Update README.md

README.md

@@ -17,12 +17,12 @@ 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) float8 dynamic activation and float8 weight quantization (per row granularity), by PyTorch team.
+[Phi4-mini](https://huggingface.co/microsoft/Phi-4-mini-instruct) model quantized with [torchao](https://huggingface.co/docs/transformers/main/en/quantization/torchao) float8 dynamic activation and float8 weight quantization (per row granularity), by PyTorch team. Use it directly, or serve using [vLLM](https://docs.vllm.ai/en/latest/) with 36% VRAM reduction, 15-20% speedup and little to no accuracy impact on H100.
 
 
 # Quantization Recipe
 
-First need to install the required packages:
+Install the required packages:
 
 ```
 pip install git+https://github.com/huggingface/transformers@main
@@ -31,7 +31,7 @@ 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
@@ -131,7 +131,6 @@ lm_eval --model hf --model_args pretrained=pytorch/Phi-4-mini-instruct-float8dq
 
 # Peak Memory Usage
 
-We can use the following code to get a sense of peak memory usage during inference:
 
 ## Results
 
@@ -143,6 +142,9 @@ We can use the following code to get a sense of peak memory usage during inferen
 
 ## Benchmark Peak Memory
 
+We can use the following code to get a sense of peak memory usage during inference:
+
+
 ```
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig