RedHatAI/Sparse-Llama-3.1-8B-ultrachat_200k-2of4

Sparse-Llama-3.1-8B-ultrachat_200k-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 a multi-turn conversational AI model obtained by fine-tuning the 2:4 sparse Sparse-Llama-3.1-8B-2of4 on the ultrachat_200k dataset. On the AlpacaEval benchmark (version 1), it achieves a score of 61.1, compared to 62.0 for the fine-tuned dense model Llama-3.1-8B-ultrachat_200k — demonstrating a 98.5% accuracy recovery.

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 Neural Magic's fork of AlpacaEval benchmark. We adopt the same setup as in Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment, using version 1 of the benchmark and Llama-2-70b-chat as the annotator.

Accuracy

AlpacaEval Benchmark

<table> <tr> <td><strong>Metric</strong></td> <td style="text-align: center"><strong>Llama-3.1-8B-ultrachat200k</strong></td> <td style="text-align: center"><strong>Sparse-Llama-3.1-8B-ultrachat200k-2of4</strong></td> </tr> <tr> <td>Win rate</td> <td style="text-align: center">62.0</td> <td style="text-align: center">61.1</td> </tr> </table>