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README.md

@@ -196,3 +196,9 @@ fn main() -> ort::Result<()> {
 - **ONNX Export:** Exported with `torch.onnx.export(dynamo=True)` + `onnxscript`. The old JIT tracer (`dynamo=False`) is incompatible with the new masking utilities in `transformers >= 5.x`.
 - **INT8 Quantization:** Symmetric per-tensor quantization applied directly to the ONNX graph initializers (numpy-based). PyTorch `quantize_dynamic` models are not exportable via the dynamo exporter (`LinearPackedParamsBase` is not serializable by `torch.export`).
 - **ONNX Architecture:** To overcome issues with ByT5 relative positional embeddings dynamically broadcasting at runtime, the model is exported as a **separated Encoder and Decoder**. The Decoder expects a fixed-length sequence of 16, which is updated sequentially using a padding mask during the autoregressive loop (see Python and Rust examples above).
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+## Author & Contact
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+- **Author:** Dario Finardi
+- **Company:** [Semplifica](https://semplifica.ai)
+- **Email:** hf@semplifica.ai