---
base_model:
- meta-llama/Llama-2-13b-hf
library_name: transformers
license: llama2
tags:
- pruning
- sparsity
- 2:4-sparsity
- patch
- maskllm
- mask
pipeline_tag: text-generation
---
# llama_2_13b-PATCH-35Sparse
> **This checkpoint** (LLaMA-2 13B, PATCH-Tile, 35% sparsity): **54.60%** average zero-shot accuracy, **5.44** WikiText2 perplexity.
[](https://arxiv.org/abs/2509.23410) [](https://github.com/Paramathic/patch)
This repository hosts a **mask only** release for the paper
**[PATCH: Learnable Tile-level Hybrid Sparsity for LLMs](https://arxiv.org/abs/2509.23410)**.
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: [`meta-llama/Llama-2-13b-hf`](https://huggingface.co/meta-llama/Llama-2-13b-hf)
- Method: **PATCH-Tile** | Target sparsity: **35%** | Pattern: **Dense / 2:4 tiles**
- Measured mask sparsity: **36.90%** over 280 Linear layers (12,687,769,600 weights).
## Results (LLaMA-2 13B)
| Sparsity | Method | Pattern | Avg Acc (% ↑) | WikiText2 PPL (↓) |
|---|---|---|---|---|
| 0% | Dense | - | 58.38 | 4.89 |
| 50% | Magnitude | 2:4 | 45.94 | 8.89 |
| 50% | Wanda | 2:4 | 47.95 | 8.91 |
| 50% | SparseGPT | 2:4 | 49.67 | 8.86 |
| 50% | Thanos | 2:4 | 49.33 | 8.80 |
| 50% | ProxSparse | 2:4 | 50.80 | 7.11 |
| 45% | PATCH-Tile | Dense/2:4 | 53.24 | 5.85 |
| 35% | **PATCH-Tile** ⭐ | Dense/2:4 | **54.60** | **5.44** |
| 25% | PATCH-Tile | Dense/2:4 | 56.31 | 5.00 |
Per-task zero-shot accuracy (%) for this checkpoint:
| MMLU | PIQA | ARC-E | ARC-C | WinoG. | OBQA | RACE | HellaS. | **Average** |
|---|---|---|---|---|---|---|---|---|
| 41.07 | 77.75 | 75.55 | 44.03 | 70.80 | 31.20 | 39.43 | 56.95 | **54.60** |
All numbers are from the PATCH paper ([arXiv:2509.23410](https://arxiv.org/abs/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) | 100 -> 500 |
| Gumbel temp (tau) | 2 -> 0.05 |
| Sparsity reg. (lambda1) | 2 |
| Weight reg. (lambda2) | 0.05 |
## 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/llama_2_13b-PATCH-35Sparse", filename="mask.npz")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf", torch_dtype=torch.bfloat16)
apply_patch_mask(model, npz) # zeroes the pruned weights in place
```
Or from the command line:
```bash
python load_patch_mask.py --base_model meta-llama/Llama-2-13b-hf --mask_repo mohammad-mozaffari/llama_2_13b-PATCH-35Sparse
```
Speedup on real hardware requires a 2:4-aware / hybrid sparse kernel; see the
[GitHub repository](https://github.com/Paramathic/patch) and [STOICC](https://github.com/Paramathic/stoicc).
## License
The released mask is a derivative of the base model and is distributed under the
base model's license (**`llama2`**). You must comply with that license and
obtain access to the base model separately.
> Use governed by the Llama 2 Community License.
The mask-generation code is released under the MIT license (see the
[PATCH repository](https://github.com/Paramathic/patch)).
## 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}
}
```
