Update README.md

README.md

@@ -1,12 +1,36 @@
 ---
-# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
-# Doc / guide: https://huggingface.co/docs/hub/model-cards
-{}
+# Model card metadata following Hugging Face specification: 
+# https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
+# Documentation: https://huggingface.co/docs/hub/model-cards
+license: apache-2.0
+tags:
+  - flash-attention
+  - nvidia
+  - blackwell
+  - windows
+  - prebuilt-wheels
+  - python
+  - machine-learning
+  - deep-learning
 ---
 
-# flash-attn 2.7.4 for windows prebuilt wheels for nvidia blackwell cu128
-- Available for python 3.10 and 3.11
-- flash_attn-2.7.4.post1-cp310-cp310-win_amd64.whl
-- flash_attn-2.7.4.post1-cp311-cp311-win_amd64.whl
+# Flash-Attention 2.7.4 Prebuilt Wheels for NVIDIA Blackwell (cu128) on Windows
 
-Also works with previous generations, tested on 5090RTX and 3090RTX
+This repository provides prebuilt wheels for **Flash-Attention 2.7.4** optimized for NVIDIA Blackwell GPUs (cu128) on Windows systems. These wheels are compatible with Python 3.10 and 3.11, enabling seamless integration for high-performance attention mechanisms in deep learning workflows.
+
+## Available Wheels
+- `flash_attn-2.7.4.post1-cp310-cp310-win_amd64.whl` (Python 3.10)
+- `flash_attn-2.7.4.post1-cp311-cp311-win_amd64.whl` (Python 3.11)
+
+## Compatibility
+The prebuilt wheels are designed for NVIDIA Blackwell GPUs but have been tested and confirmed compatible with previous-generation NVIDIA GPUs, including:
+- NVIDIA RTX 5090
+- NVIDIA RTX 3090
+
+## Installation
+To install, use pip with the appropriate wheel for your Python version:
+
+```bash
+pip install flash_attn-2.7.4.post1-cp310-cp310-win_amd64.whl
+# or
+pip install flash_attn-2.7.4.post1-cp311-cp311-win_amd64.whl