SparseLlama-3-8B-pruned_50.2of4-FP8
This repo contains model files for a 2:4 (N:M) sparse Meta-Llama-3-8B model pruned in one-shot with SparseGPT, and then additionally retrained with the SquareHead knowledge distillation while maintaining the 2:4 sparsity mask. It was then quantized using AutoFP8 to FP8 weights and activations with per-tensor scales, calibrated on UltraChat2k.
Note: The unquantized SparseLlama-3-8B-pruned_50.2of4-FP8 is still a work in progress and subject to change. This FP8 model will be updated once the unquantized model is updated too.
Evaluation Benchmark Results
Model evaluation results obtained via lm-evaluation-harness following the configuration of Open LLM Leaderboard.
| Benchmark | Meta-Llama-3-8B | SparseLlama-3-8B-pruned_50.2of4 | SparseLlama-3-8B-pruned_50.2of4-FP8 (this model) |
|---|---|---|---|
| ARC-c 25-shot |
59.47% | 57.76% | 58.02% |
| MMLU 5-shot |
65.29% | 60.44% | 60.71% |
| HellaSwag 10-shot |
82.14% | 79.97% | 79.61% |
| WinoGrande 5-shot |
77.27% | 77.19% | 76.32% |
| GSM8K 5-shot |
44.81% | 47.92% | 49.36% |
| TruthfulQA 0-shot |
43.96% | 41.02% | 40.82% |
| Average Accuracy |
62.16% | 60.72% | 60.81% |
| Recovery | 100% | 97.68% | 97.83% |
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