Update doc

ROCm_performance.md

@@ -21,9 +21,23 @@ The custom PagedAttention kernel is enabled for dtype: fp16, block-size=16, head
 
 ## Fp8 Quantization
 
-To use fp8 quantization, first step is to quantize your model to fp8 format. Please follow this [instruction](https://github.com/ROCm/vllm/tree/main/examples/fp8/quantizer) to generating a safetensor file that contains the quantized weights and the corresponding scaling factors of your model. The safetensor file should be placed under your model folder.
+To use fp8 quantization, first step is to quantize your model to fp8 format. 
 
-Then we can run a model with fp8 quantization using vllm. When creating `vllm.LLM` object, two additional parameters should be added: `quantization="fp8"` and `quantization_param_path={relative path of the safetensors with your model path}`.
+By default, rocm-vllm accepts the quantized weights generated by Quark quantizer. To do this, install quark and run the command:
+
+```
+python3 quantize_quark.py --model_dir [llama2 checkpoint folder] \
+                          --output_dir output_dir \
+                          --quant_scheme w_fp8_a_fp8_o_fp8 \
+                          --num_calib_data 128 \
+                          --export_safetensors \
+                          --no_weight_matrix_merge
+```
+For more details, please refer to Quark's documentation.
+
+To use ammo, please follow this [instruction](https://github.com/ROCm/vllm/tree/main/examples/fp8/quantizer), and set `VLLM_FP8_USE_AMMO=1`.
+
+Both quantizers generate a safetensor file that contains the quantized weights and the corresponding scaling factors of your model. The safetensor file should be placed under your model folder. Then we can run a model with fp8 quantization using vllm. When creating `vllm.LLM` object, two additional parameters should be added: `quantization="fp8"` and `quantization_param_path={relative path of the safetensors with your model path}`.
 
 ## Gemm Tuning for Fp8