README.md

---
license: other
license_name: modified-mit
license_link: LICENSE
base_model:
- moonshotai/Kimi-K2-Instruct-0905
---

# Model Overview

- **Model Architecture:** Kimi-K2-Instruct
  - **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)
  - **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-Instruct 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-Instruct-0905-BF16](https://huggingface.co/unsloth/Kimi-K2-Instruct-0905-BF16) using [AMD-Quark](https://quark.docs.amd.com/latest/index.html). The weights and activations are quantized to MXFP4.

# 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-Instruct-0905 </strong>
   </td>
   <td><strong>Kimi-K2-Instruct-0905-MXFP4(this model)</strong>
   </td>
   <td><strong>Recovery</strong>
   </td>
  </tr>
  <tr>
   <td>GSM8K (strict-match)
   </td>
   <td>95.53
   </td>
   <td>94.47
   </td>
   <td>98.89%
   </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 and lm-eval 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-Instruct-0905-MXFP4 \
  --port 8000 \
  --served-model-name kimi-k2-mxfp4 \
  --trust-remote-code \
  --tensor-parallel-size 8 \
  --enable-auto-tool-choice \
  --tool-call-parser kimi_k2
```

#### Evaluating model in a new terminal
```
lm_eval \
  --model local-completions \
  --model_args "model=kimi-k2-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.