add missing files for legal scan

README.md

@@ -1,116 +1,9 @@
 ---
 license: mit
-base_model:
-- zai-org/GLM-5.1
 ---
 
-# Model Overview
+**Disclaimer**
 
-- **Model Architecture:** GLM-5.1
-  - **Input:** Text
-  - **Output:** Text
-- **Supported Hardware Microarchitecture:** AMD MI300/MI350/MI355 (emulation)
-- **ROCm:** 7.2.2
-- **PyTorch**: 2.10.0
-- **Transformers**: 5.2.0
-- **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) (V0.12)
-  - **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[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.
+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.

tokenizer.json

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