Update README.md

README.md

@@ -12,12 +12,14 @@ Large Language Models (LLMs) are good with code generations. Sometimes they do m
 This is what I have tried over here. The base Llama-2 model was used for training purpose. It is trained on around 74000 set of codes. Each set having 2 conversations.
 Along with Python, Java, JavaScript, GO, C++, Rust etc. code with detailed explanation is used for training purpose. It is built upon using my existing Dataset [Python-Code-23k-ShareGPT](https://huggingface.co/datasets/ajibawa-2023/Python-Code-23k-ShareGPT).
 This conversation is in Vicuna/ShareGPT format. Each set, along with code, has detailed explanation. 
-I have released the new [data](https://huggingface.co/datasets/ajibawa-2023/Python-Code-23k-ShareGPT).
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+I have released the new data [Code-74k-ShareGPT](https://huggingface.co/datasets/ajibawa-2023/Code-74k-ShareGPT) on which this Model is trained.
 
 **Training:**
 
 Entire dataset was trained on Azure 4 x A100 80GB. For 3 epoch, training took 42 hours. DeepSpeed codebase was used for training purpose. This was trained on Llama-1 by Meta.
 
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 This is a full fine tuned model. Links for quantized models will be released soon.