Improve model card: Add pipeline tag, paper link, code link, and usage example

README.md

@@ -1,10 +1,37 @@
 ---
-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
+pipeline_tag: text-generation
 tags: []
 ---
 
-Built with Llama
+# Learning When to Stop: Adaptive Latent Reasoning via Reinforcement Learning
+
+This repository hosts a model presented in the paper "[Learning When to Stop: Adaptive Latent Reasoning via Reinforcement Learning](https://huggingface.co/papers/2511.21581)".
+
+Latent reasoning is a novel development in Transformer language models that compresses reasoning lengths by directly passing information-rich previous final latent states. This model implements an adaptive-length latent reasoning approach optimized via a post-SFT reinforcement-learning methodology. This optimization minimizes reasoning length while maintaining accuracy, demonstrating a 52% drop in total reasoning length with no penalty to accuracy on the Llama 3.2 1B model and the GSM8K-Aug dataset.
+
+-   **Paper**: [Learning When to Stop: Adaptive Latent Reasoning via Reinforcement Learning](https://huggingface.co/papers/2511.21581)
+-   **Code**: [https://github.com/apning/adaptive-latent-reasoning](https://github.com/apning/adaptive-latent-reasoning)
+
+## Sample Usage
+
+You can load this model and other trained weights using the `automodelforcausallm_from_pretrained_latent` function from `src.model_creation`, as demonstrated in the official GitHub repository:
+
+```python
+from transformers import AutoTokenizer
+# For full functionality, clone the official GitHub repo: https://github.com/apning/adaptive-latent-reasoning
+# and ensure 'src.model_creation' is in your Python path or adapt the import.
+from src.model_creation import automodelforcausallm_from_pretrained_latent
+
+repo_id = "Lapisbird/Llama-adaLR-model-latent-6" # Example repo_id from the paper's GitHub README
+
+model = automodelforcausallm_from_pretrained_latent(repo_id)
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+
+print(f"Model '{repo_id}' and tokenizer loaded successfully.")
+# You can now use 'model' and 'tokenizer' for inference as described in the paper.
+```