AI가 움직이고 미래가 업데이트됩니다.DeepSeek-V4-Flash-Vision-Exp-Abliterated
> [!WARNING] > Experimental research release. Text and focused image smoke tests pass, but > broader quality, safety, and production-runtime evaluation remains pending. This catalog contains the native abliterated checkpoint and two DS4 GGUF profiles. Both GGUF receivers use tensor types accepted by `antirez/ds4` main; neither requires the legacy Python vision sidecar for current native vision. `Basic128-Routed-IQ2_M` uses the closest familiar BPW class for the complete bundle: 2.915 BPW overall, close to llama.cpp's documented 2.9294 BPW for IQ2_M. The routed-expert subset averages 2.715 BPW, close to IQ2_S. This is a custom mixed recipe rather than llama.cpp's exact IQ2_M preset: routed gate/up tensors use IQ2_XXS, routed down tensors use Q2_K, ten routed layers retain MXFP4, and non-routed tensors include F32, F16, and Q8_0. See the llama.cpp quantization table for the reference BPW classes.
번역 준비 중입니다. 원문을 표시합니다.
> [!WARNING] > Experimental research release. Text and focused image smoke tests pass, but > broader quality, safety, and production-runtime evaluation remains pending. This catalog contains the native abliterated checkpoint and two DS4 GGUF profiles. Both GGUF receivers use tensor types accepted by `antirez/ds4` main; neither requires the legacy Python vision sidecar for current native vision. `Basic128-Routed-IQ2_M` uses the closest familiar BPW class for the complete bundle: 2.915 BPW overall, close to llama.cpp's documented 2.9294 BPW for IQ2_M. The routed-expert subset averages 2.715 BPW, close to IQ2_S. This is a custom mixed recipe rather than llama.cpp's exact IQ2_M preset: routed gate/up tensors use IQ2_XXS, routed down tensors use Q2_K, ten routed layers retain MXFP4, and non-routed tensors include F32, F16, and Q8_0. See the llama.cpp quantization table for the reference BPW classes.