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

README.md

@@ -1,10 +1,40 @@
 ---
-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 repository contains the weights for **Adaptive Latent Reasoning models**, as introduced in the paper [Learning When to Stop: Adaptive Latent Reasoning via Reinforcement Learning](https://huggingface.co/papers/2511.21581).
+
+Latent reasoning represents a new development in Transformer language models that has shown potential in compressing reasoning lengths compared to chain-of-thought reasoning. This work develops adaptive-length latent reasoning models and introduces a post-SFT reinforcement-learning methodology to optimize latent reasoning length by minimizing reasoning length while maintaining accuracy. Experiments on the Llama 3.2 1B model and the GSM8K-Aug dataset showed a 52% drop in total reasoning length with no penalty to accuracy.
+
+The official code and pretrained weights are available at the GitHub repository: https://github.com/apning/adaptive-latent-reasoning
+
+## Usage
+
+All weights used for results in the paper are available on Hugging Face. You can load these models using the function `automodelforcausallm_from_pretrained_latent` from `src.model_creation`.
+
+First, set up your environment by cloning the repository and installing dependencies:
+```bash
+git clone https://github.com/apning/adaptive-latent-reasoning.git
+cd adaptive-latent-reasoning
+conda env create -f environment.yml && conda activate adaptive-latent-reasoning
+```
+
+Then, you can load a model like this:
+```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)
+```
+For more detailed instructions on replication, training, and evaluation, please refer to the [official GitHub repository](https://github.com/apning/adaptive-latent-reasoning).