Update README.md

README.md

@@ -1,6 +1,131 @@
 ---
 license: mit
 ---
-# Disclaimer
+# Model Overview
 
-This model is provided for experimental purposes only. Its accuracy, stability, and suitability for deployment are not guaranteed. Users are advised to independently evaluate the model before any practical or production use.
+- **Model Architecture:** GLM-5
+  - **Input:** Text
+  - **Output:** Text
+- **Supported Hardware Microarchitecture:** AMD MI350/MI355
+- **ROCm:** 7.1.0
+- **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) (V0.11.1)
+  - **moe**
+    - **Weight quantization:** MOE-only, OCP MXFP4, Static
+    - **Activation quantization:** MOE-only, OCP MXFP4, Dynamic
+- **Calibration Dataset:** [Pile](https://huggingface.co/datasets/mit-han-lab/pile-val-backup)
+
+This model was built with GLM-5 model by applying [AMD-Quark](https://quark.docs.amd.com/latest/index.html) for MXFP4 quantization.
+
+# Model Quantization
+
+The model was quantized from [zai-org/GLM-5](https://huggingface.co/zai-org/GLM-5) using [AMD-Quark](https://quark.docs.amd.com/latest/index.html). The weights and activations are quantized to MXFP4.
+
+**Quantization scripts:**
+
+```python
+from quark.torch import LLMTemplate, ModelQuantizer
+
+# --- Register GLM-5 template ---
+GLM5_template = LLMTemplate(
+    model_type="glm_moe_dsa",
+    kv_layers_name=["*kv_a_proj_with_mqa", "*kv_b_proj"],
+    q_layer_name="*q_a_proj",
+    exclude_layers_name=["lm_head"],
+)
+LLMTemplate.register_template(GLM5_template)
+print(f"[INFO]: Registered template '{GLM5_template.model_type}'")
+
+# --- Configuration ---
+model_dir = "zai-org/GLM-5"
+output_dir = "amd/GLM-5-MXFP4"
+quant_scheme = "mxfp4"
+exclude_layers = [
+    "*self_attn*",
+    "*mlp.gate",
+    "*lm_head",
+    "*mlp.gate_proj",
+    "*mlp.up_proj",
+    "*mlp.down_proj",
+    "*shared_experts*",
+]
+
+# --- Build quant config from template ---
+template = LLMTemplate.get("glm_moe_dsa")
+quant_config = template.get_config(scheme=quant_scheme, exclude_layers=exclude_layers)
+
+# --- File-to-file quantization (memory-efficient, no full model loading) ---
+quantizer = ModelQuantizer(quant_config)
+quantizer.direct_quantize_checkpoint(
+    pretrained_model_path=model_dir,
+    save_path=output_dir,
+)
+
+print(f"[INFO]: Quantization complete. Output saved to {output_dir}")
+
+```
+
+# Deployment
+### Use with vLLM
+
+This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend.
+
+## Evaluation
+The model was evaluated on GSM8K benchmarks. 
+
+### Accuracy
+
+<table>
+  <tr>
+   <td><strong>Benchmark</strong>
+   </td>
+   <td><strong>GLM-5 </strong>
+   </td>
+   <td><strong>GLM-5-MXFP4(this model)</strong>
+   </td>
+   <td><strong>Recovery</strong>
+   </td>
+  </tr>
+  <tr>
+   <td>GSM8K (flexible-extract)
+   </td>
+   <td>95.45
+   </td>
+   <td>95.00
+   </td>
+   <td>99.53%
+   </td>
+  </tr>
+</table>
+
+### Reproduction
+
+The GSM8K results were obtained using the `lm-evaluation-harness` framework, based on the Docker image `rocm/pytorch-private:vllm_glm5_0225`, 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
+```
+export VLLM_ROCM_USE_AITER=1
+export VLLM_ROCM_USE_AITER_FP8BMM=0
+export VLLM_ROCM_USE_AITER_FP4BMM=0
+vllm serve  amd/GLM-5-MXFP4 \
+  -tp 8 \
+  --block-size 1 \
+  --trust-remote-code \
+  --max-model-len 4096
+```
+
+#### Evaluating model in a new terminal
+```
+lm_eval \
+  --model local-completions \
+  --model_args '{"model": "amd/GLM-5-MXFP4", "base_url": "http://localhost:8000/v1/completions", "num_concurrent": 32, "max_retries": 10, "max_gen_toks": 2048, "tokenizer_backend":"None","tokenized_requests":"False" }' \
+  --tasks gsm8k \
+  --batch_size auto \
+  --num_fewshot 5 \
+  --trust_remote_code  
+```
+
+# License
+Modifications Copyright(c) 2025 Advanced Micro Devices, Inc. All rights reserved.