Update README.md

README.md

@@ -10,7 +10,9 @@ base_model:
 
 <img src="https://huggingface.co/LLM360/K2-V2/resolve/main/figures/K2.LOGO.PRIMARY.RGB.png" width="100" alt="K2-V2 model logo"/>
 
-📚 [Tech Report](https://www.llm360.ai/reports/K2_V2_report.pdf) - 📝 [Code](https://github.com/llm360/k2v2_train) - 🏢 [Project Page](https://huggingface.co/LLM360/K2-V2-Instruct)
+📚 [Tech Report](https://www.llm360.ai/reports/K2_V2_report.pdf) - 📝 [Training Code](https://github.com/llm360/k2v2_train) - 🏢 [Evaluation Code](https://github.com/llm360/eval360) 
+
+🗂️ [Pretraining Data: TxT360](https://huggingface.co/datasets/LLM360/TxT360) - 🗂️ [Midtraining Data: TxT360-Midas](https://huggingface.co/datasets/LLM360/TxT360-Midas) - 🗂️ [SFT Data: TxT360-3efforts](https://huggingface.co/datasets/LLM360/TxT360-Midas)
 
 K2-V2 is our most capable fully open model to date, and one of the strongest open-weight models in its class. It uses a 70B-parameter dense transformer architecture and represents the latest advancement in the LLM360 model family.
 
@@ -134,7 +136,7 @@ If you use K2-V2-Instruct in your research, please cite the following:
 
 ```
 @misc{llm360_k2v2_2025,
-  title         = {K2-V2: A 360-Open, Reasoning-Enhanced Open LLM},
+  title         = {K2-V2: A 360-Open, Reasoning-Enhanced LLM},
   author        = {K2 Team},
   year          = {2025},
   archivePrefix = {arXiv},