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- **Model Architecture:** DeepSeek-R1 - **Input:** Text - **Output:** Text - **Supported Hardware Microarchitecture:** AMD MI350/MI355 - **ROCm**: 7.0 - **PyTorch**: 2.8.0 - **Transformers**: 4.53.0 - **Operating System(s):** Linux - **Inference Engine:** SGLang - **Model Optimizer:** AMD-Quark (V0.10) - **Weight quantization:** OCP MXFP4, Static - **Activation quantization:** OCP MXFP4, Dynamic - **KV cache**: OCP FP8, Static - **Calibration Dataset:** Pile This model was built with deepseek-ai DeepSeek-R1 model by applying AMD-Quark for MXFP4 quantization. The model was quantized from deepseek-ai/DeepSeek-R1 using AMD-Quark. Both weights and activations were quantized to MXFP4 format.
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View details ↗- **Model Architecture:** GLM-5.2 - **Input:** Text - **Output:** Text - **Supported Hardware Microarchitecture:** AMD MI350/MI355 - **ROCm:** 7.0.0 - **PyTorch:** 2.9.0 - **Transformers:** 5.8.1 - **Operating System(s):** Linux - **Inference Engine:** SGLang/vLLM - **Model Optimizer:** AMD-Quark (V0.11) - **Weight quantization:** MOE-only (shared experts quantized), OCP MXFP4, Static - **Activation quantization:** MOE-only, OCP MXFP4, Dynamic This model was built with GLM-5.2 model by applying AMD-Quark for MXFP4 quantization. The model was quantized from zai-org/GLM-5.2 using AMD-Quark. The weights and activations are quantized to MXFP4.
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View details ↗- **Model Architecture:** GLM-5.3-Flash - **Input:** Text, Image, Video - **Output:** Text - **Supported Hardware Microarchitecture:** AMD MI350/MI355 - **ROCm:** 7.2.0 - **PyTorch:** 2.9.1+rocm7.2.0.git7e1940d4 - **Transformers:** 5.12.1 - **Operating System(s):** Linux - **Inference Engine:** SGLang - **Model Optimizer:** AMD-Quark (V0.12) - **Weight quantization:** MOE-only (shared experts quantized), OCP MXFP4, Static - **Activation quantization:** MOE-only, OCP MXFP4, Dynamic This model was built with GLM-5.3-Flash model by applying AMD-Quark for MXFP4 quantization. The model was quantized from zai-org/GLM-5.3-Flash using AMD-Quark. The weights and activations are quantized to MXFP4.
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View details ↗The model was quantized from openai/gpt-oss-120b using AMD-Quark. The weights are quantized MXFP4 and activations were quantized to FP8. The results of AIME25 and GPQA Diamond were obtained using gpt_oss.evals with `low` effort setting, and vLLM docker `rocm/vllm-private:rocm7.0_ubuntu_22.04_vllm_0.10.1_instinct_gptoss_wmxfp4_afp8_20251030`.
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