---
language:
  - en
tags:
  - llama
  - causal-lm
  - digit-recognition
  - sparse-model
  - sparsegpt
  - model-compression
  - 50-percent-sparse
license: apache-2.0
base_model: junzzhu/atomllama-33K-5x5-DigitMesh
library_name: transformers
pipeline_tag: text-generation
---

# AtomLlama-33K-5x5-DigitMesh-Sparse

A 50% sparse version of [atomllama-33K-5x5-DigitMesh](https://huggingface.co/junzzhu/atomllama-33K-5x5-DigitMesh) for efficient 5×5 digit mesh recognition.

## Model Description

This is a **~50% unstructured sparse** variant of the AtomLlama-33K-5x5-DigitMesh model, pruned using the SparseGPT algorithm. Half of the model weights have been set to zero while maintaining digit recognition accuracy through second-order optimization.

### Key Features

- **Base Model**: [junzzhu/atomllama-33K-5x5-DigitMesh](https://huggingface.co/junzzhu/atomllama-33K-5x5-DigitMesh)
- **Sparsity**: ~50% (unstructured)
- **Pruning Method**: SparseGPT with Hessian-based importance scoring
- **Parameters**: ~33K total, ~16.5K non-zero
- **Architecture**: LlamaForCausalLM
- **Task**: 5×5 binary digit mesh recognition

## Usage

### Serving with vLLM

```bash
python -m vllm.entrypoints.openai.api_server \
  --model junzzhu/atomllama-33K-5x5-DigitMesh-sparse \
  --max-model-len 32
```

### Example Inference

```bash
curl http://localhost:8000/v1/completions \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "junzzhu/atomllama-33K-5x5-DigitMesh-sparse",
    "prompt": "1 1 1 1 1 1 0 0 0 1 1 0 0 0 1 1 0 0 0 1 1 1 1 1 1 <SEP>",
    "max_tokens": 1,
    "temperature": 0
  }'
```
Expected output: `D0`

## Sparsity Details

- **Type**: Unstructured (weights pruned individually based on importance)
- **Target Sparsity**: 50%
- **Calibration**: Pruned using digit pattern activations
- **Benefits**: Reduced memory footprint and potential inference speedup with sparse tensor libraries

## License

Apache-2.0

## Citation

```bibtex
@misc{atomllama-33k-digitMesh-sparse,
  title={AtomLlama-33K-5x5-DigitMesh-Sparse: A 50% Sparse Model for Digit Recognition},
  author={Jun Zhu},
  year={2026},
  howpublished={\url{https://huggingface.co/junzzhu/atomllama-33K-5x5-DigitMesh-sparse}}
}