Update README.md

README.md

@@ -13,11 +13,10 @@ base_model:
 - **ROCm:** 7.0
 - **Operating System(s):** Linux
 - **Inference Engine:** [vLLM](https://docs.vllm.ai/en/latest/)
-- **Model Optimizer:** [AMD-Quark](https://quark.docs.amd.com/latest/index.html) (V0.11)
+- **Model Optimizer:** [AMD-Quark](https://quark.docs.amd.com/latest/index.html) (V0.11.1)
   - **moe**
     - **Weight quantization:** MOE-only, OCP MXFP4, Static
     - **Activation quantization:** MOE-only, OCP MXFP4, Dynamic
-  - **KV cache quantization:** OCP FP8, Static
 - **Calibration Dataset:** [Pile](https://huggingface.co/datasets/mit-han-lab/pile-val-backup)
 
 This model was built with GLM-4.7 model by applying [AMD-Quark](https://quark.docs.amd.com/latest/index.html) for MXFP4 quantization.
@@ -25,48 +24,22 @@ This model was built with GLM-4.7 model by applying [AMD-Quark](https://quark.do
 # Model Quantization
 
 The model was quantized from [zai-org/GLM-4.7](https://huggingface.co/zai-org/GLM-4.7) using [AMD-Quark](https://quark.docs.amd.com/latest/index.html). The weights and activations are quantized to MXFP4.
-AMD-Quark has been installed from source code inside the Docker image `rocm/vllm-private:vllm_dev_base_mxfp4_20260122`.
 
 **Quantization scripts:**
 
-Note that GLM-4.7 is not in the built-in model template list in Quark V0.11, it has to be registered before quantization.
-
-- **Step1:** Register model template: creat fle `Quark/examples/torch/language_modeling/llm_ptq/quantize_glm.py`
-```
-import runpy
-from quark.torch import LLMTemplate
-
-# Register GLM-4 MoE template
-glm4_moe_template = LLMTemplate(
-    model_type="glm4_moe",
-    kv_layers_name=["*k_proj", "*v_proj"],
-    q_layer_name="*q_proj",
-    exclude_layers_name=["lm_head","*mlp.gate","*self_attn*","*shared_experts.*","*mlp.down_proj","*mlp.gate_proj","*mlp.up_proj"],
-)
-LLMTemplate.register_template(glm4_moe_template)
-print(f"[INFO]: Registered template '{glm4_moe_template.model_type}'")
-
-# Run quantize_quark.py
-# Get the absolute path to the quantize_quark.py script
-quantize_script = "/app/Quark/examples/torch/language_modeling/llm_ptq/quantize_quark.py"
-
-runpy.run_path(quantize_script, run_name="__main__")
-```
-- **Step2:** Quantize with the quantize_glm.py
 ```
 export CUDA_VISIBLE_DEVICES=0,1,2,3
 export MODEL_DIR=zai-org/GLM-4.7
 export output_dir=amd/GLM-4.7-MXFP4
 
 exclude_layers="*self_attn* *mlp.gate lm_head *mlp.gate_proj *mlp.up_proj *mlp.down_proj *shared_experts.*"
-python3 quantize_glm.py --model_dir $MODEL_DIR \
-                        --quant_scheme mxfp4 \
-                        --num_calib_data 128 \
-                        --exclude_layers $exclude_layers \
-                        --kv_cache_dtype fp8 \
-                        --model_export hf_format \
-                        --output_dir $output_dir \
-                        --multi_gpu
+python3 quantize_quark.py --model_dir $MODEL_DIR \
+                          --quant_scheme mxfp4 \
+                          --num_calib_data 128 \
+                          --exclude_layers $exclude_layers \
+                          --model_export hf_format \
+                          --output_dir $output_dir \
+                          --multi_gpu
 ```
 
 # Deployment
@@ -104,7 +77,8 @@ The model was evaluated on GSM8K benchmarks.
 
 ### Reproduction
 
-The GSM8K results were obtained using the `lm-evaluation-harness` framework, based on the Docker image `rocm/vllm-private:vllm_dev_base_mxfp4_20260122`, with vLLM, lm-eval and amd-quark compiled and installed from source inside the image.
+The GSM8K results were obtained using the `lm-evaluation-harness` framework, based on the Docker image `rocm/vllm-private:vllm_dev_base_mxfp4_20260122`, with vLLM, lm-eval compiled and installed from source inside the image.
+The Docker image contains the necessary vLLM code modifications to support this model.
 
 #### Launching server
 ```
@@ -112,8 +86,7 @@ vllm serve amd/GLM-4.7-MXFP4 \
     --tensor-parallel-size 4 \
     --tool-call-parser glm47 \
     --reasoning-parser glm45 \
-    --enable-auto-tool-choice \
-    --kv_cache_dtype fp8
+    --enable-auto-tool-choice
 ```
 
 #### Evaluating model in a new terminal