Update README.md

README.md

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 # MoMonir/Phi-3-mini-128k-instruct-GGUF
-This model was converted to GGUF format from [`microsoft/Phi-3-mini-128k-instruct`](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct)
-Refer to the [original model card](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) for more details on the model.
+This model was converted to GGUF format from [`nvidia/Llama3-ChatQA-1.5-8B`](https://huggingface.co/nvidia/Llama3-ChatQA-1.5-8B)
+Refer to the [original model card](https://huggingface.co/nvidia/Llama3-ChatQA-1.5-8B) for more details on the model.
 
 <!-- README_GGUF.md-about-gguf start -->
 ### About GGUF ([TheBloke](https://huggingface.co/TheBloke) Description)
@@ -36,7 +36,7 @@ Here is an incomplete list of clients and libraries that are known to support GG
 
 <!-- README_GGUF.md-about-gguf end -->
 
-
+## #--# Original Model Card #--#
 ## Model Details
 We introduce Llama3-ChatQA-1.5, which excels at conversational question answering (QA) and retrieval-augmented generation (RAG). Llama3-ChatQA-1.5 is developed using an improved training recipe from [ChatQA (1.0)](https://arxiv.org/abs/2401.10225), and it is built on top of [Llama-3 base model](https://huggingface.co/meta-llama/Meta-Llama-3-8B). Specifically, we incorporate more conversational QA data to enhance its tabular and arithmetic calculation capability. Llama3-ChatQA-1.5 has two variants: Llama3-ChatQA-1.5-8B and Llama3-ChatQA-1.5-70B. Both models were originally trained using [Megatron-LM](https://github.com/NVIDIA/Megatron-LM), we converted the checkpoints to Hugging Face format.