Update README.md acknowledgement section

README.md

@@ -25,6 +25,10 @@ https://github.com/user-attachments/assets/7f46b736-edec-4828-b809-4be780a3e5b1
 - 02/27/2024 [The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits](https://arxiv.org/abs/2402.17764)
 - 10/17/2023 [BitNet: Scaling 1-bit Transformers for Large Language Models](https://arxiv.org/abs/2310.11453)
 
+## Acknowledgements
+
+This project is based on the [llama.cpp](https://github.com/ggerganov/llama.cpp) framework. We would like to thank all the authors for their contributions to the open-source community. Also, bitnet.cpp's kernels are built on top of the Lookup Table methodologies pioneered in [T-MAC](https://github.com/microsoft/T-MAC/). For inference of general low-bit LLMs beyond ternary models, we recommend using T-MAC.
+
 ## Supported Models
 ❗️**We use existing 1-bit LLMs available on [Hugging Face](https://huggingface.co/) to demonstrate the inference capabilities of bitnet.cpp. These models are neither trained nor released by Microsoft. We hope the release of bitnet.cpp will inspire the development of 1-bit LLMs in large-scale settings in terms of model size and training tokens.**
 
@@ -230,6 +234,4 @@ python utils/generate-dummy-bitnet-model.py models/bitnet_b1_58-large --outfile
 python utils/e2e_benchmark.py -m models/dummy-bitnet-125m.tl1.gguf -p 512 -n 128
 ```
 
-## Acknowledgements
 
-This project is based on the [llama.cpp](https://github.com/ggerganov/llama.cpp) framework. We would like to thank all the authors for their contributions to the open-source community. We also thank [T-MAC](https://github.com/microsoft/T-MAC/) team for the helpful discussion on the LUT method for low-bit LLM inference.