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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
- **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)
- **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 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). The weights and activations are quantized to MXFP4.
**Quantization scripts:**
```
cd Quark/examples/torch/language_modeling/llm_ptq/
exclude_layers="*self_attn* *mlp.gate *lm_head *mlp.gate_proj *mlp.up_proj *mlp.down_proj *shared_experts*"
python quantize_quark.py \
--model_dir unsloth/Kimi-K2-Thinking-BF16 \
--quant_scheme mxfp4 \
--exclude_layers $exclude_layers \
--output_dir amd/Kimi-K2-Thinking-MXFP4 \
--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(this model)</strong>
</td>
<td><strong>Recovery</strong>
</td>
</tr>
<tr>
<td>GSM8K (strict-match)
</td>
<td>94.16
</td>
<td>93.48
</td>
<td>99.28%
</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 \
--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,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. |