Update README.md

README.md

@@ -33,8 +33,8 @@ MiniCPM4 series are highly efficient large language models (LLMs) designed expli
 - [BitCPM4-1B](https://huggingface.co/openbmb/BitCPM4-1B): Extreme ternary quantization applied to MiniCPM3-1B compresses model parameters into ternary values, achieving a 90% reduction in bit width.
 - [MiniCPM4-Survey](https://huggingface.co/openbmb/MiniCPM4-Survey): Based on MiniCPM4-8B, accepts users' quiries as input and autonomously generate trustworthy, long-form survey papers.
 - [MiniCPM4-MCP](https://huggingface.co/openbmb/MiniCPM4-MCP): Based on MiniCPM4-8B, accepts users' queries and available MCP tools as input and autonomously calls relevant MCP tools to satisfy users' requirements.
-- [BitCPM4-0.5-GGUF](https://huggingface.co/openbmb/BitCPM4-0.5B-GGUF): GGUF version of BitCPM4-0.5B.
-- [BitCPM4-0.5-GGUF](https://huggingface.co/openbmb/BitCPM4-1B-GGUF): GGUF version of BitCPM4-1B. (**<-- you are here**)
+- [BitCPM4-0.5B-GGUF](https://huggingface.co/openbmb/BitCPM4-0.5B-GGUF): GGUF version of BitCPM4-0.5B.
+- [BitCPM4-1B-GGUF](https://huggingface.co/openbmb/BitCPM4-1B-GGUF): GGUF version of BitCPM4-1B. (**<-- you are here**)
 ## Introduction
 BitCPM4 are ternary quantized models derived from the MiniCPM series models through quantization-aware training (QAT), achieving significant improvements in both training efficiency and model parameter efficiency.
 - Improvements of the training method