---
tags:
- vllm
- sparsity
pipeline_tag: text-generation
license: llama3.1
base_model: meta-llama/Llama-3.1-8B
---
# Sparse-Llama-3.1-8B-2of4
## Model Overview
- **Model Architecture:** Llama-3.1-8B
- **Input:** Text
- **Output:** Text
- **Model Optimizations:**
- **Sparsity:** 2:4
- **Release Date:** 11/20/2024
- **Version:** 1.0
- **License(s):** [llama3.1](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B/blob/main/LICENSE)
- **Model Developers:** Neural Magic
This is the 2:4 sparse version of [Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B).
On the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), it achieves an average score of 62.16, compared to 63.19 for the dense model—demonstrating a **98.37% accuracy recovery**. On the [Mosaic Eval Gauntlet](https://github.com/mosaicml/llm-foundry/blob/main/scripts/eval/local_data/EVAL_GAUNTLET.md) benchmark (version v0.3), it achieves an average score of 53.85, versus 55.34 for the dense model—representing a **97.3% accuracy recovery**.
### Model Optimizations
This model was obtained by pruning all linear operators within transformer blocks to the 2:4 sparsity pattern: in each group of four weights, two are retained while two are pruned. In addition to pruning, the sparse model was trained with knowledge distillation for 13B tokens to recover the accuracy loss incurred by pruning. For pruning, we utilize optimized version of [SparseGPT](https://arxiv.org/abs/2301.00774) through [LLM-Compressor](https://github.com/vllm-project/llm-compressor), and for sparse training with knowledge distillation we utilize [SquareHead approach](https://arxiv.org/abs/2310.06927).
## Deployment with vLLM
This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend. vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
## Evaluation
This model was evaluated on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1) with the [vLLM](https://docs.vllm.ai/en/stable/) engine for faster inference. In addition to the OpenLLM benchmark, the model was evaluated on the [Mosaic Eval Gauntlet](https://github.com/mosaicml/llm-foundry/blob/main/scripts/eval/local_data/EVAL_GAUNTLET.md) benchmark (version v0.3). The evaluation results are summarized below.
### Accuracy
#### Open LLM Leaderboard evaluation scores
| Benchmark |
Llama-3.1-8B |
Sparse-Llama-3.1-8B-2of4 |
| ARC-C (25-shot) |
58.2 |
59.4 |
| MMLU (5-shot) |
65.4 |
60.6 |
| HellaSwag (10-shot) |
82.3 |
79.8 |
| WinoGrande (5-shot) |
78.3 |
75.9 |
| GSM8K (5-shot) |
50.7 |
56.3 |
| TruthfulQA (0-shot) |
44.2 |
40.9 |
| Average Score |
63.19 |
62.16 |
| Accuracy Recovery (%) |
100 |
98.37 |
#### Mosaic Eval Gauntlet evaluation scores
| Benchmark |
Llama-3.1-8B |
Sparse-Llama-3.1-8B-2of4 |
| World Knowledge |
59.4 |
55.6 |
| Commonsense Reasoning |
49.3 |
50.0 |
| Language Understanding |
69.8 |
69.0 |
| Symbolic Problem Solving |
40.0 |
37.1 |
| Reading Comprehension |
58.2 |
57.5 |
| Average Score |
55.34 |
53.85 |
| Accuracy Recovery (%) |
100 |
97.3 |