Improve model card: Add pipeline tag, paper link, GitHub link, and sample usage

README.md

@@ -1,10 +1,32 @@
 ---
-library_name: transformers
-license: llama3.2
 base_model: meta-llama/Llama-3.2-1B-Instruct
 datasets:
 - whynlp/gsm8k-aug
+library_name: transformers
+license: llama3.2
 tags: []
+pipeline_tag: text-generation
 ---
 
-Built with Llama
+# Learning When to Stop: Adaptive Latent Reasoning via Reinforcement Learning
+
+This model introduces adaptive-length latent reasoning, a novel approach that uses a post-SFT reinforcement-learning methodology to optimize reasoning length while maintaining accuracy. It is presented in the paper: [Learning When to Stop: Adaptive Latent Reasoning via Reinforcement Learning](https://huggingface.co/papers/2511.21581).
+
+The official PyTorch implementation and training scripts are available on GitHub: https://github.com/apning/adaptive-latent-reasoning.
+
+## Sample Usage
+
+You can load these models using the function `automodelforcausallm_from_pretrained_latent` from `src.model_creation` as shown below.
+
+```python
+from transformers import AutoTokenizer
+from src.model_creation import automodelforcausallm_from_pretrained_latent
+
+repo_id = "Lapisbird/Llama-adaLR-model-latent-6" # Example model from the paper
+
+model = automodelforcausallm_from_pretrained_latent(repo_id)
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+
+# Example usage with the loaded model (you would typically use this for text generation)
+# For full inference examples, refer to the GitHub repository.
+```