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README.md
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SaulLM-54B-Instruct is a state-of-the-art language model specifically designed for legal professionals. Developed through a collaboration between Legal Equall.ai and MICS at CentraleSupélec (Université Paris-Saclay), SaulLM-141B aims to revolutionize how legal data is processed and analyzed, enhancing the efficiency and accuracy of legal professionals worldwide.
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**
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SaulLM-54B-Instruct is intended for use in various legal contexts.
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While SaulLM-54B-Instruct is designed to be robust across multiple European legal systems, it may not perform as well on legal systems outside of its training scope, particularly non-European jurisdictions.
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Despite efforts to mitigate bias, SaulLM-141B may still exhibit biases inherent in its training data. Users should be cautious and critically evaluate the model's outputs, especially in sensitive legal cases.
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Users are encouraged to use SaulLM-141B responsibly, ensuring that its application does not infringe on privacy rights or propagate unfair decisions.
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SaulLM-54B-Instruct was trained on a rich dataset comprising European legal texts, court rulings, and legislative documents, ensuring a deep understanding of the legal landscape within the EU.
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```
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@misc{saul_llm_2024,
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title={SaulLM-
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author={Legal Equall.ai and MICS CentraleSupélec},
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year={2024},
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eprint={2404.12345},
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# Model Card for SaulLM-54B-Instruct
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<p align="center">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/644a900e3a619fe72b14af0f/tD6ZJIlh5pxR1iqcxtOSs.jpeg" alt="image/jpeg">
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</p>
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**Note:** This model is a research artifact and should be considered as such.
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## Model Details
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### Model Description
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**SaulLM-54B-Instruct** is a state-of-the-art language model specifically designed for the legal domain. It was developed through a collaboration between Equall and MICS at CentraleSupélec (Université Paris-Saclay) and aims to contribute to the advancement of LLMs specialized for legal work.
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- **Developed by:** Equall and MICS of CentraleSupélec (Université Paris-Saclay)
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- **Model type:** A 54 billion parameter model pretrained and finetuned for legal tasks, leveraging data from US and European legal databases.
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- **Language(s) (NLP):** English
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- **License:** MIT-License
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- **Finetuned from model:** Base model developed by Equall relying on continuous pretraining of Mixtral’s models.
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## Intended Uses & Limitations
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### Intended Uses
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SaulLM-54B-Instruct is intended to support further research and be adapted for various legal use cases.
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### Limitations
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The information provided by the model is for informational purposes only and should not be interpreted as legal advice. Also, because SaulLM-54B-Instruct was trained with a focus on US and European legal systems, it may not perform as well on legal systems outside of those jurisdictions.
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## Bias, Risks, and Ethical Considerations
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### Bias and Risks
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Despite efforts to mitigate bias, SaulLM-54B may still exhibit biases inherent in its training data or otherwise provide inaccurate responses. The model is trained on information up to a certain point in time, and the model cannot account for all recent legal developments. Users should be cautious and critically evaluate the model's outputs, especially in sensitive legal cases. The responsibility for making decisions based on the information rests with the user, not the model or its developers. Users are encouraged to seek the assistance of qualified legal professionals where legal advice is needed.
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### Ethical Considerations
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Users must use SaulLM-54B responsibly, ensuring that the model is not misused in a way that violates the law or infringes on the rights of others. Among other things, the model may not be used to generate harmful content, spread misinformation, or violate privacy or intellectual property rights.
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## Technical Details
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### Training Data
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SaulLM-54B was trained on a rich dataset comprising European and US legal texts, court rulings, and legislative documents.
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## Citation
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To reference SaulLM-54B in your work, please cite this model card.
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```
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@misc{saul_llm_2024,
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title={SaulLM-54B: A Specialized Large Language Model for European Legal Tasks},
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author={Legal Equall.ai and MICS CentraleSupélec},
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year={2024},
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eprint={2404.12345},
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