Improve model card: add arXiv ID, H1 title, and update paper links

This PR enhances the model card by:
1. Adding the `arxiv` identifier to the metadata for improved discoverability.
2. Introducing a top-level H1 heading to clearly state the model's name.
3. Updating the paper links in the introductory quick links and the "Resources" section to point to the official Hugging Face Papers page ([CoDA: Coding LM via Diffusion Adaptation](https://huggingface.co/papers/2510.03270)).
4. Integrating the paper citation directly into the model's introduction.
5. Removing the outdated "Technical report coming soon" statement.

These changes provide a more comprehensive, structured, and up-to-date model card.

README.md

@@ -1,29 +1,32 @@
 ---
-license: cc-by-nc-4.0
 language:
 - en
+library_name: transformers
+license: cc-by-nc-4.0
 pipeline_tag: text-generation
 tags:
 - text diffusion model
 - language model
 - code generation
-library_name: transformers
+arxiv: 2510.03270
 ---
 
+# CoDA: Coding LM via Diffusion Adaptation
+
 <p align="center">
   <img alt="coda-logo" src="https://raw.githubusercontent.com/weirayao/CoDA/main/CoDA-logo.png">
 </p>
 
 <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/papers/2510.03270"><strong>Paper</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.
+Welcome to CoDA, Salesforce AI Research's diffusion-based language model designed for powerful code generation and bidirectional context understanding, presented in the paper [CoDA: Coding LM via Diffusion Adaptation](https://huggingface.co/papers/2510.03270).
 
 We're releasing CoDA as a lightweight yet capable model:
 - `CoDA-1.7B-Instruct` — optimized for code generation tasks with bidirectional diffusion modeling (1.7B parameters)
@@ -34,29 +37,29 @@ CoDA leverages discrete diffusion processes to enable understanding of both past
 > [!NOTE]
 > This model card is dedicated to the `CoDA-1.7B-Instruct` model. Check out our [model collection](https://huggingface.co/collections/Salesforce/coda-68d627d87921c0e28a69e340) for other variants.
 
-# ⭐️ Highlights
+# ⭐ 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.
+*   **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
 
-- **Model Size**: 1.7B parameters
-- **Architecture**: Diffusion-based language model
-- **Training**: TPU-based pre-training with GPU fine-tuning
-- **Primary Use**: Code generation and completion tasks
+-   **Model Size**: 1.7B parameters
+-   **Architecture**: Diffusion-based language model
+-   **Training**: TPU-based pre-training with GPU fine-tuning
+-   **Primary Use**: Code generation and completion tasks
 
 ## ✨ Key Features
 
-- **Bidirectional Context**: Diffusion modeling enables understanding of both past and future tokens
-- **Confidence-Guided Sampling**: Maintains competitive inference latency through intelligent sampling
-- **Lightweight Design**: Achieves strong performance with fewer parameters than comparable models
-- **Open Training Pipeline**: Fully reproducible training from pre-training to fine-tuning
+-   **Bidirectional Context**: Diffusion modeling enables understanding of both past and future tokens
+-   **Confidence-Guided Sampling**: Maintains competitive inference latency through intelligent sampling
+-   **Lightweight Design**: Achieves strong performance with fewer parameters than comparable models
+-   **Open Training Pipeline**: Fully reproducible training from pre-training to fine-tuning
 
 ## 📈 Performance
 
@@ -191,8 +194,6 @@ bash eval_mbpp_humaneval.sh
 ```
 ## 📚 Citation
 
-Technical report coming soon. For now, please cite:
-
 ```bibtex
 @misc{coda2025,
   title={CoDA: Coding LM via Diffusion Adaptation},
@@ -204,7 +205,7 @@ Technical report coming soon. For now, please cite:
 
 ## 🔗 Resources
 
-- 📄 **Technical Report**: [technical_report.pdf](https://github.com/SalesforceAIResearch/CoDA/blob/main/technical_report.pdf)
+- 📄 **Paper**: [huggingface.co/papers/2510.03270](https://huggingface.co/papers/2510.03270)
 - 💻 **Code Repository**: [github.com/SalesforceAIResearch/CoDA](https://github.com/SalesforceAIResearch/CoDA)
 - 🤗 **Model Hub**: [Salesforce CoDA collection](https://huggingface.co/collections/Salesforce/coda-68d627d87921c0e28a69e340)