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README.md
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---
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library_name: transformers
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tags:
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---
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#
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/644a900e3a619fe72b14af0f/OU4Y3s-WckYKMN4fQkNiS.png)
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- **Model type:** 7B
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- **Language(s) (NLP):** English
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- **License:** MIT
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- **Finetuned from model:** Check Saul-7B-Instruct
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### Model Sources
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import torch
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from transformers import pipeline
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pipe = pipeline("text-generation", model="Equall/Saul-
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# We use the tokenizer’s chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
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messages = [
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{"role": "user", "content": "[YOUR QUERY GOES HERE]"},
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## Citation
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```bibtex
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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---
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library_name: transformers
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tags:
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- legal
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license: mit
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language:
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- en
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# Equall/Saul-Base-v1
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This is the base model for Equall/Saul-Base, a large instruct language model tailored for Legal domain. This model is obtained by continue pretraining of Mistral-7B.
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/644a900e3a619fe72b14af0f/OU4Y3s-WckYKMN4fQkNiS.png)
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- **Model type:** 7B
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- **Language(s) (NLP):** English
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- **License:** MIT
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### Model Sources
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import torch
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from transformers import pipeline
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pipe = pipeline("text-generation", model="Equall/Saul-Instruct-v1", torch_dtype=torch.bfloat16, device_map="auto")
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# We use the tokenizer’s chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
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messages = [
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{"role": "user", "content": "[YOUR QUERY GOES HERE]"},
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## Citation
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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```bibtex
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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