docs: highlight 8,192 token context window in model card

README.md

@@ -235,14 +235,14 @@ model-index:
 
 # 💻 code-embed-v2
 
-**CodeEmbed v2** is a lightweight, state-of-the-art multilingual code embedding model (≤131M parameters) developed by **[Divakant Pandey](https://github.com/divakantpandey)**. Specialized for semantic code search, code-to-code retrieval, text-to-SQL mapping, and repository navigation across **6 major programming languages** with an extended **1,024 token context window** and native **Matryoshka Representation Learning (MRL)**.
+**CodeEmbed v2** is a lightweight, state-of-the-art multilingual code embedding model (≤131M parameters) developed by **[Divakant Pandey](https://github.com/divakantpandey)**. Specialized for semantic code search, code-to-code retrieval, text-to-SQL mapping, and repository navigation across **6 major programming languages** with an extended **8,192 token context window** and native **Matryoshka Representation Learning (MRL)**.
 
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 ## 🚀 Highlights & Functional Capabilities
 
 - **Multilingual Support**: High-accuracy semantic search across **Python, JavaScript, Go, Java, Ruby, and PHP**.
-- **Extended Context**: Full **1,024 token context window** allowing whole-file, multi-function, and long class embeddings without aggressive truncation.
+- **Extended Context**: Full **8,192 token context window** allowing whole-file, multi-function, and long class embeddings without aggressive truncation.
 - **Matryoshka Representation Learning (MRL)**: Embeddings can be dynamically truncated from **1536** down to **768, 384, 256, 128, or 64 dimensions** with negligible loss in accuracy, enabling significant vector database memory and latency savings.
 - **Compact & Efficient**: ~131M parameters (**~484 MB** safetensors footprint) optimized for low-latency CPU inference and fast GPU batch indexing.
 - **Universal Compatibility**: Works out of the box with `sentence-transformers`, `transformers`, and PyTorch.