Add pipeline tag and paper/code links

README.md

@@ -1,9 +1,11 @@
 ---
-library_name: transformers
-license: mit
 base_model:
 - meta-llama/Llama-3.2-1B-Instruct
+library_name: transformers
+license: mit
+pipeline_tag: text-generation
 ---
+
 # CoLaR Model
 
 <div align="center">
@@ -14,7 +16,10 @@ base_model:
 
 ## Overview
 
-**CoLaR** (Compressed Latent Reasoning) is a latent reasoning model based on LLaMA that uses a specialized LatentHead module for generating continuous latent representations. This model is part of the [Parallel Test-Time Scaling for Latent Reasoning Models](https://arxiv.org/abs/2510.07745) framework.
+**CoLaR** (Compressed Latent Reasoning) is a latent reasoning model based on LLaMA that uses a specialized LatentHead module for generating continuous latent representations. This model is part of the [Parallel Test-Time Scaling for Latent Reasoning Models](https://huggingface.co/papers/2510.07745) framework.
+
+- **Paper:** [Parallel Test-Time Scaling for Latent Reasoning Models](https://huggingface.co/papers/2510.07745)
+- **Code:** [https://github.com/ModalityDance/LatentTTS](https://github.com/ModalityDance/LatentTTS)
 
 ## Model Details
 
@@ -201,7 +206,7 @@ Run evaluation using the provided scripts:
 
 ## Model Card
 
-- **Paper**: [Parallel Test-Time Scaling for Latent Reasoning Models](https://arxiv.org/abs/2510.07745)
+- **Paper**: [Parallel Test-Time Scaling for Latent Reasoning Models](https://huggingface.co/papers/2510.07745)
 - **HuggingFace**: [ModalityDance/latent-tts-colar](https://huggingface.co/ModalityDance/latent-tts-colar)
 - **Benchmarks**: GSM8K Test, GSM8K Hard, MultiArith
 
@@ -235,4 +240,4 @@ If you use this model, please cite:
       primaryClass={cs.CL},
       url={https://arxiv.org/abs/2505.16552}, 
 }
-```
+```