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qwen2

Update model card with metadata and links

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by nielsr HF Staff - opened
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  1. README.md +10 -5
README.md CHANGED
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  ---
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- license: mit
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  datasets:
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  - HuggingFaceTB/smollm-corpus
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  language:
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  - en
 
 
 
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  ---
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  # Raw 1B Shared
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  ## How to Get Started with the Model
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  Use the code below to get started with the model.
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  ## Citation
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- ```
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  @article{yamaguchi2026enhancinglinguisticcompetencelanguage,
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  title={Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks},
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  author={Atsuki Yamaguchi and Maggie Mi and Nikolaos Aletras},
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  journal={arXiv},
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  volume={abs/2601.03448}
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  }
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- ```
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-
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-
 
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  ---
 
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  datasets:
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  - HuggingFaceTB/smollm-corpus
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  language:
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  - en
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+ license: mit
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+ library_name: transformers
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+ pipeline_tag: text-generation
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  ---
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  # Raw 1B Shared
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+ This model is a 1B parameter language model pre-trained as a baseline for the research presented in the paper [Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks](https://huggingface.co/papers/2601.03448).
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+ L2T (Language Learning Tasks) is a pre-training framework that integrates structured linguistic tasks alongside standard next-token prediction to explicitly optimize for linguistic competence in Large Language Models (LLMs). This specific checkpoint is the baseline model trained on raw text.
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+
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+ - **Paper:** [Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks](https://huggingface.co/papers/2601.03448)
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+ - **Repository:** [gucci-j/l2t](https://github.com/gucci-j/l2t)
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  ## How to Get Started with the Model
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  Use the code below to get started with the model.
 
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  ## Citation
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+ ```bibtex
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  @article{yamaguchi2026enhancinglinguisticcompetencelanguage,
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  title={Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks},
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  author={Atsuki Yamaguchi and Maggie Mi and Nikolaos Aletras},
 
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  journal={arXiv},
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  volume={abs/2601.03448}
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  }
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+ ```