README.md

---
license: other
license_name: modified-mit
license_link: LICENSE
base_model:
- moonshotai/Kimi-K2.6
---

# Model Overview

- **Model Architecture:** Kimi-K2.6
  - **Input:** Text, Image, Video
  - **Output:** Text
- **Supported Hardware Microarchitecture:** AMD MI300/MI350/MI355 (emulation)
- **ROCm:** 7.2.2
- **PyTorch**: 2.10.0
- **Transformers**: 5.2.0
- **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) (V0.12)
  - **Quantized layers:** `experts` and `shared_experts`
  - **Weight quantization:** NVFP4, Static 
  - **Activation quantization:** NVFP4, Dynamic 
- **Calibration Dataset:** [Pile](https://huggingface.co/datasets/mit-han-lab/pile-val-backup)

This model was built with Kimi-K2.6 model by applying [AMD-Quark](https://quark.docs.amd.com/latest/index.html) for NVFP4 quantization.

# Model Quantization

The model was quantized from [moonshotai/Kimi-K2.6](https://huggingface.co/moonshotai/Kimi-K2.6) using [AMD-Quark](https://quark.docs.amd.com/latest/index.html). The weights and activations are quantized to NVFP4.

**Quantization scripts:**
```
cd Quark/examples/torch/language_modeling/llm_ptq/
export output_dir=amd/Kimi-K2.6-NVFP4
exclude_layers="*self_attn* *mlp.gate *mlp.gate.linear *lm_head *mlp.gate_proj *mlp.up_proj *mlp.down_proj *mm_projector* *vision_tower*"
python3 quantize_quark.py --model_dir $MODEL_DIR \
                          --quant_scheme nvfp4 \
                          --num_calib_data 128 \
                          --exclude_layers $exclude_layers \
                          --model_export hf_format \
                          --output_dir $output_dir \
                          --trust_remote_code \
                          --multi_gpu balanced 
```

# 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 and MMLU_PRO benchmarks. 

### Accuracy

<table>
  <tr>
   <td><strong>Benchmark</strong>
   </td>
   <td><strong>Kimi-K2.6 </strong>
   </td>
   <td><strong>Kimi-K2.6-NVFP4(this model) </strong>
   </td>
   <td><strong>Recovery</strong>
   </td>
  </tr>
  <tr>
   <td>GSM8K (flexible-extract)
   </td>
   <td>93.93
   </td>
   <td>93.48
   </td>
   <td>99.52%
   </td>
  </tr>
    <tr>
   <td>MMLU_PRO (exact-extract)
   </td>
   <td>81.43
   </td>
   <td>79.21
   </td>
   <td>97.27%
   </td>
  </tr>
</table>

### Reproduction

The GSM8K and MMLU_PRO results were 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[api]
```

#### Launching server
```
export VLLM_ROCM_USE_AITER=1
vllm serve amd/Kimi-K2.6-NVFP4 -tp 8 \
  --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-K2.6-NVFP4,kv_cache_dtype=fp8,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 
```
```
lm_eval \
  --model local-completions \
  --model_args "model=amd/Kimi-K2.6-NVFP4,kv_cache_dtype=fp8,base_url=http://0.0.0.0:8000/v1/completions,tokenized_requests=False,tokenizer_backend=None,num_concurrent=32,max_length=16384,timeout=14400" \
  --tasks mmlu_pro \
  --gen_kwargs "do_sample=True,temperature=1.0,top_p=0.95,max_tokens=4096,max_gen_toks=4096" \
  --batch_size auto \
  --limit 100
```

# License
Modifications Copyright(c) 2026 Advanced Micro Devices, Inc. All rights reserved.