Update README.md

README.md

@@ -9,13 +9,13 @@ tags: []
 <!-- Provide a longer summary of what this model is/does. -->
 LoRA adapter weights from fine-tuning [Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the MIMIC-III mortality prediction task. The [PEFT](https://github.com/huggingface/peft) was used and the model was trained for a maximum of 5 epochs with early stopping, full details can be found at the [github repo](https://github.com/nlpie-research/efficient-ml). 
 
-- **Developed by:** Niall Taylor
+<!-- - **Developed by:** Niall Taylor -->
 <!-- - **Shared by [Optional]:** More information needed -->
 - **Model type:** Language model LoRA adapter
 - **Language(s) (NLP):** en
 - **License:** apache-2.0
 - **Parent Model:** Llama-2-7b-hf
-- **Resources for more information:** More information needed
+- **Resources for more information:** 
     - [GitHub Repo](https://github.com/nlpie-research/efficient-ml)
     - [Associated Paper](https://arxiv.org/abs/2402.10597)
 
@@ -37,7 +37,37 @@ LoRA adapter weights from fine-tuning [Llama-2-7b-hf](https://huggingface.co/met
 <!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
  
 
+# How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+<details>
+<summary> Click to expand </summary>
+
+
+```python
+from peft import AutoPeftModelForCausalLM, AutoPeftModelForSequenceClassification
+from transformers import AutoTokenizer
+
+model_name = "NTaylor/Llama-2-7b-hf-mimic-mp-lora"
+
+# load using AutoPeftModelForSequenceClassification
+model = AutoPeftModelForSequenceClassification.from_pretrained(lora_id)
+
+# use base llama tokenizer
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
 
+# example input
+text = "82 year old patient initially presented with severe chest pain and shortness of breath. They have a history of heart attacks, and there has been a struggle to bring the heart into a normal rythym ."
+inputs = tokenizer(text, return_tensors="pt")
+outputs = reloaded_model(**inputs)
+# extract prediction from outputs based on argmax of logits
+pred = torch.argmax(outputs.logits, axis = -1)
+print(f"Prediction is: {pred}") # Prediction is: tensor([1])
+```
+
+
+</details>
 
 ## Out-of-Scope Use
 
@@ -187,35 +217,5 @@ More information needed -->
 
 <!-- More information needed -->
 
-# How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-<details>
-<summary> Click to expand </summary>
-
-
-```python
-from peft import AutoPeftModelForCausalLM, AutoPeftModelForSequenceClassification
-from transformers import AutoTokenizer
-
-model_name = "NTaylor/Llama-2-7b-hf-mimic-mp-lora"
-
-# load using AutoPeftModelForSequenceClassification
-model = AutoPeftModelForSequenceClassification.from_pretrained(lora_id)
-
-# use base llama tokenizer
-tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
 
-# example input
-text = "82 year old patient initially presented with severe chest pain and shortness of breath. They have a history of heart attacks, and there has been a struggle to bring the heart into a normal rythym ."
-inputs = tokenizer(text, return_tensors="pt")
-outputs = reloaded_model(**inputs)
-# extract prediction from outputs based on argmax of logits
-pred = torch.argmax(outputs.logits, axis = -1)
-print(f"Prediction is: {pred}") # Prediction is: tensor([1])
-```
-
-
-</details>