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mohammad-mozaffari/
qwen2.5_0.5b-PATCH-35Sparse

Name: qwen2.5_0.5b-PATCH-35Sparse
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PATCH

qwen2.5_0.5b-PATCH-35Sparse

This checkpoint (Qwen-2.5 0.5B, PATCH-Joint, 35% sparsity): 41.15% average zero-shot accuracy, 13.84 WikiText2 perplexity.

Paper

GitHub

This repository hosts a mask only release for the paper PATCH: Learnable Tile-level Hybrid Sparsity for LLMs. PATCH (Pruning with a Learnable Tile-level Configuration for Hybrid Sparsity) learns a structured mask on frozen pretrained weights, assigning each tile as dense (0% sparsity) or 2:4 sparse (50% sparsity) to hit a flexible global sparsity target while staying hardware-friendly.

Because PATCH/MaskLLM keep the base weights frozen, we distribute only the binary keep/prune mask (bit-packed in mask.npz) - no weight values. You recover the sparse model by downloading the original base model and applying the mask.

Base model: Qwen/Qwen2.5-0.5B
Method: PATCH-Joint | Target sparsity: 35% | Pattern: Dense / 2:4 tiles
Measured mask sparsity: 35.03%.

Results (Qwen-2.5 0.5B)

Sparsity	Method	Pattern	Avg Acc (% ↑)	WikiText2 PPL (↓)
0%	Dense	-	46.00	12.08
50%	Magnitude	2:4	30.16	6734.97
50%	Wanda	2:4	32.97	72.48
50%	SparseGPT	2:4	34.81	36.59
50%	Thanos	2:4	34.85	37.32
50%	ProxSparse	2:4	32.05	111.05
50%	MaskLLM	2:4	39.33	15.22
45%	PATCH-Joint	Dense/2:4	40.29	14.57
35%	PATCH-Joint ⭐	Dense/2:4	41.15	13.84
25%	PATCH-Joint	Dense/2:4	42.39	13.47

Per-task zero-shot accuracy (%) for this checkpoint:

MMLU	PIQA	ARC-E	ARC-C	WinoG.	OBQA	RACE	HellaS.	Average
29.04	68.88	60.40	26.37	55.09	20.40	32.44	36.58	41.15

All numbers are from the PATCH paper (arXiv:2509.23410); accuracy is the average over MMLU, PIQA, ARC-Easy, ARC-Challenge, Winogrande, OpenBookQA, RACE and HellaSwag, evaluated with the LM-Evaluation-Harness. PPL is WikiText2.

Training hyper-parameters

Hyper-parameter	Value
Fine-tuning dataset	SlimPajama (2B tokens)
Training steps	2000
Global batch size	256
Sequence length	4096
Mask tile size	128 x 128 (hardware tiles: 128x128 / 128x64 / 64x128 / 64x64)
Logits init.	N(0, 0.014)
Tile-logit prior	SparseGPT (strength 3)
Regularization scope	Global (single target density)
Evaluation	LM-Eval-Harness (8 zero-shot tasks) + WikiText2 PPL @ seqlen 4096
Hardware	1 node x 4 GPUs, data parallel (HuggingFace Trainer)
Optimizer	Adam
Learning rate	1e-3
Gumbel scaling (kappa)	25 -> 350
Gumbel temp (tau)	2 -> 0.05
Sparsity reg. (lambda1)	7
Weight reg. (lambda2)	10

How to use

python

from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM
import torch
from load_patch_mask import apply_patch_mask  # shipped in this repo

npz = hf_hub_download(repo_id="mohammad-mozaffari/qwen2.5_0.5b-PATCH-35Sparse", filename="mask.npz")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B", torch_dtype=torch.bfloat16)
apply_patch_mask(model, npz)   # zeroes the pruned weights in place

Or from the command line:

python load_patch_mask.py --base_model Qwen/Qwen2.5-0.5B --mask_repo mohammad-mozaffari/qwen2.5_0.5b-PATCH-35Sparse

Speedup on real hardware requires a 2:4-aware / hybrid sparse kernel; see the GitHub repository and STOICC.

License

The released mask is a derivative of the base model and is distributed under the base model's license (apache-2.0). You must comply with that license and obtain access to the base model separately.

The mask-generation code is released under the MIT license (see the PATCH repository).

Citation

bibtex

@article{hourri2025patch,
    title  = {PATCH: Learnable Tile-level Hybrid Sparsity for LLMs},
    author = {Hourri, Younes and Mozaffari, Mohammad and Mehri Dehnavi, Maryam},
    year   = 2025,
    journal = {arXiv preprint arXiv:2509.23410}
}