Each variant is a single precast safetensors file (model.safetensors) + config.json. The runtime detects the precision from the file's metadata header — no flag needed.

Download only what you need (~6.3 GB, not ~18 GB). from_pretrained(...) fetches only the chosen variant + tokenizer. If you download manually, use --include (below) — a bare git clone / full snapshot_download pulls both variants (~18 GB total).

Usage

python

from mlx_umt5 import from_pretrained, encode

loaded = from_pretrained("shraey/umt5-xxl-mlx", variant="int8")
embeds, masks = encode(loaded, ["A cinematic shot of a mountain at sunrise."])
# embeds[0]: (1, 512, 4096) fp32
# masks[0]:  (1, 512)       int32

bash

# Download only the variant you need:
hf download shraey/umt5-xxl-mlx \
    --include "int8/*" tokenizer.json tokenizer_config.json \
    special_tokens_map.json spiece.model \
    --local-dir ./umt5-xxl-mlx

Parity

int8/ precast is byte-identical to the in-process per-channel-symmetric int8 path (max-abs-diff 0).
bf16/ precast is bit-identical to loading the HF fp32 shards and casting to bf16 (cosine 1.0).
int8 vs bf16 quality cosine: short 0.999631, 512-token 0.999059, unicode 0.998631 — worst 0.99863, above the 0.995 gate.
MLX-bf16 vs torch-fp32 oracle: cosine 0.999924 / 0.999575 / 0.999900 across three prompt lengths.

Quantization note

Per-channel-symmetric int8 is the only safe scheme for UMT5-XXL. MLX group-affine quantization corrupts this model (cosine ~0.108). UMT5 has no attention output scaling and a monotonically growing residual across 24 layers — group-affine error compounds with depth. The int8/ variant uses pcs only and is byte-identical to the in-process conversion.

The precast advantage

Standard HF shard loading casts ~21 GB of fp32 tensors at startup, creating a ~22 GB transient before any inference. These precast files eliminate that transient entirely — the encoder loads directly into the target dtype via lazy mmap.

Attribution

Converted from google/umt5-xxl (Apache-2.0) and hosted as a convenience. Full credit to Google for the UMT5 model, architecture, and weights. This conversion does not change the license.