Improve model card: Add metadata and paper link

This PR enhances the model card by:
- Adding `pipeline_tag: feature-extraction` to improve discoverability on the Hub.
- Specifying `library_name: transformers` for better integration with the `transformers` library.
- Including the `license: apache-2.0`.
- Adding relevant `tags` such as `tokenizer`, `embeddings`, `LLM`, `MoE`, and `unicode` for improved searchability.
- Linking directly to the Hugging Face paper page ([Growing Transformers: Modular Composition and Layer-wise Expansion on a Frozen Substrate](https://huggingface.co/papers/2507.07129)) and providing a brief summary of the paper's focus for quick context.

These changes will provide clearer and more comprehensive information to users.

README.md

@@ -1,11 +1,20 @@
 ---
-# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
-# Doc / guide: https://huggingface.co/docs/hub/model-cards
-{}
+license: apache-2.0
+library_name: transformers
+pipeline_tag: feature-extraction
+tags:
+  - tokenizer
+  - embeddings
+  - LLM
+  - MoE
+  - unicode
 ---
 
 # bvv241-abs: Unified Unicode Tokenizer (SOTA Intersection) with Frozen Embeddings and Extended Vector Dim (4096)
 
+This model is a core component described in the paper [**Growing Transformers: Modular Composition and Layer-wise Expansion on a Frozen Substrate**](https://huggingface.co/papers/2507.07129).
+
+This work explores a novel constructive approach to model development, built upon the foundation of non-trainable, deterministic input embeddings. It demonstrates that this fixed representational substrate acts as a universal "docking port," enabling seamless modular composition and progressive layer-wise growth of Transformer models.
 
 ## Tokenizer Description
 
@@ -75,4 +84,4 @@ If you use this model or the underlying concepts in your research, please cite o
 }
 ```
 
-This work demonstrates that transformer blocks, not token embeddings, carry the semantic burden in LLMs — a step toward modular, fusable, multilingual LMs.
+This work demonstrates that transformer blocks, not token embeddings, carry the semantic burden in LLMs — a step toward modular, fusable, multilingual LMs.