Instructions to use Lapisbird/Llama-adaLR-appendix-model-codi_intermediate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lapisbird/Llama-adaLR-appendix-model-codi_intermediate with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Lapisbird/Llama-adaLR-appendix-model-codi_intermediate") model = AutoModel.from_pretrained("Lapisbird/Llama-adaLR-appendix-model-codi_intermediate", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Improve model card: Add pipeline tag, paper link, code link, and usage example
Verified signatureThis PR significantly enhances the model card by: - Adding the `pipeline_tag: text-generation` to the metadata, which improves model discoverability on the Hugging Face Hub. - Including a concise description of the model and its purpose, based on the paper's abstract. - Linking directly to the research 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 directly from the GitHub README, demonstrating how to load the model with the `transformers` library. These changes provide users with more comprehensive information for understanding and utilizing the model.21ce869
-
Create NOTICE
Verified signature6c4de21 -
Create LICENSE
Verified signature76a8459 -
Update README.md
Verified signatureecd5ee4 -
Upload tokenizer
Verified signature914d55c -
Upload model
Verified signaturee2a8ecc -
initial commit
Verified signature4d557af