Update README.md

README.md

@@ -4,9 +4,11 @@ license: apache-2.0
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 # LoNAS Adapter Card: lonas-llama-7b-commonsense-adapter
-
 The super-adapter-network fine-tuned on LLaMA-7B with some commonsense reasoning datasets using LoNAS.
 
+## Paper Abstract
+Recently, several approaches successfully demonstrated that weight-sharing Neural Architecture Search (NAS) can effectively explore a search space of elastic low-rank adapters (LoRA), allowing the parameter-efficient fine-tuning (PEFT) and compression of large language models. In this paper, we introduce a novel approach called Shears, demonstrating how the integration of cost-effective sparsity and a proposed Neural Low-rank adapter Search (NLS) algorithm can further improve the efficiency of PEFT approaches. Results demonstrate the benefits of Shears compared to other methods, reaching high sparsity levels while improving or with little drop in accuracy, utilizing a single GPU for a pair of hours.
+
 ## Model Details
 
 ### Information
@@ -64,7 +66,7 @@ Results of the heuristic sub-network discoverd from the super-network:
 ## Model Sources
 
 - **Repository:** [https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/LoNAS](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/LoNAS)
-- **Paper:** [LoNAS: Elastic Low-Rank Adapters for Efficient Large Language Models]()
+- **Paper:** [LoNAS: Elastic Low-Rank Adapters for Efficient Large Language Models](https://arxiv.org/abs/2404.10934)
 
 ## Ethical Considerations