Qwen3.8-Flash-Next-NVFP4

모델 소개

local-inference-lab/Qwen3.8-Flash-Next-NVFP4 is a mixed-precision model distilled from Qwen/Qwen3.8-Flash-Next using quantization-aware distillation (QAD). The student is trained against the original BF16 teacher with quantized weights in its forward pass, learning to compensate for quantization error rather than relying on post-training quantization alone. The architecture is unchanged: 48 decoder layers, 512 routed experts per layer with 10 active per token, hybrid Gated DeltaNet/Qwen Sparse Attention (QSA), and n-gram embedding tables. Compression comes from lower-precision weights, not fewer layers or experts. The checkpoint occupies approximately 98 GiB on disk and is particularly suited to run on a single RTX 6000 (with PLE offload), or a DGX Spark (with or without PLE offload). NVFP4 stores 4-bit E2M1 values with FP8 block scales per 16 elements and an FP32 global scale. MXFP8 stores 8-bit E4M3 values with power-of-two block scales per 32 elements. Residual-mixing and PLE normalization weights are trained in FP32 and exported in BF16. Shared-expert scalar gates remain frozen in BF16.

주요 특징

特点摘要待依据来源补齐，不凭模型名称推断能力。

한계 및 유의 사항

구체적 한계를 판단할 출처가 부족합니다. 실제 성능, 지연, 데이터 정책, 라이선스와 버전 수명을 확인하세요.

출처별 요금 및 비용 분석

요금은 채널, 지역, 설정에 따라 다릅니다. 각 채널 요금은 개별 표시되며 제공사의 통일 공식 요금이 아닙니다.

공개 평가 및 실행 설정

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

자주 묻는 질문

출처 및 추적 기록

许可证：other。模型介绍与第三方评测均以原来源的具体版本和方法为准。