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abenliao/minimax-h3-conditioner-fp8
minimax-h3-conditioner-fp8 is a machine learning model from abenliao. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for minimax-h3.
The Qwen3-VL conditioner MiniMax-H3 reads hiddenstates[50] from, narrowed to FP8 e4m3 with one float32 scale per 128 elements of a row.
Downloads · 30 days
19
35% of all-time downloads
All-time downloads
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33.6B
36.5 GB on disk
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Click a slice to open those files.
.safetensors36.5 GB · 100%
How the weights are stored.
F8_E4M331.2B · 93%
From the Hugging Face model README
The Qwen3-VL conditioner MiniMax-H3 reads hidden_states[50] from, narrowed to
FP8 e4m3 with one float32 scale per 128 elements of a row.
Which weights. The stock MiniMaxAI/MiniMax-H3 text_encoder/ with the 9
tensors of abenliao/minimax-h3-conditioner-patch spliced in --
layers.31..39.self_attn.o_proj.weight, the only tensors the fine-tune changed.
Applied byte-for-byte and read back to verify before quantising.
gate_proj and up_proj are already joined into mlp.gate_up_proj.weight.
That is not a convenience: the loader keeps streamed weights in pinned
write-combined host memory, which cannot be read back cheaply to join anything,
so the join has to happen here or not at all.
66.7 GB -> 36.5 GB, of which 31.2 GB is e4m3 and the rest is what stays bf16.
Load with H3_TEXT_ENCODER=<this dir>. Add H3_STREAM_WEIGHTS=1 and it is
2.3 GB resident instead of 25.9.