nielsr HF Staff commited on
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1 Parent(s): f3f4077

Update model card metadata and add Hugging Face paper link

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This PR makes the following improvements to the model card:

- Removes the `arxiv:2610.18877` tag from the YAML metadata, because arXiv IDs should only be referenced in the Markdown content.
- Changes the `pipeline_tag` to `text-generation`, since LOTUS is a language model that outputs textual answers and rationales.
- Adds a link to the Hugging Face paper page next to the existing arXiv link.
- Keeps the existing informative content, usage examples, and evaluation results unchanged.

Files changed (1) hide show
  1. README.md +7 -8
README.md CHANGED
@@ -1,16 +1,15 @@
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  ---
 
 
 
 
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  license: mit
 
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  tags:
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  - time-series
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  - time-series-language-model
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  - forecast-grounding
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  - lora
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- - arxiv:2610.18877
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- base_model:
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- - google/gemma-3-270m
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- - google/gemma-3-1b-pt
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- - Qwen/Qwen3-0.6B
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- pipeline_tag: time-series-forecasting
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  ---
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  # LOTUS: Learning to Understand Time-series by Learning to Forecast
@@ -37,7 +36,7 @@ best average performance in every matched-backbone group with minimal trainable
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  baseline by **3.36%** — and its 270M-parameter model is the top system on the TSRBench zero-shot
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  reasoning benchmark ahead of backbones 25–50× larger.
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- Paper: [arXiv:2610.18877](https://arxiv.org/abs/2610.18877) · Code: [DA2I2-SLM/Lotus](https://github.com/DA2I2-SLM/Lotus)
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  · Phase-1 (pretraining) checkpoints: [hiepnh137/LangTimeFoundationModel](https://huggingface.co/hiepnh137/LangTimeFoundationModel)
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  ## Training
@@ -228,4 +227,4 @@ split, printing accuracy next to the paper's number together with the model's ve
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  journal = {arXiv preprint arXiv:2610.18877},
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  year = {2026}
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  }
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- ```
 
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  ---
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+ base_model:
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+ - google/gemma-3-270m
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+ - google/gemma-3-1b-pt
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+ - Qwen/Qwen3-0.6B
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  license: mit
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+ pipeline_tag: text-generation
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  tags:
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  - time-series
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  - time-series-language-model
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  - forecast-grounding
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  - lora
 
 
 
 
 
 
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  ---
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  # LOTUS: Learning to Understand Time-series by Learning to Forecast
 
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  baseline by **3.36%** — and its 270M-parameter model is the top system on the TSRBench zero-shot
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  reasoning benchmark ahead of backbones 25–50× larger.
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+ Paper: [arXiv:2610.18877](https://arxiv.org/abs/2610.18877) · [Hugging Face Paper page](https://huggingface.co/papers/2610.18877) · Code: [DA2I2-SLM/Lotus](https://github.com/DA2I2-SLM/Lotus)
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  · Phase-1 (pretraining) checkpoints: [hiepnh137/LangTimeFoundationModel](https://huggingface.co/hiepnh137/LangTimeFoundationModel)
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  ## Training
 
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  journal = {arXiv preprint arXiv:2610.18877},
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  year = {2026}
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  }
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+ ```