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View details ↗- **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. The model was quantized from moonshotai/Kimi-K2.5 using AMD-Quark. The weights and activations are quantized to MXFP4.
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View details ↗The model was quantized from a BF16-decompressed version of moonshotai/Kimi-K2.6 using AMD-Quark. The original checkpoint uses native INT4 (compressed-tensors) quantization; it was first decompressed to BF16 before applying MXFP4 quantization. The weights and activations are quantized to MXFP4. The GSM8K results were obtained using the vLLM framework, based on the Docker image `rocm/vllm-dev:nightly_main_20260417`, with lm-eval and amd-quark compiled and installed from source, and vLLM (version `0.19.1rc1.dev369+gb1dc87a09`) pre-installed in the docker image.
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View details ↗Quark has its own export format and allows FP8 quantized models to be efficiently deployed using the vLLM backend(vLLM-compatible). Quark currently uses perplexity(PPL) as the evaluation metric for accuracy loss before and after quantization.The specific PPL algorithm can be referenced in the quantize_quark.py. The quantization evaluation results are conducted in pseudo-quantization mode, which may slightly differ from the actual quantized inference accuracy. These results are provided for reference only.
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View details ↗The model was quantized from MiniMaxAI/MiniMax-M2.5 which was converted to bf16 using QuixiAI/MiniMax-M2.1-bf16/minimax_to_bf16.py using AMD-Quark. The weights are quantized to MXFP4 and activations are quantized to MXFP4. For further details or issues, please refer to the AMD-Quark documentation or contact the respective developers. The GSM8K results were obtained using the lm-eval framework, based on the Docker image `rocm/vllm-dev:nightly`.
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View details ↗- **Model Architecture:** MiniMaxM3SparseForConditionalGeneration - **Input:** Text, Image - **Output:** Text - **Supported Hardware Microarchitecture:** AMD MI350/MI355 - **ROCm**: 7.1.1 - **PyTorch**: 2.10.0 - **Transformers**: 5.2.0 - **Operating System(s):** Linux - **Inference Engine:** vLLM - **Model Optimizer:** AMD-Quark - **Weight quantization:** OCP MXFP4, Static - **Activation quantization:** OCP MXFP4, Dynamic The model was quantized from MiniMaxAI/MiniMax-M3 using AMD-Quark. The weights are quantized to MXFP4 and activations are quantized to MXFP4. The GSM8K results were obtained using the lm-eval framework, based on the Docker image `rocm/pytorch-private:vllm-hy-mm-06112026`. The vLLM shipped in that image was used as-is, with the patch from this PR (#45794) applied on top.
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View details ↗The model was quantized from MiniMaxAI/MiniMax-M3 using AMD-Quark. The weights are quantized to MXFP4 and activations are quantized to MXFP4, and self_attn layers are quantized to PTPC-FP8. The GSM8K results were obtained using the lm-eval framework, based on the Docker image `rocm/pytorch-private:vllm-hy-mm-06112026`. The vLLM shipped in that image was used as-is, with the patch from this PR (#45794) applied on top.
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