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This checkpoint quantizes the routed **and** shared MoE experts to OCP MXFP4 and keeps everything else in bf16, including the full self-attention and the (Mamba-style) linear-attention projections, the MTP head, router gates, shared-expert gate, PLE n-gram path, vision tower, hyper-connection mixers, and `lm_head`. The model was quantized from Qwen3.8-Flash-Next using AMD-Quark. MoE expert weights and activations are quantized to OCP MXFP4. The GSM8K results were obtained using the vLLM framework, based on the Docker image `vllm/vllm-openai-rocm:v0.32.0`, with lm-eval installed from source. This Reproduction serves the model without MTP. Enabling MTP on the formal `v0.32.0` release currently requires vLLM PR #61770.
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Adjust provider, category or input/output filters. New models are added continuously.
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Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.
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