data update

README.md

@@ -309,11 +309,6 @@ Granite Language Instruct models are trained on a collection of publicly availab
 * Math: [MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA), [StackMathQA](https://huggingface.co/datasets/math-ai/StackMathQA ), and [MathInstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct)
 * Tools: [xlam-function-calling](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k), [Glaive Function Calling V2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2), [Hermes Function Calling V1](https://huggingface.co/datasets/NousResearch/hermes-function-calling-v1), and IBM Synthetic API data.
 * Safety: [SimpleSafetyTests](https://huggingface.co/datasets/Bertievidgen/SimpleSafetyTests), [HarmBench Behaviors](https://github.com/centerforaisafety/HarmBench/blob/main/data/behavior_datasets/harmbench_behaviors_text_all.csv), [Strong Reject](https://github.com/alexandrasouly/strongreject/blob/main/strongreject_dataset/strongreject_dataset.csv), [AdvBench](https://huggingface.co/datasets/walledai/AdvBench), [MistralGuard](https://huggingface.co/datasets/natolambert/xstest-v2-copy), [Do-Not-Answer](https://huggingface.co/datasets/LibrAI/do-not-answer), and IBM Synthetic data for safety.
-<!-- ### Instruction Datasets
-* Language Instruction Datasets: We include high-quality datasets such as [TO DO: List of datasets]
-* Synthetic Instruction Datasets: [TO DO: paragraph about synthetic data]
-### Processing
-* [TO DO: Data annotation with MagPie pipeline: quality, duplicates] -->
 
 ## Infrastructure
 We train the Granite Language models using IBM's super computing cluster, Blue Vela, which is outfitted with NVIDIA H100 GPUs. This cluster provides a scalable and efficient infrastructure for training our models over thousands of GPUs.