Update README.md

README.md

@@ -68,7 +68,7 @@ library_name: transformers
 
 ![30bcoder](https://cdn-uploads.huggingface.co/production/uploads/67d46bc5fe6ad6f6511d6f44/uolLKxUkpOc_eQYIysPs_.png)
 
-# 💻 Next-CodeX 30B (L846MoE)
+# 💻 Next-Codex (L846MoE)
 
 ### Code your future with our models.
 
@@ -80,9 +80,9 @@ library_name: transformers
 
 ## 📖 Overview
 
-**Next-CodeX 30B** is a high-performance, specialized **Mixture-of-Experts (MoE)** Large Language Model designed specifically for code generation, debugging, and software engineering tasks.
+**Next-Codex 30B** is a high-performance, specialized **Mixture-of-Experts (MoE)** Large Language Model designed specifically for code generation, debugging, and software engineering tasks.
 
-Unlike traditional dense models, **Next-CodeX** utilizes a sparse architecture with **30 Billion total parameters**, but only activates **3 Billion parameters per token**. This unique design allows it to deliver the deep reasoning capabilities of a massive model while maintaining the ultra-low latency and inference cost of a lightweight 3B model. It is fine-tuned on a massive corpus of code across 20+ programming languages, making it the most efficient coding assistant in its class.
+Unlike traditional dense models, **Next-Codex** utilizes a sparse architecture with **30 Billion total parameters**, but only activates **3 Billion parameters per token**. This unique design allows it to deliver the deep reasoning capabilities of a massive model while maintaining the ultra-low latency and inference cost of a lightweight 3B model. It is fine-tuned on a massive corpus of code across 20+ programming languages, making it the most efficient coding assistant in its class.
 
 ---
 
@@ -101,7 +101,7 @@ Unlike traditional dense models, **Next-CodeX** utilizes a sparse architecture w
 
 **Next-Coder 30B** achieves state-of-the-art results among open-weights coding models, balancing extreme efficiency with high accuracy.
 
-| Benchmark | Task Description | Next-Coder 30B (MoE) | CodeLlama 34B | DeepSeek Coder 33B |
+| Benchmark | Task Description | Next-Codex | CodeLlama 34B | DeepSeek Coder 33B |
 | :--- | :--- | :---: | :---: | :---: |
 | **HumanEval** | Python Code Generation | **82.4%** | 48.2% | 79.3% |
 | **MBPP** | Basic Python Programming | **86.1%** | 56.0% | 84.0% |
@@ -131,7 +131,7 @@ model, tokenizer = FastLanguageModel.from_pretrained(
 )
 
 messages = [
-    {"role": "system", "content": "You are Next-CodeX, an expert software engineer and AI coding assistant."},
+    {"role": "system", "content": "You are Next-Codex, an expert software engineer and AI coding assistant."},
     {"role" : "user", "content" : "Write a highly optimized Rust function to calculate the Fibonacci sequence using memoization."}
 ]