Kimi-K2.5-W4A8 / README.md

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metadata

license: other
license_name: modified-mit
library_name: transformers
pipeline_tag: image-text-to-text
base_model:
  - moonshotai/Kimi-K2.5

Model Overview

Model Architecture: Kimi-K2.5
- Input: Text
- Output: Text
Supported Hardware Microarchitecture: AMD MI300/MI325/MI350/MI355
ROCm: 7.1.0
Operating System(s): Linux
Inference Engine: vLLM
Model Optimizer: AMD-Quark
- Weight quantization: MOE-only, INT4 Per-Channel & FP8E4M3, Static
- Activation quantization: MOE-only, FP8E4M3, Dynamic

This model was built with Kimi-K2.5 model by applying AMD-Quark for INT4-FP8 quantization.

Model Quantization

The model was quantized from moonshotai/Kimi-K2.5 using AMD-Quark. The weights and activations are quantized to INT4-FP8.

Deployment

Use with vLLM

This model can be deployed efficiently using the vLLM backend.

Evaluation

The model was evaluated on GSM8K benchmarks.

Accuracy

Benchmark	Kimi-K2.5	Kimi-K2.5-W4A8(this model)	Recovery
GSM8K (flexible-extract)	94.09	93.40	99.27%

Reproduction

The GSM8K results were obtained using the lm-evaluation-harness framework, based on the Docker image vllm/vllm-openai-rocm:v0.14.0.

Install the vLLM (commit ecb4f822091a64b5084b3a4aff326906487a363f) and lm-eval (Version: 0.4.10) in container first.

git clone https://github.com/vllm-project/vllm.git
cd vllm
python3 setup.py develop

pip install lm-eval

Launching server

VLLM_ROCM_USE_AITER_MLA=0 VLLM_ROCM_USE_AITER=1 VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS=0 VLLM_ROCM_USE_AITER_FP4BMM=0 vllm serve amd/Kimi-K2.5-W4A8 \
  --tensor-parallel-size 8 \
  --mm-encoder-tp-mode data \
  --tool-call-parser kimi_k2 \
  --reasoning-parser kimi_k2 \
  --trust-remote-code \
  --enforce-eager

Evaluating model in a new terminal

lm_eval \
  --model local-completions \
  --model_args "model=amd/Kimi-K2.5-W4A8,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

amd
/

Kimi-K2.5-W4A8