLR 2025 APPENDIX A E XPERIMENTS DETAILS AND VISUALIZATION Experiments details As shown in Algorithm 2, instead of directly using our algorithm, we combined it with the RIA Zhang et al. (2024) pruning method. This is because post-training...
2024
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