neuralmagic/mpt-7b-gsm8k-pruned60-pt

mpt-7b-gsm8k-pruned60

Paper: Sparse Finetuning for Inference Acceleration of Large Language Models
Code: https://github.com/neuralmagic/deepsparse/tree/main/research/mpt

This model was produced from a MPT-7B base model finetuned on the GSM8k dataset for 2 epochs, pruned to 60% with SparseGPT and retrained for 2 epochs with L2 distillation.

GSM8k zero-shot accuracy with lm-evaluation-harness : 28.8%

All MPT model weights are available on SparseZoo and CPU speedup for generative inference can be reproduced by following the instructions at DeepSparse

Model Links	Compression
neuralmagic/mpt-7b-gsm8k-quant	Quantization (W8A8)
neuralmagic/mpt-7b-gsm8k-pruned40-quant	Quantization (W8A8) & 40% Pruning
neuralmagic/mpt-7b-gsm8k-pruned50-quant	Quantization (W8A8) & 50% Pruning
neuralmagic/mpt-7b-gsm8k-pruned60-quant	Quantization (W8A8) & 60% Pruning
neuralmagic/mpt-7b-gsm8k-pruned70-quant	Quantization (W8A8) & 70% Pruning
neuralmagic/mpt-7b-gsm8k-pruned70-quant	Quantization (W8A8) & 75% Pruning
neuralmagic/mpt-7b-gsm8k-pruned80-quant	Quantization (W8A8) & 80% Pruning

For general questions on these models and sparsification methods, reach out to the engineering team on our community Slack.