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edp1096/Huihui-Qwen3.8-Flash-Next-abliterated-NVFP4-QAD

AI model by edp1096

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Base: Conversion: DQ(LIL QAD) + DQ(Huihui Q8) - DQ(Unsloth Q8), followed by BF16 rounding and ModelOpt requantization. Original LIL activation scales are retained without new activation calibration. GGUF residuals and requantization error remain; equivalence to Huihui BF16 and preservation of LIL benchmark scores are not claimed. See transfer-manifest.json. Validated on DGX Spark TP1 with my SGLang: See runtime-qualification.json. Recipe example: 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…

edp1096/Huihui-Qwen3.8-Flash-Next-abliterated-NVFP4-QAD on Hugging Face A short preview, not the full document.

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Inside the original model card — Document outline
  1. Huihui-Qwen3.8-Flash-Next-abliterated-NVFP4-QAD
  2. Model Description
  3. What's quantized
  4. Quantization-aware distillation
  5. Training data
  6. Activation calibration
  7. Requirements
  8. Evaluation

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edp1096/Huihui-Qwen3.8-Flash-Next-abliterated-NVFP4-QAD is a task-unspecified model repository published by edp1096 on Hugging Face. A library was not reported.

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Created
2026-09-21T06:33:06.000Z
Last modified
2026-09-21T06:33:06.000Z

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