Link paper and GitHub repository, add task category

README.md

@@ -1,12 +1,19 @@
 ---
 license: apache-2.0
+task_categories:
+- text-generation
 ---
 
-
 # Prolong_64K_v2_Llama2_Tokenizer
 
 This is the Prolong_64K dataset, tokenized using the [Llama-2-7b-hf tokenizer](https://github.com/microsoft/Samba/blob/main/scripts/prepare_slimpajama.py#L22) for use in Samba-style training.
 
+This dataset was used in the research paper: [Rethinking Language Model Scaling under Transferable Hypersphere Optimization](https://huggingface.co/papers/2603.28743).
+
+The official training codebase can be found at [GitHub - microsoft/ArchScale](https://github.com/microsoft/ArchScale).
+
+## Download
+
 👉 You can download and unzip the dataset from: [prolong_64K_v2.zip](https://huggingface.co/datasets/jsun/Prolong_64K_v2_Llama2_Tokenizer/blob/main/prolong_64K_v2.zip)
 
 ```bash
@@ -14,4 +21,9 @@ wget -c https://huggingface.co/datasets/jsun/Prolong_64K_v2_Llama2_Tokenizer/res
 sudo apt install zip # Ubuntu
 unzip prolong_64K_v2.zip -d prolong_64K_v2
 ```
-Once extracted, the dataset can be loaded using the [PackedDataset](https://github.com/microsoft/Samba/blob/383c016f2fb20ce75eed777761e1a4456c87b2b0/lit_gpt/packed_dataset.py#L33) class from the Samba codebase.
+
+## Usage
+
+Once extracted, the dataset can be loaded using the [PackedDataset](https://github.com/microsoft/Samba/blob/383c016f2fb20ce75eed777761e1a4456c87b2b0/lit_gpt/packed_dataset.py#L33) class from the Samba/ArchScale codebase.
+
+Example training scripts utilizing this data format are provided in the [ArchScale repository](https://github.com/microsoft/ArchScale).