tags:
- vllm
- sparsity
pipeline_tag: text-generation
license: llama3.1
base_model: neuralmagic/Sparse-Llama-3.1-8B-2of4
datasets:
- HuggingFaceH4/ultrachat_200k
language:
- en
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
| Metric | Llama-3.1-8B-ultrachat_200k | Sparse-Llama-3.1-8B-ultrachat_200k-2of4 |
| Win rate | 62.0 | 61.1 |