Update README.md

README.md

@@ -13,7 +13,7 @@ base_model:
 # Granite-3.1-8B-Instruct
 
 **Model Summary:**
-Granite-3.1-8B-Instruct is a 8B parameter model finetuned from *Granite-3.1-8B-Base* using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets. This model is developed using a diverse set of techniques with a structured chat format, including supervised finetuning, model alignment using reinforcement learning, and model merging.
+Granite-3.1-8B-Instruct is a 8B parameter long-context instruct model finetuned from *Granite-3.1-8B-Base* using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets tailored for solving long context problems. This model is developed using a diverse set of techniques with a structured chat format, including supervised finetuning, model alignment using reinforcement learning, and model merging.
 
 - **Developers:** Granite Team, IBM
 - **GitHub Repository:** [ibm-granite/granite-3.1-language-models](https://github.com/ibm-granite/granite-3.1-language-models)
@@ -37,6 +37,7 @@ The model is designed to respond to general instructions and can be used to buil
 * Code related tasks
 * Function-calling tasks
 * Multilingual dialog use cases
+* Long-context tasks including long document/meeting summarization, long document QA, etc.
 
 **Generation:** 
 This is a simple example of how to use Granite-3.1-8B-Instruct model.
@@ -98,7 +99,7 @@ Granite-3.1-8B-Instruct is based on a decoder-only dense transformer architectur
 | # Training tokens         | 12T      | **12T**      | 10T    | 10T    |
 
 **Training Data:** 
-Overall, our SFT data is largely comprised of three key sources: (1) publicly available datasets with permissive license, (2) internal synthetic data targeting specific capabilities, and (3) very small amounts of human-curated data. A detailed attribution of datasets can be found in the [Granite Technical Report]() and [Accompanying Author List]().
+Overall, our SFT data is largely comprised of three key sources: (1) publicly available datasets with permissive license, (2) internal synthetic data targeting specific capabilities including long-context tasks, and (3) very small amounts of human-curated data. A detailed attribution of datasets can be found in the [Granite Technical Report]() and [Accompanying Author List]().
 
 **Infrastructure:**
 We train Granite 3.1 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.