Sparse-Llama-3.1-8B-gsm8k-2of4

Sparse-Llama-3.1-8B-gsm8k-2of4

Model Overview

• Model Architecture: Llama-3.1-8B
  • Input: Text
  • Output: Text
• Model Optimizations:
  • Sparsity: 2:4
• Release Date: 11/21/2024
• Version: 1.0
• License(s): llama3.1
• Model Developers: Neural Magic

This is AI model especialized in grade-school math obtained by fine-tuning the 2:4 sparse Sparse-Llama-3.1-8B-2of4 on the GSM8k dataset. It achieves 66.9% 0-shot accuracy on the test set of GSM8k, compared to 66.3% for the fine-tuned dense model Llama-3.1-8B-gsm8k — demonstrating over 100% accuracy recovery. In constrast, the pretrained Llama-3.1-8B achieves 50.7% 5-shot accuracy and the sparse foundational Sparse-Llama-3.1-8B-2of4 model achieves 56.3% 5-shot accuracy.

Model Optimizations

This inherits the optimizations from its parent, Sparse-Llama-3.1-8B-2of4. Namely, all linear operators within transformer blocks were pruned to the 2:4 sparsity pattern: in each group of four weights, two are retained while two are pruned.

Deployment with vLLM

This model can be deployed efficiently using the vLLM backend. vLLM aslo supports OpenAI-compatible serving. See the documentation for more details.

Evaluation

This model was evaluated on the lm-evaluation-harness.

Accuracy

GSM8k Benchmark

<table> <tr> <td><strong>Metric</strong></td> <td style="text-align: center"><strong>Llama-3.1-8B<br>(5-shot)</strong></td> <td style="text-align: center"><strong>Sparse-Llama-3.1-8B-2of4<br>(5-shot)</strong></td> <td style="text-align: center"><strong>Llama-3.1-8B-gsm8k<br>(0-shot)</strong></td> <td style="text-align: center"><strong>Sparse-Llama-3.1-8B-gsm8k-2of4<br>(0-shot)</strong></td> </tr> <tr> <td>Accuracy</td> <td style="text-align: center">50.7%</td> <td style="text-align: center">56.3%</td> <td style="text-align: center">66.3%</td> <td style="text-align: center">66.9%</td> </tr> </table>