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

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This PR enhances the model card by:
- Adding the `pipeline_tag: text-generation` to improve discoverability on the Hugging Face Hub.
- Including a direct link to the paper [Learning When to Stop: Adaptive Latent Reasoning via Reinforcement Learning](https://huggingface.co/papers/2511.21581).
- Providing a link to the official GitHub repository: https://github.com/apning/adaptive-latent-reasoning.
- Adding a sample usage code snippet from the GitHub README to guide users on how to load and use the model.

Please review and merge this PR if everything looks good.

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  1. README.md +25 -3
README.md CHANGED
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  ---
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- library_name: transformers
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- license: llama3.2
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  base_model: meta-llama/Llama-3.2-1B-Instruct
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  datasets:
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  - whynlp/gsm8k-aug
 
 
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  tags: []
 
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  ---
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- Built with Llama
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
 
 
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  base_model: meta-llama/Llama-3.2-1B-Instruct
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  datasets:
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  - whynlp/gsm8k-aug
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+ library_name: transformers
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+ license: llama3.2
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  tags: []
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+ pipeline_tag: text-generation
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  ---
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+ # Learning When to Stop: Adaptive Latent Reasoning via Reinforcement Learning
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+
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+ 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).
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+ The official PyTorch implementation and training scripts are available on GitHub: https://github.com/apning/adaptive-latent-reasoning.
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+
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+ ## Sample Usage
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+ You can load these models using the function `automodelforcausallm_from_pretrained_latent` from `src.model_creation` as shown below.
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+ ```python
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+ from transformers import AutoTokenizer
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+ from src.model_creation import automodelforcausallm_from_pretrained_latent
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+
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+ repo_id = "Lapisbird/Llama-adaLR-model-latent-6" # Example model from the paper
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+
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+ model = automodelforcausallm_from_pretrained_latent(repo_id)
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+ tokenizer = AutoTokenizer.from_pretrained(repo_id)
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+
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+ # Example usage with the loaded model (you would typically use this for text generation)
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+ # For full inference examples, refer to the GitHub repository.
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+ ```