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 # BitBLAS
 
 BitBLAS is a library to support mixed-precision BLAS operations on GPUs, for example, the $W_{wdtype}A_{adtype}$ mixed-precision matrix multiplication where $C_{cdtype}[M, N] = A_{adtype}[M, K] \times W_{wdtype}[N, K]$.
-BitBLAS aims to support efficient mixed-precision DNN model deployment, especially the $W_{wdtype}A_{adtype}$ quantization in large language models (LLMs), for example, the $W_{UINT4}A_{FP16}$ in [GPTQ](https://arxiv.org/abs/2210.17323), the $W_{INT2}A_{FP16}$ in [BitDistiller](https://arxiv.org/abs/2402.10631), the $W_{INT2}A_{INT8}$ in [BitNet-b1.58](https://arxiv.org/abs/2402.17764). BitBLAS is based on techniques from our accepted submission at OSDI'24.
+BitBLAS aims to support efficient mixed-precision DNN model deployment, especially the $W_{wdtype}A_{adtype}$ quantization in large language models (LLMs), for example, the $W_{UINT4}A_{FP16}$ in [GPTQ](https://arxiv.org/abs/2210.17323), the $W_{INT2}A_{FP16}$ in [BitDistiller](https://arxiv.org/abs/2402.10631), the $W_{INT2}A_{INT8}$ in [BitNet-b1.58](https://arxiv.org/abs/2402.17764). BitBLAS is based on techniques from our accepted submission "Ladder: Enabling Efficient Low-Precision Deep Learning Computing through Hardware-aware Tensor Transformation" at OSDI'24.
 
 
 Some of the key features of BitBLAS include:
@@ -83,11 +83,27 @@ We are continuously expanding the support matrix. If you have any specific requi
   - ```bitblas.Matmul``` implements the $W_{wdtype}A_{adtype}$ mixed-precision matrix multiplication of $C_{cdtype}[M, N] = A_{adtype}[M, K] \times W_{wdtype}[N, K]$.
   - ```bitblas.Linear``` is a PyTorch ```nn.Linear```-like module to support a Linear of mixed-precision.
 
+- [Python API](https://github.com/microsoft/BitBLAS/blob/main/docs/PythonAPI.md): The Python API doc of BitBLAS.
+
 - [Integration](https://github.com/microsoft/BitBLAS/tree/main/integration): Explore how BitBLAS seamlessly integrates with LLM deployment frameworks through our examples. Discover the ease of integrating BitBLAS with PyTorch, AutoGPTQ, and vLLM in the 3rd-party integration examples.
 
 - [Customization](https://github.com/microsoft/BitBLAS/blob/main/docs/ExtendOperatorsWithDSL.md): BitBLAS supports implementing customized mixed-precision DNN operations rather than matrix multiplication with the flexible DSL (TIR Script).
 
 
+## Reference
+
+Please cite BitBLAS/Ladder in your publications if it helps your research:
+```tex
+@inproceedings {ladder-osdi24,
+author = {Lei Wang and Lingxiao Ma and Shijie Cao and Quanlu Zhang and Jilong Xue and Yining Shi and Ningxin Zheng and Ziming Miao and Fan Yang and Ting Cao and Yuqing Yang and Mao Yang},
+title = {Ladder: Enabling Efficient Low-Precision Deep Learning Computing through Hardware-aware Tensor Transformation},
+booktitle = {18th USENIX Symposium on Operating Systems Design and Implementation (OSDI 24)},
+year = {2024},
+url = {https://www.usenix.org/conference/osdi24/presentation/wang-lei},
+}
+```
+
+
 ## Contributing
 
 This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.