Improve model card with description, links, and sample usage

README.md

@@ -1,10 +1,31 @@
 ---
-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
+# Llama-adaLR-model-latent-6: Learning When to Stop: Adaptive Latent Reasoning via Reinforcement Learning
+
+This repository contains the `Llama-adaLR-model-latent-6` model, presented in the paper "[Learning When to Stop: Adaptive Latent Reasoning via Reinforcement Learning](https://huggingface.co/papers/2511.21581)".
+
+This model introduces adaptive-length latent reasoning, a novel approach to optimizing the reasoning length of Transformer language models. By leveraging a post-SFT reinforcement-learning methodology, it aims to minimize reasoning length while maintaining accuracy. Experiments on the Llama 3.2 1B model and the GSM8K-Aug dataset demonstrated a 52% drop in total reasoning length without sacrificing accuracy.
+
+For more details, including additional model weights and ongoing developments, please refer to the official [GitHub repository](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` with the `transformers` library, as shown in the following example found in the GitHub repository:
+
+```python
+from transformers import AutoTokenizer
+from src.model_creation import automodelforcausallm_from_pretrained_latent
+
+repo_id = "Lapisbird/Llama-adaLR-model-latent-6"
+
+model = automodelforcausallm_from_pretrained_latent(repo_id)
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+```