Model Overview Model Architecture: Kimi K2.5 Input: Text, Image, Video Output: Text Supported Hardware Microarchitecture: AMD MI350/MI355 ROCm: 7.1.0 Operating System(s): Linux Inference Engine: vLLM Model Optimizer: AMD Quark (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 This model was built with Kimi K2.5 model by applying AMD Quark for MXFP4 quantization. Model Quantization The model was quantized from moonshotai/Kimi K2.5 using AMD Quark. The weights and activations are quantized to MXFP4. Quantization scripts: 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 MXFP4(this model) Recovery GSM8K (flexible extract) 94.09 93.1 98.95% 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. Launching server Evaluating model in a new terminal License Modifications Copyright(c) 20…
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