Update README.md

README.md

@@ -50,10 +50,13 @@ You can download the full details of our grid search for all models in all tasks
 
 For the XL-Sum task, we choose our best run for each model using the eval set. We use the official evaluation script from XL-Sum, which uses the stemmer function, which may show better results than papers that don't use the stemmer function. The official XL-Sum paper uses a stemmer function.
 
+# FineTuning our efficient ArabicT5-49GB-Small model with Torch on 3070 laptop GPU ###
+
+If you are running your code on a laptop GPU (e.g., a gaming laptop) or limited GPU memory, we recommended using our ArabicT5-49GB-Small model, which was the only model from the list that we were able to run on 3070 Laptop card with a batch size of 8. We manage to achieve an F1 score of 85.391 (slightly better than our FLAX code ) on the TyDi QA task. See the notebook below for reference : 
+
+[![Open In Colab][COLAB]](https://colab.research.google.com/github/salrowili/ArabicT5/blob/main/ArabicT5_49GB_Small_on_3070_Laptop_GPU.ipynb)
+
 
-# Continual Pre-Training of ArabicT5 with T5x
-if you want to continue pre-training ArabicT5 on your own data, we have uploaded the raw t5x checkpoint to this link https://huggingface.co/sultan/ArabicT5-49GB-base/blob/main/arabict5_49GB_base_t5x.tar.gz
-We will soon share a tutorial on how you can do that for free with Kaggle TPU
 
 
 
@@ -63,6 +66,12 @@ We will soon share a tutorial on how you can do that for free with Kaggle TPU
 
 [COLAB]: https://colab.research.google.com/assets/colab-badge.svg
 
+
+# Continual Pre-Training of ArabicT5 with T5x
+if you want to continue pre-training ArabicT5 on your own data, we have uploaded the raw t5x checkpoint to this link https://huggingface.co/sultan/ArabicT5-49GB-base/blob/main/arabict5_49GB_base_t5x.tar.gz
+We will soon share a tutorial on how you can do that for free with Kaggle TPU
+
+
 ## GitHub Page
 
 https://github.com/salrowili/ArabicT5