add missing files for legal scan

README.md

@@ -1,11 +1,108 @@
 ---
+base_model:
+- MiniMaxAI/MiniMax-M2.7
+language:
+- en
+library_name: transformers
 license: other
-license_name: other
-license_link: LICENSE
+license_name: modified-mit
+license_link: https://huggingface.co/MiniMaxAI/MiniMax-M2.7/blob/main/LICENSE
 ---
+# Model Overview
 
-**Disclaimer**
+- **Model Architecture:** MiniMaxM2ForCausalLM
+  - **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)
+- **Inference Engine:** [SGLang](https://docs.sglang.ai/)/[vLLM](https://docs.vllm.ai/en/latest/)
+- **Model Optimizer:** [AMD-Quark](https://quark.docs.amd.com/latest/index.html)
+  - **Weight quantization:** NVFP4, Static 
+  - **Activation quantization:** NVFP4, Dynamic
 
-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 Quantization
+
+The model was quantized from [amd/MiniMax-M2.7-BF16](https://huggingface.co/amd/MiniMax-M2.7-BF16), originally from [MiniMax/MiniMax-M2.7](https://huggingface.co/MiniMax/MiniMax-M2.7), using [AMD-Quark](https://quark.docs.amd.com/latest/index.html). The weights are quantized to NVFP4 and activations are quantized to NVFP4.
+
+
+**Quantization scripts:**
+```
+cd /Quark/examples/torch/language_modeling/llm_ptq
+exclude_layers="lm_head *block_sparse_moe.gate* *self_attn*"
+python3 quantize_quark.py --model_dir amd/MiniMax-M2.7-BF16 \
+                          --quant_scheme nvfp4 \
+                          --exclude_layers $exclude_layers \
+                          --num_calib_data 128 \
+                          --model_export hf_format \
+                          --multi_gpu balanced \
+                          --trust_remote_code \
+                          --output_dir amd/MiniMax-M2.7-NVFP4 
+```
+
+
+For further details or issues, please refer to the AMD-Quark documentation or contact the respective developers.
+
+# Evaluation
+The model was evaluated on gsm8k benchmarks using the vllm framework.
+
+### Accuracy
+
+<table>
+  <tr>
+   <td><strong>Benchmark</strong>
+   </td>
+   <td><strong>MiniMaxAI/MiniMax-M2.7 </strong>
+   </td>
+   <td><strong>amd/MiniMax-M2.7-NVFP4(this model)</strong>
+   </td>
+   <td><strong>Recovery</strong>
+   </td>
+  </tr>
+  <tr>
+   <td>gsm8k (flexible-extract) 
+   </td>
+   <td>91.81
+   </td>
+   <td>92.20
+   </td>
+   <td>100.04%
+   </td>
+  </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
+```
+vllm serve \
+    --model amd/MiniMax-M2.7-NVFP4 \
+    --trust-remote-code \
+    --host 0.0.0.0 \
+    --port 8011 \
+    --tensor-parallel-size 4 \
+    --enable-auto-tool-choice \
+    --tool-call-parser minimax_m2 \
+    --reasoning-parser minimax_m2_append_think
+```
+
+
+#### Evaluating model in a new terminal
+```
+python3 vllm/tests/evals/gsm8k/gsm8k_eval.py --host http://0.0.0.0 --port 8011
+```
+
+
+# License
+Modifications Copyright(c) 2026 Advanced Micro Devices, Inc. All rights reserved.

tokenizer.json

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+        "<filepath>",
+        "<file_sep>"
+    ],
+  "add_prefix_space": false,
   "bos_token": "]~!b[",
   "clean_up_tokenization_spaces": false,
   "eos_token": "[e~[",
-  "extra_special_tokens": {},
   "model_max_length": 40960000,
-  "pad_token": "[e~[",
-  "padding_side": "left",
   "tokenizer_class": "GPT2Tokenizer",
   "unk_token": "]!d~["
 }