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[DOCS] readme update. #18

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Apr 16, 2024
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[DOCS] readme update. #18

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Update dependabot.yml
LeiWang1999 Apr 16, 2024
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remove dependency.
LeiWang1999 Apr 16, 2024
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Merge branch 'main' of https://github.com/microsoft/BitBLAS into bitnet
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UPDATE INT8xINT2
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refactor the docs' link
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LeiWang1999 Apr 16, 2024
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Update installation and integration links in README.md
LeiWang1999 Apr 16, 2024
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Merge branch 'main' into bitnet
LeiWang1999 Apr 16, 2024
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11 changes: 6 additions & 5 deletions README.md
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Expand Up @@ -68,16 +68,17 @@ We are continuously expanding the support matrix. If you have any specific requi

## Getting Started

- [Installation](/docs/Installation.md):
To install BitBLAS, please checkout the document [installation](/docs/Installation.md). Also Make sure you already have the cuda toolkit (version >= 11) installed in the system. Or you can easily install from `pip install bitblas` in the root directory.
- [Installation](docs/Installation.md):
To install BitBLAS, please checkout the document [installation](docs/Installation.md). Also Make sure you already have the cuda toolkit (version >= 11) installed in the system. Or you can easily install from `pip install bitblas` in the root directory.

- [QuickStart](/docs/QuickStart.md): BitBLAS provides two Python APIs to perform mixed-precision matrix multiplication:
- [QuickStart](docs/QuickStart.md): BitBLAS provides two Python APIs to perform mixed-precision matrix multiplication:
- ```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.

- [Integration](/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.
- [Integration](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](docs/ExtendOperatorsWithDSL.md): BitBLAS supports implementing customized mixed-precision DNN operations rather than matrix multiplication with the flexible DSL (TIR Script).

- [Customization](/docs/ExtendOperatorsWithDSL.md): BitBLAS supports implementing customized mixed-precision DNN operations rather than matrix multiplication with the flexible DSL (TIR Script).

## Contributing

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