Update README.md

README.md

@@ -39,7 +39,7 @@ co2_eq_emissions:
 
 [GGML with ChatGLM.cpp (recommended)](https://huggingface.co/CausalLM/miniG/tree/ggml): https://github.com/li-plus/chatglm.cpp
 
-[GGUF (Text-Only, not recommended)](https://huggingface.co/CausalLM/miniG/tree/gguf)
+[GGUF (Text-Only, not recommended)](https://huggingface.co/CausalLM/miniG/tree/gguf): There is a significant degradation, even with the F16.
 
 A model trained on a synthesis dataset of over **120 million** entries, this dataset having been generated through the application of state-of-the-art language models utilizing large context windows, alongside methodologies akin to retrieval-augmented generation and knowledge graph integration, where the data synthesis is conducted within clusters derived from a curated pretraining corpus of 20 billion tokens, with subsequent validation performed by the model itself.
 
@@ -71,7 +71,7 @@ Despite the absence of thorough alignment with human preferences, the model is u
 
 [GGML 用于 ChatGLM.cpp (推荐)](https://huggingface.co/CausalLM/miniG/tree/ggml): https://github.com/li-plus/chatglm.cpp
 
-[GGUF (纯文本，不推荐)](https://huggingface.co/CausalLM/miniG/tree/gguf)
+[GGUF (纯文本，不推荐)](https://huggingface.co/CausalLM/miniG/tree/gguf): 即使使用F16，性能也有显著下降。
 
 一个在超过**1.2亿**条数据合成数据集上训练的模型，这些数据集是通过应用具有大上下文窗口的最先进语言模型生成的，并结合了类似于检索增强生成和知识图谱集成的方法，数据合成是在一个由200亿个标记组成的预训练语料库中提取的聚类内进行的，随后由模型本身进行验证。