Update README.md

README.md

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 # mpt-7b-gsm8k-pruned40-quant
 
-This model was produced from a MPT-7B base model finetuned on the GSM8k dataset with pruning and quantization applied using [SparseGPT](https://arxiv.org/abs/2301.00774). Then it was exported for optimized inference with [DeepSparse](https://github.com/neuralmagic/deepsparse/tree/main/research/mpt).
+**Paper**: [https://arxiv.org/pdf/xxxxxxx.pdf](https://arxiv.org/pdf/xxxxxxx.pdf)  
+**Code**: https://github.com/neuralmagic/deepsparse/tree/main/research/mpt 
 
-GSM8k zero-shot accuracy with [lm-evaluation-harness](https://github.com/neuralmagic/lm-evaluation-harness) : 30.33% (dense fp32 is 28.2%)
+This model was produced from a [MPT-7B base model](https://huggingface.co/neuralmagic/mpt-7b-gsm8k-pt) finetuned on the GSM8k dataset with pruning applied using [SparseGPT](https://arxiv.org/abs/2301.00774) and retrain for 2 epochs with L2 distillation. Then it was exported for optimized inference with [DeepSparse](https://github.com/neuralmagic/deepsparse/tree/main/research/mpt).
+
+GSM8k zero-shot accuracy with [lm-evaluation-harness](https://github.com/neuralmagic/lm-evaluation-harness) : 30.33% (FP32 baseline is 28.2%)
 
 ### Usage
 
@@ -29,6 +32,7 @@ All MPT model weights are available on [SparseZoo](https://sparsezoo.neuralmagic
 | [neuralmagic/mpt-7b-gsm8k-pruned50-quant](https://huggingface.co/neuralmagic/mpt-7b-gsm8k-pruned50-quant) | Quantization (W8A8) & 50% Pruning |
 | [neuralmagic/mpt-7b-gsm8k-pruned60-quant](https://huggingface.co/neuralmagic/mpt-7b-gsm8k-pruned60-quant) | Quantization (W8A8) & 60% Pruning |
 | [neuralmagic/mpt-7b-gsm8k-pruned70-quant](https://huggingface.co/neuralmagic/mpt-7b-gsm8k-pruned70-quant) | Quantization (W8A8) & 70% Pruning |
+| [neuralmagic/mpt-7b-gsm8k-pruned70-quant](https://huggingface.co/neuralmagic/mpt-7b-gsm8k-pruned75-quant) | Quantization (W8A8) & 75% Pruning |
 | [neuralmagic/mpt-7b-gsm8k-pruned80-quant](https://huggingface.co/neuralmagic/mpt-7b-gsm8k-pruned80-quant) | Quantization (W8A8) & 80% Pruning |