Instructions to use junzzhu/atomllama-33K-5x5-DigitMesh-sparse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use junzzhu/atomllama-33K-5x5-DigitMesh-sparse with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="junzzhu/atomllama-33K-5x5-DigitMesh-sparse")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("junzzhu/atomllama-33K-5x5-DigitMesh-sparse")
model = AutoModelForCausalLM.from_pretrained("junzzhu/atomllama-33K-5x5-DigitMesh-sparse")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use junzzhu/atomllama-33K-5x5-DigitMesh-sparse with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "junzzhu/atomllama-33K-5x5-DigitMesh-sparse"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "junzzhu/atomllama-33K-5x5-DigitMesh-sparse",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/junzzhu/atomllama-33K-5x5-DigitMesh-sparse

SGLang

How to use junzzhu/atomllama-33K-5x5-DigitMesh-sparse with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "junzzhu/atomllama-33K-5x5-DigitMesh-sparse" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "junzzhu/atomllama-33K-5x5-DigitMesh-sparse",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "junzzhu/atomllama-33K-5x5-DigitMesh-sparse" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "junzzhu/atomllama-33K-5x5-DigitMesh-sparse",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use junzzhu/atomllama-33K-5x5-DigitMesh-sparse with Docker Model Runner:
```
docker model run hf.co/junzzhu/atomllama-33K-5x5-DigitMesh-sparse
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

AtomLlama-33K-5x5-DigitMesh-Sparse

A 50% sparse version of 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
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

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

Example Inference

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

@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}}
}

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Safetensors

Model size

33.8k params

Tensor type

F32

Model tree for junzzhu/atomllama-33K-5x5-DigitMesh-sparse

Base model

junzzhu/atomllama-33K-5x5-DigitMesh

Finetuned

(1)

this model

Quantizations

1 model