Update README.md

README.md

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 # Model Card for abs-bvv-4
 
-**GitHub Repository**: [https://github.com/Bochkov/BVV241-Tokenizers-Embeddings-Benchmarks](https://github.com/Bochkov/BVV241-Tokenizers-Embeddings-Benchmarks)
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 ## Model Description
 
 `abs-bvv-4` is a 1.9 billion parameter decoder-only Transformer model. It is the 4th model in the **Progressive Growth Transformers (PGT)** series, designed to explore how linguistic and reasoning capabilities emerge as a function of model depth.
 
-This model was not trained monolithically. Instead, it was "grown" constructively, one layer at a time, upon a foundation of **frozen, non-semantic visual embeddings**, as introduced in the paper "[Emergent Semantics Beyond Token Embeddings: Transformer LMs with Frozen Visual Unicode Representations](https://arxiv.org/abs/2507.04886)".
+This model was not trained monolithically. Instead, it was "grown" constructively, one layer at a time, upon a foundation of **frozen, non-semantic visual embeddings**, as introduced in the papers
+
+[📚 Paper (Emergent Semantics Beyond Token Embeddings: Transformer LMs with Frozen Visual Unicode Representations)](https://huggingface.co/papers/2507.04886) - 
+
+[📚 Paper (Growing Transformers: Modular Composition and Layer-wise Expansion on a Frozen Substrate)](https://huggingface.co/papers/2507.07129) - 
+
+[💻 Code](https://github.com/AVBochkov/Embeddings)
 
 The core idea is to demonstrate an alternative, more modular and resource-efficient paradigm for building LLMs. The PGT series shows that:
 1.  Semantic understanding can emerge without trainable embeddings.
@@ -34,31 +38,18 @@ This model is primarily an artifact for research into emergent capabilities, con
 
 ## Training Details
 Architecture: 4-layer Decoder-Only Transformer (n_layer=4, d_model=4096, n_head=32).
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 Embeddings: The token embedding layer is frozen and derived from visual representations of Unicode glyphs. It is never updated during training.
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 Training Method: Progressive Layer-Wise Growth. The model was built by training one layer at a time. Layer 1 was trained to convergence, then frozen. Layer 2 was added and trained, etc. For deeper layers (5 and 6), LoRA was used to fine-tune all existing layers simultaneously with the new layer to ensure global coherence.
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 Parameters: Total: 1.9B.
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 Data: A ~9B token mix of Wikipedia and SFT datasets (10%).
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 ## Limitations and Bias
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 This model is a research prototype and has several limitations:
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 Not Instruction-Tuned: It is a base model and will not follow instructions or engage in dialogue reliably.
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 Potential for Hallucinations: Like all LLMs, it can generate factually incorrect or nonsensical text.
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 Data Bias: Trained primarily on Wikipedia, it will reflect the biases present in that corpus.
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 Limited Scope: The model was trained on a relatively small dataset (9B tokens) compared to state-of-the-art models. Its performance is intended to be evaluated relative to its own baseline (trainable embeddings) and shallower versions, not against giant commercial models.
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 ## 🧑‍🔬 Citation & Concept
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 If you use this model or the underlying concepts in your research, please cite our work:
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 ```
 @misc{bochkov2025emergentsemanticstokenembeddings,
       title={Emergent Semantics Beyond Token Embeddings: Transformer LMs with Frozen Visual Unicode Representations}, 
@@ -69,7 +60,6 @@ If you use this model or the underlying concepts in your research, please cite o
       primaryClass={cs.CL},
       url={https://arxiv.org/abs/2507.04886}, 
 }
-
 @misc{bochkov2025growingtransformersmodularcomposition,
       title={Growing Transformers: Modular Composition and Layer-wise Expansion on a Frozen Substrate}, 
       author={A. Bochkov},
@@ -80,11 +70,8 @@ If you use this model or the underlying concepts in your research, please cite o
       url={https://arxiv.org/abs/2507.07129}, 
 }
 ```
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 This work demonstrates that transformer blocks, not token embeddings, carry the semantic burden in LLMs — a step toward modular, fusable, multilingual LMs.
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 ## How to Use
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 The model can be loaded using the `transformers` library. Note that `trust_remote_code=True` is required as it uses a custom model architecture.
 
 ```python