Update README.md

README.md

@@ -7,6 +7,11 @@ pipeline_tag: text-generation
 tags:
 - language
 - granite-3.0
+- mainframe
+- s390x
+- z15
+- z16
+- big-endian
 quantized_by: taronaeo
 ---
 # Granite 3.0 1B A400M Instruct Big-Endian - GGUF (Verified for IBM Z & LinuxONE Mainframes)
@@ -126,4 +131,4 @@ Overall, our SFT data is largely comprised of three key sources: (1) publicly av
 We train Granite 3.0 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 while minimizing environmental impact by utilizing 100% renewable energy sources.
 
 **Ethical Considerations and Limitations:** 
-Granite 3.0 Instruct Models are primarily finetuned using instruction-response pairs mostly in English, but also multilingual data covering eleven languages. Although this model can handle multilingual dialog use cases, its performance might not be similar to English tasks. In such case, introducing a small number of examples (few-shot) can help the model in generating more accurate outputs. While this model has been aligned by keeping safety in consideration, the model may in some cases produce inaccurate, biased, or unsafe responses to user prompts. So we urge the community to use this model with proper safety testing and tuning tailored for their specific tasks.
+Granite 3.0 Instruct Models are primarily finetuned using instruction-response pairs mostly in English, but also multilingual data covering eleven languages. Although this model can handle multilingual dialog use cases, its performance might not be similar to English tasks. In such case, introducing a small number of examples (few-shot) can help the model in generating more accurate outputs. While this model has been aligned by keeping safety in consideration, the model may in some cases produce inaccurate, biased, or unsafe responses to user prompts. So we urge the community to use this model with proper safety testing and tuning tailored for their specific tasks.