add model card and update tokenizer file

README.md

@@ -1,9 +1,119 @@
 ---
-license: mit
+license: other
+license_name: modified-mit
+license_link: LICENSE
+base_model:
+- zai-org/GLM-5.1
 ---
 
-**Disclaimer**
+# Model Overview
 
-This model is provided for research and evaluation purposes only.
-Quantization may introduce accuracy or behavioral differences compared to the original model.
-Users are responsible for validating the model in their own environments and complying with the original model license.
+- **Model Architecture:** GLM-5.1
+  - **Input:** Text
+  - **Output:** Text
+- **Supported Hardware Microarchitecture:** AMD MI300/MI350/MI355 (emulation)
+- **ROCm:** 7.2.2
+- **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) (V0.12)
+  - **Quantized layers:** `experts` and `shared_experts`
+  - **Weight quantization:** NVFP4, Static 
+  - **Activation quantization:** NVFP4, Dynamic 
+- **Calibration Dataset:** [Pile](https://huggingface.co/datasets/mit-han-lab/pile-val-backup)
+
+This model was built with GLM-5.1 model by applying [AMD-Quark](https://quark.docs.amd.com/latest/index.html) for NVFP4 quantization.
+# Model Quantization
+
+The model was quantized from [zai-org/GLM-5.1](https://huggingface.co/zai-org/GLM-5.1) using [AMD-Quark](https://quark.docs.amd.com/latest/index.html). The weights and activations are quantized to NVFP4.
+
+**Quantization scripts:**
+```
+sudo sysctl -w vm.max_map_count=4194304
+cd Quark/examples/torch/language_modeling/llm_ptq/
+export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+export MODEL_DIR=/zai-org/GLM-5.1
+export output_dir=/amd/GLM-5.1-NVFP4
+exclude_layers="*self_attn* *mlp.gate lm_head *mlp.gate_proj *mlp.up_proj *mlp.down_proj"
+python3 quantize_quark.py --model_dir $MODEL_DIR \
+                          --quant_scheme nvfp4 \
+                          --num_calib_data 128 \
+                          --exclude_layers $exclude_layers \
+                          --model_export hf_format \
+                          --output_dir $output_dir \
+                          --multi_gpu balanced
+```
+
+# 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-5.1 </strong>
+   </td>
+   <td><strong>GLM-5.1-NVFP4(this model) </strong>
+   </td>
+   <td><strong>Recovery</strong>
+   </td>
+  </tr>
+  <tr>
+   <td>GSM8K (flexible-extract)
+   </td>
+   <td>95.38
+   </td>
+   <td>95.68
+   </td>
+   <td>100.31%
+   </td>
+  </tr>
+
+  </tr>
+</table>
+
+### Reproduction
+
+The GSM8K result was obtained using the `lm-evaluation-harness` framework, based on the Docker image `rocm/vllm-dev:nightly_main_20260603`.
+
+Install the lm-eval `(Version: 0.4.12)` in container first.
+```
+pip install lm-eval
+pip install lm-eval[api]
+```
+
+#### Launching server
+```
+export VLLM_ROCM_USE_AITER=1
+export VLLM_ROCM_USE_AITER_FP8BMM=0
+export VLLM_ROCM_USE_AITER_FP4BMM=0
+HIP_VISIBLE_DEVICES=4,5,6,7 vllm serve /amd/GLM-5.1-NVFP4 \
+  -tp 4 \
+  --block-size 1 \
+  --trust-remote-code \
+  --max-model-len 4096 \
+  --port 8082
+```
+
+#### Evaluating model in a new terminal
+```
+lm_eval \
+  --model local-completions \
+  --model_args '{"model": "/amd/GLM-5.1-NVFP4", "base_url": "http://localhost:8082/v1/completions", "num_concurrent": 32, "max_retries": 10, "max_gen_toks": 2048, "tokenizer_backend": null, "tokenized_requests": false}' \
+  --tasks gsm8k \
+  --batch_size auto \
+  --num_fewshot 5 \
+  --trust_remote_code
+```
+```
+
+
+# License
+Modifications Copyright(c) 2026 Advanced Micro Devices, Inc. All rights reserved.
+ 

tokenizer.json

@@ -1,3 +1,3 @@
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-size 20217707
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+size 20217442