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layers.0.mlp, experts and shared_expertscd 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| Benchmark | Kimi-K2.5 | Kimi-K2.5-MXFP4(this model) | Recovery |
| GSM8K (flexible-extract) | 94.09 | 93.1 | 98.95% |
lm-evaluation-harness framework, based on the Docker image vllm/vllm-openai-rocm:v0.17.0.(Version: 0.4.11) in container first.pip install lm-eval
pip install lm-eval[api]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-codelm_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