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README.md

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 license: apache-2.0
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+
+# LogicLLaMA Model Card
+
+## Model details
+
+LogicLLaMA is a language model that translates natural-language (NL) statements into first-order logic (FOL) rules.
+It is trained by fine-tuning the LLaMA2-13B model on the [MALLS-v0.1](https://huggingface.co/datasets/yuan-yang/MALLS-v0) dataset.
+
+**Model type:**
+This repo contains the LoRA delta weights for direct translation LogicLLaMA, which directly translates the NL statement into a FOL rule in one go.
+We also provide the delta weights for other modes:
+- [direct translation LogicLLaMA-7B](https://huggingface.co/yuan-yang/LogicLLaMA-7b-direct-translate-delta-v0.1)
+- [naive correction LogicLLaMA-7B](https://huggingface.co/yuan-yang/LogicLLaMA-7b-naive-correction-delta-v0.1)
+- [direct translation LogicLLaMA-13B](https://huggingface.co/yuan-yang/LogicLLaMA-13b-direct-translate-delta-v0.1)
+- [naive correction LogicLLaMA-13B](https://huggingface.co/yuan-yang/LogicLLaMA-13b-naive-correction-delta-v0.1)
+
+**License:**
+Apache License 2.0
+
+## Using the model
+
+Check out how to use the model on our project page:  https://github.com/gblackout/LogicLLaMA
+
+
+**Primary intended uses:**
+LogicLLaMA is intended to be used for research.
+
+
+## Citation
+
+```
+@article{yang2023harnessing,
+      title={Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation}, 
+      author={Yuan Yang and Siheng Xiong and Ali Payani and Ehsan Shareghi and Faramarz Fekri},
+      journal={arXiv preprint arXiv:2305.15541},
+      year={2023}
+}
+```