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README.md

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----
-license: other
-license_name: modified-mit
-license_link: LICENSE
----
+---
+license: other
+license_name: modified-mit
+license_link: LICENSE
+base_model:
+- moonshotai/Kimi-K2-Thinking
+---
+
+# Model Overview
+
+- **Model Architecture:** Kimi-K2-Thinking
+  - **Input:** Text
+  - **Output:** Text
+- **Supported Hardware Microarchitecture:** AMD MI350/MI355
+- **ROCm:** 7.0
+- **Transformers:** 4.57.6
+- **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) (V0.11.2)
+  - **Quantized layers:** `experts`, `shared_experts`, `self_attn`
+  - **Weight quantization:** MoE OCP MXFP4, Static; self_attn Perchannel, FP8E4M3, Static
+  - **Activation quantization:** MoE OCP MXFP4, Dynamic; self_attn Pertoken, FP8E4M3, Dynamic
+- **Calibration Dataset:** [Pile](https://huggingface.co/datasets/mit-han-lab/pile-val-backup)
+
+This model was built with Kimi-K2-Thinking model by applying [AMD-Quark](https://quark.docs.amd.com/latest/index.html) for MXFP4 quantization.
+
+# Model Quantization
+
+The model was quantized from [unsloth/Kimi-K2-Thinking-BF16](https://huggingface.co/unsloth/Kimi-K2-Thinking-BF16) using [AMD-Quark](https://quark.docs.amd.com/latest/index.html). Both weights and activations were quantized.
+
+**Quantization scripts:**
+```
+cd Quark/examples/torch/language_modeling/llm_ptq/
+exclude_layers="*mlp.gate *lm_head *mlp.gate_proj *mlp.up_proj *mlp.down_proj"
+
+python quantize_quark.py \
+    --model_dir unsloth/Kimi-K2-Thinking-BF16 \
+    --quant_scheme mxfp4 \
+    --layer_quant_scheme '*self_attn*' ptpc_fp8 \
+    --exclude_layers  $exclude_layers \
+    --output_dir  amd/Kimi-K2-Thinking-MXFP4-AttnFP8 \
+    --file2file_quantization
+```
+
+# 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>Kimi-K2-Thinking </strong>
+   </td>
+   <td><strong>Kimi-K2-Thinking-MXFP4-AttnFP8(this model)</strong>
+   </td>
+   <td><strong>Recovery</strong>
+   </td>
+  </tr>
+  <tr>
+   <td>GSM8K (flexible-extract)
+   </td>
+   <td>94.16
+   </td>
+   <td>92.95
+   </td>
+   <td>98.71%
+   </td>
+  </tr>
+</table>
+
+### 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.
+
+#### Launching server
+```
+export VLLM_ATTENTION_BACKEND="TRITON_MLA"
+export VLLM_ROCM_USE_AITER=1
+export VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS=0
+
+vllm serve amd/Kimi-K2-Thinking-MXFP4-AttnFP8 \
+  --tensor-parallel-size 8 \
+  --enable-auto-tool-choice \
+  --tool-call-parser kimi_k2 \
+  --reasoning-parser kimi_k2 \
+  --trust-remote-code
+```
+
+#### Evaluating model in a new terminal
+```
+lm_eval \
+  --model local-completions \
+  --model_args "model=amd/Kimi-K2-Thinking-MXFP4-AttnFP8,base_url=http://0.0.0.0:8000/v1/completions,tokenized_requests=False,tokenizer_backend=None,num_concurrent=32" \
+  --tasks gsm8k \
+  --num_fewshot 5 \
+  --batch_size 1
+```
+
+# License
+Modifications Copyright(c) 2025 Advanced Micro Devices, Inc. All rights reserved.