Update README.md

README.md

@@ -3,15 +3,15 @@ license: apache-2.0
 tags:
 - biology
 - chemistry
-- IntFold
 - biomolecular-structure-prediction
+- IntelliFold
 ---
 
-![IntFold Cover](https://raw.githubusercontent.com/IntelliGen-AI/IntFold/main/assets/intfold-cover.png)
+![IntelliFold Cover](https://raw.githubusercontent.com/IntelliGen-AI/IntelliFold/main/assets/intellifold-cover.png)
 
-# IntFold: A Controllable Foundation Model for General and Specialized Biomolecular Structure Prediction.
+# IntelliFold: A Controllable Foundation Model for General and Specialized Biomolecular Structure Prediction.
 [![HuggingFace Models](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Models-yellow)](https://huggingface.co/GAGABIG/CNN)
-[![PyPI](https://img.shields.io/pypi/v/intfold)](https://pypi.org/project/intfold/)
+[![PyPI](https://img.shields.io/pypi/v/intellifold)](https://pypi.org/project/intellifold/)
 [![License](https://img.shields.io/badge/license-Apache%202.0-blue)](LICENSE)
 [![Email](https://img.shields.io/badge/Email-Contact-lightgrey?logo=gmail)](#contact-us)
 
@@ -22,38 +22,38 @@ tags:
 </div>
 
 
-![IntFold Model](https://raw.githubusercontent.com/IntelliGen-AI/IntFold/main/assets/Intfold-Model-Arc.png)
+![IntelliFold Model](https://raw.githubusercontent.com/IntelliGen-AI/IntelliFold/main/assets/Intellifold-Model-Arc.png)
 
 
 ## 🚀 Quick Start
 
 To quickly get started with IntelliFold, you can use the following commands:
 ```bash
-# Install IntFold from PyPI
-pip install intfold
+# Install IntelliFold from PyPI
+pip install intellifold
 # Run inference with an example YAML file
-intfold predict ./examples/5S8I_A.yaml --out_dir ./output
+intellifold predict ./examples/5S8I_A.yaml --out_dir ./output
 ```
 
 ## ⚙️ Installation
 
-To more complete installation instructions and usage, please refer to the [Installation Guide](https://github.com/IntelliGen-AI/IntFold/blob/main/docs/installation.md).
+To more complete installation instructions and usage, please refer to the [Installation Guide](https://github.com/IntelliGen-AI/IntelliFold/blob/main/docs/installation.md).
 
 
 ## 🔍 Inference
 
-1. **Prepare Input File**: Create a YAML file with your sequences following our [input format specification](https://github.com/IntelliGen-AI/IntFold/blob/main/docs/input_yaml_format.md)
+1. **Prepare Input File**: Create a YAML file with your sequences following our [input format specification](https://github.com/IntelliGen-AI/IntelliFold/blob/main/docs/input_yaml_format.md)
 
 2. **Run Prediction**:
    ```bash
-   intfold predict your_input.yaml --out_dir ./results
+   intellifold predict your_input.yaml --out_dir ./results
    ```
 
-3. **Check Results**: Find predicted structures and confidence scores in the output directory, you can also check the section of **output format** in [output documentation](https://github.com/IntelliGen-AI/IntFold/blob/main/docs/input_yaml_format.md#output-format).
+3. **Check Results**: Find predicted structures and confidence scores in the output directory, you can also check the section of **output format** in [output documentation](https://github.com/IntelliGen-AI/IntelliFold/blob/main/docs/input_yaml_format.md#output-format).
 
-4. **Optional Optimization**: Enable [custom kernels](https://github.com/IntelliGen-AI/IntFold/blob/main/docs/kernels.md) for faster inference and reduced memory usage
+4. **Optional Optimization**: Enable [custom kernels](https://github.com/IntelliGen-AI/IntelliFold/blob/main/docs/kernels.md) for faster inference and reduced memory usage
 
-For comprehensive usage instructions and examples, refer to the [Usage Guide](https://github.com/IntelliGen-AI/IntFold/blob/main/docs/usage.md).
+For comprehensive usage instructions and examples, refer to the [Usage Guide](https://github.com/IntelliGen-AI/IntelliFold/blob/main/docs/usage.md).
 
 
 ## 📊 Benchmarking
@@ -62,14 +62,14 @@ To comprehensively evaluate the performance of To quickly get started with Intel
 
 For more details on the benchmarking process and results, please refer to our [Technical Report](https://arxiv.org/abs/2507.02025).
 
-![Benchmark Metrics](https://raw.githubusercontent.com/IntelliGen-AI/IntFold/main/assets/intfold_metrics.png)
+![Benchmark Metrics](https://raw.githubusercontent.com/IntelliGen-AI/IntelliFold/main/assets/intellifold_metrics.png)
 
 
 ## 🌐 IntelliFold Server
 
 **We highly recommend using the [IntelliFold Server](https://server.intfold.com) for the most accurate, complete, and convenient biomolecular structure predictions.** It requires no installation and provides an intuitive web interface to submit your sequences and visualize results directly in your browser. The server runs the **full, optimized, latest** IntelliFold implementation for optimal performance.
 
-![IntelliFold Server](https://raw.githubusercontent.com/IntelliGen-AI/IntFold/main/assets/intfold-server-screenshot.png)
+![IntelliFold Server](https://raw.githubusercontent.com/IntelliGen-AI/IntelliFold/main/assets/intellifold-server-screenshot.png)
 
 
 ## 📜 Citation
@@ -91,14 +91,14 @@ If you use IntelliFold in your research, please cite our paper:
 ## 🔗 Acknowledgements
 
 - The implementation of **fast layernorm operators** is inspired by [OneFlow](https://github.com/Oneflow-Inc/oneflow) and [FastFold](https://github.com/hpcaitech/FastFold), following [Protenix](https://github.com/bytedance/Protenix)'s usage. 
-- Many components in `intfold/openfold/` are adapted from [OpenFold](https://github.com/aqlaboratory/openfold), with substantial modifications and improvements by our team (except for the `LayerNorm` part).  
+- Many components in `intellifold/openfold/` are adapted from [OpenFold](https://github.com/aqlaboratory/openfold), with substantial modifications and improvements by our team (except for the `LayerNorm` part).  
 - This repository, the implementation of **Inference Data Pipeline**(Data/Feature Processing and MSA generation tasks) referred to [Boltz-1](https://github.com/jwohlwend/boltz), and modify some codes to adapt to the input of our model.
 
 
 
 ## ⚖️ License
 
-The IntFold project, including code and model parameters, is made available under the [Apache 2.0 License](https://github.com/IntelliGen-AI/IntFold/blob/main/LICENSE), it is free for both academic research and commercial use.
+The IntelliFold project, including code and model parameters, is made available under the [Apache 2.0 License](https://github.com/IntelliGen-AI/IntelliFold/blob/main/LICENSE), it is free for both academic research and commercial use.
 
 ## 📬 Contact Us