---
library_name: transformers
tags:
- paper-summarization
- lora
- peft
- llama
license: mit
datasets:
- armanc/scientific_papers
language:
- en
metrics:
- rouge
base_model:
- meta-llama/Llama-3.2-1B-Instruct
pipeline_tag: summarization
---

# **Llama-PaperSummarization-LoRA**

## **Model Details**
This is a **LoRA fine-tuned adapter** built on [**meta-llama/Llama-3.2-1B-Instruct**](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct). It is designed for scientific paper summarization tasks and leverages **Low-Rank Adaptation (LoRA)** to enhance model performance efficiently while maintaining a low computational overhead.


### **Performance comparison**
| Model                     | ROUGE-1 | ROUGE-2 | ROUGE-3 | ROUGE-L |
|---------------------------|----------|----------|----------|----------|
| **Llama-3.2-1B-Instruct** | 36.69    | 7.47     | 1.95     | 19.36    |
| **Llama-PaperSummarization-LoRA** | **41.56** | **11.31** | **2.67** | **21.86** |

The model was evaluated on a **6K-sample test set** using **ROUGE scores** with beam search (beam size = 4).


### **How to load**
```python
from transformers import LlamaForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B-Instruct")
peft_model_id = "gabe-zhang/Llama-PaperSummarization-LoRA"
model = PeftModel.from_pretrained(base_model, peft_model_id)
model.merge_and_unload()
```


## **Dataset**
The model was fine-tuned on the [**armanc/scientific_papers**](https://huggingface.co/datasets/armanc/scientific_papers) dataset. Below are the details of the dataset splits:
- **Training Set**: 20K samples
- **Validation Set**: 6K samples
- **Test Set**: 6K samples


## **LoRA Configuration**
- **Trainable Parameters**: 850K (~7% of base model parameters)
- **Context Length**: 10K tokens
- **Rank**: 8
- **Target Modules**: Query and Value matrices
- **Optimization Settings**:  
  - Gradient Accumulation: 4 steps  
  - Training Steps: 5K 

### **Training Setup**
- **Hardware**: NVIDIA RTX A6000 GPU
- **Evaluation Frequency**: Every 20 steps  
- **Training Duration**: 28 hours
- **Training Scripts**: [gabe-zhang/paper2summary](https://github.com/gabe-zhang/paper2summary)


## **License**
This repository contains a **LoRA fine-tuned adapter** derived from the Llama 3.2 model.  
- **Llama 3.2 Materials**: Governed by the [Llama 3.2 Community License](./LICENSE_Llama3.2).  
- **All other content**: Licensed under the [MIT License](./LICENSE).  

### **Attribution**
- The model prominently incorporates Llama 3.2 as its base.  
- "Built with Llama" is displayed as required by the Llama 3.2 Community License.