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junzzhu/
atomllama-33K-5x5-DigitMesh-sparse-q8

Name: atomllama-33K-5x5-DigitMesh-sparse-q8
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AtomLlama-33K-5x5-DigitMesh-Sparse-Q8

An INT8 quantized version of atomllama-33K-5x5-DigitMesh-sparse for ultra-efficient 5×5 digit mesh recognition.

Model Description

This is a 50% sparse + INT8 quantized variant of the AtomLlama-33K-5x5-DigitMesh model, combining structured sparsity with Quantization Aware Training (QAT). This dual compression approach maintains digit recognition accuracy while significantly reducing model size and computational requirements.

Key Features

Base Model: junzzhu/atomllama-33K-5x5-DigitMesh-sparse
Sparsity: ~50% (unstructured)
Quantization: INT8 with Sparse QAT
Parameters: ~33K total, ~16.5K non-zero
Architecture: LlamaForCausalLM
Task: 5×5 binary digit mesh recognition
Compression: ~3x smaller than original model (46KB vs. 137KB)

Usage

Basic Inference with Transformers

python

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model_path = "./models/atomllama-33K-5x5-DigitMesh-sparse-q8"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    dtype="auto",
    device_map="auto"
)

# Example: Classify a 5x5 binary digit pattern (digit "0")
pattern = "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"
prompt = f"{pattern} <SEP>"

# Tokenize and generate prediction
inputs = tokenizer([prompt], return_tensors="pt").to(model.device)
inputs.pop("token_type_ids", None)

outputs = model.generate(
    **inputs,
    max_new_tokens=1,
    do_sample=False
)

# Decode the prediction
prediction = tokenizer.decode(
    outputs[0][len(inputs.input_ids[0]):],
    skip_special_tokens=True
).strip()

print(f"Predicted digit: {prediction}")  # Expected: "D0"

Compression Details

Sparsity

Type: Unstructured (weights pruned individually based on importance)
Target Sparsity: 50%
Method: SparseGPT with Hessian-based importance scoring

Quantization

Precision: INT8 (8-bit integers)
Method: Quantization Aware Training (QAT)
Framework: Axolotl Sparse QAT Integration

License

Apache-2.0

Citation

bibtex

@misc{atomllama-33k-digitMesh-sparse-q8,
  title={AtomLlama-33K-5x5-DigitMesh-Sparse-Q8: A 50% Sparse INT8 Quantized Model for Digit Recognition},
  author={Jun Zhu},
  year={2026},
  howpublished={\url{https://huggingface.co/junzzhu/atomllama-33K-5x5-DigitMesh-sparse-q8}}
}