Update README.md

README.md

@@ -3,15 +3,21 @@ language: en
 license: apache-2.0
 ---
 
-# Shears Model Card: shears-llama-13b-50-math-heuristic-adapter
+# Shears Adapter Card: shears-llama-13b-50-math-heuristic-adapter
 
 The heuristic adapter discovered from the [super-adapter](https://huggingface.co/IntelLabs/shears-llama-13b-50-math-super-adapter) fine-tuned on sparsified LLaMA-13B with some math reasoning datasets using Shears.
 
+## 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
 
+### Note
+Please note, we only provide the model adapter and do not provide a copy of the base [yahma/llama-13b-hf](https://huggingface.co/yahma/llama-13b-hf) model or its sparsified one. Any use of this adapter requires a separate download of the base model and follow [this instruction](#sparsified-base-model) to sparse the base model.
+
 ### Information
 
-- **Model name:** shears-llama-13b-50-math-heuristic-adapter
+- **Adapter name:** shears-llama-13b-50-math-heuristic-adapter
 - **Base model:** Sparsified [LLaMA-13B](https://huggingface.co/yahma/llama-13b-hf)
 - **Sparsity:** 50%
 - **Domain:** Math
@@ -130,13 +136,25 @@ print(output)
 - **Repository:** [https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/Shears](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/Shears)
 - **Paper:** [Shears: Unstructured Sparsity with Neural Low-rank Adapter Search](https://arxiv.org/abs/2404.10934)
 
+## Ethical Considerations
+
+Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See [Intel’s Global Human Rights Principles](https://www.intel.com/content/dam/www/central-libraries/us/en/documents/policy-human-rights.pdf). Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.
+
+| Ethical Considerations | Description | 
+| ----------- | ----------- | 
+| Data | The adapter was trained using the commonsense_15k data mixture as described above. |
+| Human life | The model is not intended to inform decisions central to human life or flourishing. | 
+| Mitigations | No additional risk mitigation strategies were considered during model development. |
+| Risks and harms | This model has not been assessed for harm or biases, and should not be used for sensitive applications where it may cause harm. |
+| Use cases | - | 
+
 ## Citation
 
 ```bash
-@article{munoz2024shears,
+@inproceedings{munoz2024shears,
   title = {Shears: Unstructured Sparsity with Neural Low-rank Adapter Search},
   author={J. Pablo Munoz and Jinjie Yuan and Nilesh Jain},
-  journal={The 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-2024)},
+  booktitle={The 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-2024)},
   year={2024}
 }
 ```