---
library_name: transformers
license: cc-by-4.0
language:
- en
base_model:
- Equall/Saul-7B-Base
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** Ehsan Shareghi, Jiuzhou Han, Paul Burgess
- **Model type:** 7B
- **Language(s) (NLP):** English
- **License:** CC BY 4.0
- **Finetuned from model:** Saul-7B-Base

### Model Sources

<!-- Provide the basic links for the model. -->

- **Paper:** https://arxiv.org/pdf/2412.06272

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
Here's how you can run the model:

```python
# pip install git+https://github.com/huggingface/transformers.git
# pip install git+https://github.com/huggingface/peft.git

import torch
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig
)
from peft import PeftModel

model = AutoModelForCausalLM.from_pretrained(
    "Equall/Saul-7B-Base",
    quantization_config=BitsAndBytesConfig(load_in_8bit=True),
    device_map="auto",
)

tokenizer = AutoTokenizer.from_pretrained("Equall/Saul-7B-Base")
tokenizer.pad_token = tokenizer.eos_token

model = PeftModel.from_pretrained(
            model,
            "auslawbench/Cite-SaulLM-7B",
            device_map="auto",
            torch_dtype=torch.bfloat16,
        )
model.eval()

fine_tuned_prompt = """
### Instruction:
{}

### Input:
{}

### Response:
{}"""

model_input = fine_tuned_prompt.format("Predict the name of the case that needs to be cited in the text and explain why it should be cited.", input, '')
inputs = tokenizer(model_input, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256, temperature=1.0)
output = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(output.split("### Response:")[1].strip().split('>')[0] + '>')

```

## Citation

**BibTeX:**

```
@misc{shareghi2024auslawcite,
      title={Methods for Legal Citation Prediction in the Age of LLMs: An Australian Law Case Study}, 
      author={Ehsan Shareghi, Jiuzhou Han, Paul Burgess},
      year={2024},
      eprint={arXiv:2412.06272},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```