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new d6f922a Fix README.md (#183)
d6f922a is described below
commit d6f922a6c3799fb659318b2f0129bfeac73df711
Author: Junru Shao <[email protected]>
AuthorDate: Tue Oct 21 04:49:41 2025 -0700
Fix README.md (#183)
* Fix markdown lint
* Add links to:
📚 [Documentation](https://tvm.apache.org/ffi/) | 🚀
[Quickstart](https://tvm.apache.org/ffi/get_started/quickstart.html)
* Removed CI badge
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README.md | 10 +++++-----
1 file changed, 5 insertions(+), 5 deletions(-)
diff --git a/README.md b/README.md
index f238e7c..91901a6 100644
--- a/README.md
+++ b/README.md
@@ -17,7 +17,7 @@
# TVM FFI: Open ABI and FFI for Machine Learning Systems
-[](https://github.com/apache/tvm-ffi/actions/workflows/ci_test.yml)
+📚 [Documentation](https://tvm.apache.org/ffi/) | 🚀
[Quickstart](https://tvm.apache.org/ffi/get_started/quickstart.html)
Apache TVM FFI is an open ABI and FFI for machine learning systems. It is a
minimal, framework-agnostic,
yet flexible open convention with the following systems in mind:
@@ -30,10 +30,10 @@ yet flexible open convention with the following systems in
mind:
## Features
-* **Stable, minimal C ABI** designed for kernels, DSLs, and runtime
extensibility.
-* **Zero-copy interop** across PyTorch, JAX, and CuPy using [DLPack
protocol](https://data-apis.org/array-api/2024.12/design_topics/data_interchange.html).
-* **Compact value and call convention** covering common data types for ultra
low-overhead ML applications.
-* **Multi-language support** out of the box: Python, C++, and Rust (with a
path towards more languages).
+- **Stable, minimal C ABI** designed for kernels, DSLs, and runtime
extensibility.
+- **Zero-copy interop** across PyTorch, JAX, and CuPy using [DLPack
protocol](https://data-apis.org/array-api/2024.12/design_topics/data_interchange.html).
+- **Compact value and call convention** covering common data types for ultra
low-overhead ML applications.
+- **Multi-language support** out of the box: Python, C++, and Rust (with a
path towards more languages).
These enable broad **interoperability** across frameworks, libraries, DSLs,
and agents; the ability to **ship one wheel** for multiple frameworks and
Python versions (including free-threaded Python); and consistent infrastructure
across environments.
