Update README.md

README.md

@@ -15,7 +15,7 @@ A self‑attentive embedding model for premise / proof selection in Rocq‑based
 
 ### Model Description
 
-RocqStar is a 108 M‑parameter Transformer encoder (12 layers, 768‑dim hidden size) with multi‑head self‑attention and a learned self‑attentive pooling head. It is trained with an InfoNCE contrastive objective so that the cosine similarity of two statement embeddings approximates the similarity of their proofs, measured by a hybrid Levenshtein + Jaccard distance. The model takes tokenised Rocq (Gallina) theorem statements as input and outputs a 768‑d embedding.
+RocqStar is a 125 M‑parameter Transformer encoder (768‑dim hidden size) with multi‑head self‑attention and a learned self‑attentive pooling head. It is trained with an InfoNCE contrastive objective so that the cosine similarity of two statement embeddings approximates the similarity of their proofs, measured by a hybrid Levenshtein + Jaccard distance. The model takes tokenised Rocq (Gallina) theorem statements as input and outputs a 768‑d embedding.
 
 * **Model type:** Transformer encoder with self‑attentive pooling
 * **Language(s):** Rocq / Coq (Gallina) syntax (tokens)