This is an automated email from the ASF dual-hosted git repository.
tqchen pushed a commit to branch main
in repository https://gitbox.apache.org/repos/asf/tvm-ffi.git
The following commit(s) were added to refs/heads/main by this push:
new efd0a08 Initial stab on README (#118)
efd0a08 is described below
commit efd0a08c54924e7bcc6c1f9e52cf68b5c61972d7
Author: Tianqi Chen <[email protected]>
AuthorDate: Tue Oct 14 15:22:02 2025 -0400
Initial stab on README (#118)
---
README.md | 21 ++++++++++++++++++++-
1 file changed, 20 insertions(+), 1 deletion(-)
diff --git a/README.md b/README.md
index 88083e9..c21e46f 100644
--- a/README.md
+++ b/README.md
@@ -15,6 +15,25 @@
<!--- specific language governing permissions and limitations -->
<!--- under the License. -->
-# tvm ffi
+# TVM FFI: Open ABI and FFI for Machine Learning Systems
[](https://github.com/apache/tvm-ffi/actions/workflows/ci_test.yml)
+
+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:
+
+- Kernel libraries: ship one wheel to support multiple frameworks, Python
versions, and different languages.
+- Kernel DSLs: reusable open ABI for JIT and AOT kernel exposure to PyTorch,
JAX, and other machine learning systems.
+- ML frameworks and runtimes: unified mechanism to connect libraries and DSLs
that adopt the ABI convention.
+- Coding agents: unified mechanism to package and ship generated code to
production environments.
+- ML infrastructure: cross-language support for Python, C++, Rust, and other
languages that interface with the ABI.
+
+It has the following technical features:
+
+- Stable, minimal, and flexible C ABI to support machine learning system
use-cases.
+- First class support for PyTorch, JAX, and other array libraries.
+- Out-of-the-box multi-language support for Python, C++, Rust, and future
compatibility to other languages that bind to the ABI.
+
+With these technical solutions, we can enable better **interoperability**
across machine learning frameworks,
+libraries, kernel DSLs, and coding agents, **ship one wheel** to support
multiple frameworks and Python versions (including free-threaded python),
+and build infrastructure solutions across environments.
