Sparse Finetuning MPT
Collection
Explore our breakthrough in sparse fine-tuning LLMs! Our novel method maintains downstream accuracy even with >70% sparsity.
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mpt-7b-gsm8k
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 and contains the original PyTorch weights.
GSM8k zero-shot accuracy with lm-evaluation-harness : 28.2%
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.
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