Inference contract
The models recognize one tightly cropped printed formula and return the content
of a Typst math expression. Add $ delimiters in the consuming application.
No LaTeX conversion is needed. This package contains weights and metadata only;
the consuming application supplies preprocessing, generation, and its runtime.
Images
Composite transparency onto white, convert to RGB, and resize the complete image
to 384 by 384 using Pillow-compatible bicubic interpolation. Do not center-crop,
change aspect handling, invert polarity, or use the old Phase10 preprocessing.
Rescale each channel to float32 with (pixel / 255 - 0.5) / 0.5. The encoder
expects float32 pixel_values of shape [1, 3, 384, 384] in channel-first order.
Cached ONNX graphs
Each ONNX variant contains an encoder and decoder; keep the two from the same variant together. These are custom cached exports, not the upstream Pix2Text filenames or a generic Optimum export layout.
The encoder outputs float32 cross_keys and cross_values, each shaped
[6, 1, 8, 578, 32]. Initialize self_keys and self_values as empty float32
tensors shaped [6, 1, 8, 0, 32].
At each decoder step supply int64 token_ids of shape [1, 1], the previous
self_keys and self_values, and the encoder's cross_keys and cross_values.
Outputs are logits, self_keys_out, and self_values_out. Select the first
maximum over the 1,199 vocabulary scores, pass that token into the next step,
and reuse the new self caches. The first input token is BOS=1. EOS=2 ends decoding.
Allow at most 1,023 generated tokens after BOS, including EOS. Do not fabricate
EOS at the limit or return a truncated formula as complete.
For each content token, append the bytes at that index in token-bytes.json,
then decode the full byte sequence as strict UTF-8. Reject generated reserved
IDs 0, 1, 3 or 4 and invalid UTF-8. Preserve the resulting Typst literally;
do not apply the original model's syntax repair or LaTeX conversion.
The compact model quantizes constant MatMul/Gemm weights dynamically with per-channel signed INT8. Input, activations and caches remain floating-point. Both exports were run with the WASM provider in desktop Chromium. Four threads need cross-origin isolation headers. WebGPU and mobile behavior are not claimed.
Safetensors
The Hugging Face pack additionally contains the optimizer-free FP32 parameters
as model.safetensors, with standard DeiT/TrOCR VisionEncoderDecoderModel keys.
The recorded architecture uses a DeiT encoder without its unused pooling
layer, a TrOCR causal decoder, and eager attention. Transformers 4.52.3 was
used for the recorded results. The tokenizer has 1,199 entries and is distinct
from the upstream LaTeX tokenizer. Use the supplied tokenizer metadata.
The packaged generation_config.json sets max_new_tokens to 1,023 to match the reviewed browser budget. This adjusts metadata only; the learned parameters and ONNX graph bytes are unchanged. No new benchmark was run for packaging.