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README.md

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 license: apache-2.0
+language:
+- en
 ---
+
+# 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 LLaMA-7B model on the [MALLS](https://huggingface.co/datasets/yuan-yang/MALLS-v0) dataset.
+
+**Model type:**
+This repo contains the LoRA delta weights for naive correction LogicLLaMA, which, given a pair of the NL statement and a predicted FOL rule, 
+corrects the potential errors in the predicted FOL rule.
+This is used as a downstream model together with ChatGPT, where ChatGPT does the "heavy lifting" by predicting the initial translated FOL rule 
+and then LogicLLaMA refines the rule by correcting potential errors.
+In our [experiments](https://arxiv.org/abs/2305.15541), this mode yields better performance than ChatGPT and direction translation LogicLLaMA.
+
+We also provide the delta weights for other modes:
+- [direct translation LogicLLaMA ](https://huggingface.co/yuan-yang/LogicLLaMA-7b-direct-translate-delta-v0)
+
+**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}
+}
+```