Update README.md

README.md

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 - language model
 - code generation
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-# CoDA: Coding LM via Diffusion Adaptation
 
-**CoDA-1.7B** is a lightweight diffusion language model for code generation developed by Salesforce AI Research. Unlike traditional autoregressive models, CoDA leverages discrete diffusion processes to enable bidirectional context understanding and efficient code completion.
+<p align="center">
+  <img alt="coda-logo" src="https://raw.githubusercontent.com/weirayao/CoDA/main/CoDA-logo.png">
+</p>
 
-- 📄 [Technical Report](https://github.com/SalesforceAIResearch/CoDA/blob/main/technical_report.pdf)
-- 💻 [Code Repository](https://github.com/SalesforceAIResearch/CoDA/)
+<p align="center">
+  <a href="https://github.com/SalesforceAIResearch/CoDA"><strong>Try CoDA</strong></a> ·
+  <a href="https://github.com/SalesforceAIResearch/CoDA/blob/main/technical_report.pdf"><strong>Technical Report</strong></a> ·
+  <a href="https://huggingface.co/collections/Salesforce/coda-68d627d87921c0e28a69e340"><strong>Model Collection</strong></a> ·
+  <a href="https://github.com/SalesforceAIResearch/CoDA/blob/main/README.md"><strong>GitHub Repository</strong></a>
+</p>
+
+<br>
+
+Welcome to CoDA, Salesforce AI Research's diffusion-based language model designed for powerful code generation and bidirectional context understanding.
+
+We're releasing CoDA as a lightweight yet capable model:
+- `CoDA-1.7B-Base` — diffusion foundation model with bidirectional diffusion architecture, ideal for further fine-tuning and RL training
+- `CoDA-1.7B-Instruct` — optimized for code generation tasks with bidirectional diffusion modeling (1.7B parameters)
+
+CoDA leverages discrete diffusion processes to enable understanding of both past and future tokens, making it uniquely suited for code completion and generation tasks where context flows in both directions.
+
+> [!NOTE]
+> This model card is dedicated to the `CoDA-1.7B-Base` model. Check out our [model collection](https://huggingface.co/collections/Salesforce/coda-68d627d87921c0e28a69e340) for other variants.
+
+# ⭐️ Highlights
+
+* **Bidirectional Context Understanding:** Leverage discrete diffusion processes to understand both past and future tokens, enabling superior code completion.
+* **Confidence-Guided Sampling:** Maintain competitive inference latency through intelligent sampling strategies that balance quality and speed.
+* **Lightweight Architecture:** Achieve strong performance with only 1.7B parameters, making it accessible for researchers with limited computational resources.
+* **Full Training Pipeline:** Complete reproducible training pipeline from pre-training to fine-tuning, enabling customization for specific domains.
+* **Optimized for Code:** Specifically designed and trained for code generation tasks, with strong performance on HumanEval, MBPP, and other coding benchmarks.
+
+---
 
 ## 📊 Model Details