README.md

---
license: other
license_name: modified-mit
license_link: LICENSE
base_model:
- moonshotai/Kimi-K2.5
---

# Model Overview

- **Model Architecture:** Kimi-K2.5
  - **Input:** Text
  - **Output:** Text
- **Supported Hardware Microarchitecture:** AMD MI350/MI355
- **ROCm:** 7.1.0
- **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)
  - **Quantized layers:** `layers.0.mlp`, `experts` and `shared_experts`
  - **Weight quantization:** OCP MXFP4, Static 
  - **Activation quantization:** OCP MXFP4, Dynamic
- **Calibration Dataset:** [Pile](https://huggingface.co/datasets/mit-han-lab/pile-val-backup)

This model was built with Kimi-K2.5 model by applying [AMD-Quark](https://quark.docs.amd.com/latest/index.html) for MXFP4 quantization.

# Model Quantization

The model was quantized from [moonshotai/Kimi-K2.5](https://huggingface.co/moonshotai/Kimi-K2.5) 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 *mm_projector* *vision_tower*"

python quantize_quark.py \
    --model_dir moonshotai/Kimi-K2.5 \
    --quant_scheme mxfp4 \
    --exclude_layers  $exclude_layers \
    --output_dir amd/Kimi-K2.5-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-K2.5 </strong>
   </td>
   <td><strong>Kimi-K2.5-MXFP4(this model) </strong>
   </td>
   <td><strong>Recovery</strong>
   </td>
  </tr>
  <tr>
   <td>GSM8K (flexible-extract)
   </td>
   <td>94.09
   </td>
   <td>93.1
   </td>
   <td>98.95%
   </td>
  </tr>
</table>

### Reproduction

The GSM8K results were obtained using the `lm-evaluation-harness` framework, based on the Docker image `vllm/vllm-openai-rocm:v0.17.0`.

Install the lm-eval `(Version: 0.4.11)` in container first.
```
pip install lm-eval
pip install lm-eval[api]
```

#### Launching server
```
export VLLM_ROCM_USE_AITER=1

vllm serve amd/Kimi-K2.5-MXFP4 -tp 4 \
  --mm-encoder-tp-mode data \
  --tool-call-parser kimi_k2 \
  --reasoning-parser kimi_k2 \
  --enforce-eager \
  --trust-remote-code
```

#### Evaluating model in a new terminal
```
lm_eval \
  --model local-completions \
  --model_args "model=amd/Kimi-K2.5-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.