---
language: en
license: apache-2.0
library_name: peft
---

# Shears Adapter Card: shears-mpt-7b-50-gsm8k-super-adapter

The super-adapter-network fine-tuned on sparsified [MPT-7B](https://huggingface.co/mosaicml/mpt-7b) with GSM8K datasets using Shears.

The release of the super-network is to facilitate users to apply their own search algorithms and evaluation indicators to extract subnetworks suitable for their specific needs.

## 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

- **Adapter name:** shears-mpt-7b-50-gsm8k-super-adapter
- **Base model:** [IntelLabs/shears-mpt-7b-50-base](https://huggingface.co/IntelLabs/shears-mpt-7b-50-base)
- **Sparsity:** 50%
- **Subnetwork version:** Super
- **NNCF Configuration:** [nncf_shears_mpt.json](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/blob/main/Shears/nncf_config/nncf_shears_mpt.json)

### Adapter Configuration

- **LoRA rank:** 32
- **LoRA alpha:** 64
- **LoRA target modules:** q_proj, k_proj, v_proj, out_proj, up_proj, down_proj
- **LoRA rank search space:** [32, 24, 16] (for each LoRA module)

### Training Hyperparameters

- **Batch size:** 16
- **Learning rate:** 3e-4
- **Epoch:** 5

### Training and Evaluation

GSM8K dataset: [https://huggingface.co/datasets/gsm8k](https://huggingface.co/datasets/gsm8k)

## How to use

Refer to the illustrative example provided in [load_and_explore_supernet.ipynb](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/Shears/search/load_and_explore_supernet.ipynb) for a comprehensive understanding. This notebook shows the direct loading of a Shears super-network and the extraction of diverse subnetworks from it. 
This feature empowers users to employ their own search algorithms and evaluation metrics for the extraction of subnetworks customized to their specific requirements.

Moreover, the super-network is essentially the maximal subnetwork, and it can also be directly loaded:

```python
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM
from transformers import AutoTokenizer

def generate_prompt(instruction):
    return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request. 

                    ### Instruction:
                    {instruction}

                    ### Response:
                    """

base_model = AutoModelForCausalLM.from_pretrained("IntelLabs/shears-mpt-7b-50-base", trust_remote_code=True)
model = PeftModel.from_pretrained(base_model, "IntelLabs/shears-mpt-7b-50-gsm8k-super-adapter")
model.eval()

non_zero_params = sum([(param.data != 0).sum().item() for _, param in model.named_parameters()])
print(f"Number of all non-zero parameters: {non_zero_params}")

tokenizer = AutoTokenizer.from_pretrained("IntelLabs/shears-mpt-7b-50-base", trust_remote_code=True)

instruction = "Edgar eats 18 pretzels a day. If his brother eats 1/2 as many, how many does his brother eat in a week?"
prompt = generate_prompt(instruction)
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to(model.device)
with torch.no_grad():
    generation_output = model.generate(
        input_ids=input_ids,
        return_dict_in_generate=True,
        output_scores=True,
        max_new_tokens=256,
        use_cache=True,
        num_beams=4,
    )
    s = generation_output.sequences[0]
    output = tokenizer.decode(s)
print(output)

```

## Evaluation Results

Results of the heuristic sub-network discoverd from the super-network:

| Model                 | Sparsity    | GSM8K Accuracy |
|-----------------------|-------------|-------|
| [**MPT-7B-Shears**](https://huggingface.co/IntelLabs/shears-mpt-7b-50-gsm8k-heuristic-adapter)   | **50%**     | 33.4  |

## Model Sources

- **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 GSM8K dataset 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
@inproceedings{munoz2024shears,
  title = {Shears: Unstructured Sparsity with Neural Low-rank Adapter Search},
  author={J. Pablo Munoz and Jinjie Yuan and Nilesh Jain},
  booktitle={The 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-2024)},
  year={2024}
}
```

## License

Apache-2.0