Update README.md

README.md

@@ -17,11 +17,11 @@ base_model:
 pipeline_tag: text-generation
 ---
 
-[Phi4-mini](https://huggingface.co/microsoft/Phi-4-mini-instruct) model quantized with [torchao](https://huggingface.co/docs/transformers/main/en/quantization/torchao) int4 weight only quantization, using [hqq](https://mobiusml.github.io/hqq_blog/) algorithm for improved accuracy, by PyTorch team.
+[Phi4-mini](https://huggingface.co/microsoft/Phi-4-mini-instruct) quantized with [torchao](https://huggingface.co/docs/transformers/main/en/quantization/torchao) int4 weight only quantization, using [hqq](https://mobiusml.github.io/hqq_blog/) algorithm for improved accuracy, by PyTorch team.
 
 # Quantization Recipe
 
-First need to install the required packages:
+Install the required packages:
 ```
 pip install git+https://github.com/huggingface/transformers@main
 pip install --pre torchao --index-url https://download.pytorch.org/whl/nightly/cu126
@@ -29,7 +29,7 @@ pip install torch
 pip install accelerate
 ```
 
-We used following code to get the quantized model:
+Use the following code to get the quantized model:
 ```
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig
@@ -159,7 +159,9 @@ We can use the following code to get a sense of peak memory usage during inferen
 | Peak Memory (GB) | 8.91           | 2.98 (67% reduction)           |
 
 
-## Benchmark Peak Memory
+## Peak Memory
+
+We can use the following code to get a sense of peak memory usage during inference:
 
 ```
 import torch
@@ -203,9 +205,7 @@ print(f"Peak Memory Usage: {mem:.02f} GB")
 
 # Model Performance
 
-Our int4wo is only optimized for batch size 1, so we'll see slowdown in larger batch sizes, we expect this to be used in local server deployment for single or a few users
-and decode tokens per second will be more important than time to first token.
-
+Our int4wo is only optimized for batch size 1, so expect some slowdown with larger batch sizes, we expect this to be used in local server deployment for single or a few users where the decode tokens per second will matters more than the time to first token.
 
 ## Results (A100 machine)
 | Benchmark (Latency)              |                |                          |