Update README.md

README.md

@@ -11,8 +11,6 @@ base_model:
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-
-
 ## Model Details
 
 ### Model Description
@@ -33,6 +31,57 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 - **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