Update README.md

README.md

@@ -18,8 +18,8 @@ tags:
   - **Input:** Text
   - **Output:** Text
 - **Model Optimizations:**
-  - **Activation quantization:** INT8
-  - **Weight quantization:** INT8
+  - **Activation quantization:** FP8
+  - **Weight quantization:** FP8
 - **Intended Use Cases:** Intended for commercial and research use multiple languages. Similarly to [Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B), this models is intended for assistant-like chat.
 - **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws).
 - **Release Date:** 11/27/2024
@@ -32,13 +32,13 @@ It achieves an average score of 71.00 on the [OpenLLM](https://huggingface.co/sp
 
 ### Model Optimizations
 
-This model was obtained by quantizing the weights of [Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B) to INT8 data type.
+This model was obtained by quantizing the weights of [Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B) to FP8 data type.
 This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x).
 Weight quantization also reduces disk size requirements by approximately 50%.
 
 Only weights and activations of the linear operators within transformers blocks are quantized.
-Weights are quantized with a symmetric static per-channel scheme, where a fixed linear scaling factor is applied between INT8 and floating point representations for each output channel dimension.
-Activations are quantized with a symmetric dynamic per-token scheme, computing a linear scaling factor at runtime for each token between INT8 and floating point representations.
+Weights are quantized with a symmetric static per-channel scheme, where a fixed linear scaling factor is applied between FP8 and floating point representations for each output channel dimension.
+Activations are quantized with a symmetric dynamic per-token scheme, computing a linear scaling factor at runtime for each token between FP8 and floating point representations.
 
 ## Deployment