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@@ -86,6 +86,11 @@ Obviously, we encountered better inference quality for models with the highest b
 
 Each model's memory footprint can be anticipated by the qunatization docs in either [Hugging Face](https://huggingface.co/docs/transformers/main/en/quantization/overview) or [llama.cpp](https://github.com/ggerganov/llama.cpp/tree/master/examples/quantize).
 
+
+# Acknowledgments
+- Research supported with Cloud TPUs from [Google's TensorFlow Research Cloud](https://sites.research.google/trc/about/) (TFRC). Thanks for providing access to the TFRC ❤️
+- Thanks to the generous support from the Hugging Face team, it is possible to download models from their S3 storage 🤗
+
 ## Contact
 *Feel free to contact us whenever you confront any problems :)*