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QuantaPlanta/DeepSeek-V4-Flash-Vision-Exp-MXFP4-GGUF
DeepSeek-V4-Flash-Vision-Exp-MXFP4-GGUF is a machine learning model from QuantaPlanta. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
MXFP4 quant of https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp as it should be, including mmproj and dspark.
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.gguf168 GB · 100%
From the Hugging Face model README
MXFP4 quant of https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp as it should be, including mmproj and dspark.
$PY $LCPP/convert_hf_to_gguf.py "$SRC" \
--outtype bf16 --no-mtp --split-max-size 45G \
--model-name DeepSeek-V4-Flash-Vision-Exp \
--outfile "$OUT/DeepSeek-V4-Flash-Vision-Exp-MXFP4_MOE.gguf"
$PY $LCPP/convert_hf_to_gguf.py "$SRC" \
--mmproj --outtype bf16 \
--outfile "$OUT/mmproj-DeepSeek-V4-Flash-Vision-Exp-BF16.gguf"
$PY $LCPP/convert_hf_to_gguf.py "$SRC" \
--dspark --target-model-dir "$SRC" --outtype bf16 \
--outfile "$OUT/dspark-DeepSeek-V4-Flash-Vision-Exp-MXFP4_MOE.gguf"
ATTENTIONE
Blud Claude has a caveat on the DSpark. Please read if you want to use it.
What the workaround costs: the draft loses the vision routing bias, so on image-heavy prompts its expert selection can
diverge from the target's and acceptance drops there. Text-only drafting is unaffected, and correctness never is — the
target verifies every drafted token regardless. Given DSpark was already the speculative part of this plan, that's a fair
trade to get the file built.