Upload README.md with huggingface_hub

README.md

@@ -98,7 +98,8 @@ from huggingface_hub import hf_hub_download
 weights = Path(hf_hub_download("precisit/one-pass-sv-forms", "sv0-forms.safetensors"))
 hf_hub_download("precisit/one-pass-sv-forms", "sv0-forms.json", local_dir=weights.parent)
 # then either use the Core ML packages (no Python runtime needed) or the PyTorch code in
-# precisit/onepass — both are in this repository.
+# the toolkit repository: https://github.com/precisit/one-pass-specialists — the Core ML
+# packages below live in *this* repository and need no PyTorch.
 ```
 
 The context string and options must be built exactly as in training (byte ids = UTF-8 byte + 1,
@@ -106,6 +107,10 @@ zero-padded; `UPPGIFT fyll i formuläret från dokumentet och skicka sedan in` /
 `ELEMENT <role> "<label>" value="…"`). A mismatch there is the most likely cause of poor output —
 the recipe and the generator are in the toolkit repository.
 
+## Building your own
+
+The toolkit that produced this checkpoint is public: [`precisit/one-pass-specialists`](https://github.com/precisit/one-pass-specialists). The interesting work is the **catalogue** — the concepts, the labels they appear under, and their value formats — because everything above the trainer is language- and vertical-neutral. The recipe, the corpus manifest and both result files for this checkpoint are in that repository under `examples/sv-forms/`.
+
 ## Licence and attribution
 
 MIT. The architecture, training loop and evaluation metrics come from Cua's MIT-licensed