Update README.md

README.md

@@ -18,6 +18,7 @@ license: apache-2.0
 - **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English.
 - **Release Date:** 6/26/2024
 - **Version:** 1.0
+- **License(s):** [apache-2.0](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md)
 - **Model Developers:** Neural Magic
 
 Quantized version of [Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3).
@@ -28,7 +29,7 @@ It achieves an average score of 65.85 on the [OpenLLM](https://huggingface.co/sp
 This model was obtained by quantizing the weights and activations of [Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) to FP8 data type, ready for inference with vLLM >= 0.5.0.
 This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.
 
-Only the weights and activations of the linear operators within transformers blocks are quantized. Symmetric per-tensor quantization is applied, in which a linear scaling per output dimension maps the FP8 representations of the quantized weights and activations.
+Only the weights and activations of the linear operators within transformers blocks are quantized. Symmetric per-tensor quantization is applied, in which a single linear scaling maps the FP8 representations of the quantized weights and activations.
 [AutoFP8](https://github.com/neuralmagic/AutoFP8) is used for quantization with 10 repeats of every token in random order.
 
 ## Deployment