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IntelLabs/
shears-mpt-7b-50-gsm8k-heuristic-adapter

Name: shears-mpt-7b-50-gsm8k-heuristic-adapter
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Shears Adapter Card: shears-mpt-7b-50-gsm8k-heuristic-adapter

The heuristic adapter discovered from the super-adapter fine-tuned on sparsified MPT-7B with GSM8K 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

Information

Adapter name: shears-mpt-7b-50-gsm8k-heuristic-adapter
Base model: IntelLabs/shears-mpt-7b-50-base
Sparsity: 50%
Subnetwork version: Heuristic
NNCF Configuration: nncf_shears_mpt.json

Adapter Configuration

LoRA rank: 32 (24 in the heuristic subnetwork)
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

How to use

Use our modified PEFT library (apply patch):

bash

git clone https://github.com/huggingface/peft.git
cd peft && git checkout v0.5.0 && git apply --ignore-space-change --ignore-whitespace peft-modifications-for-shears-inference-usage.patch && pip install -e . && cd ..

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-heuristic-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

Model	Sparsity	GSM8K Accuracy
MPT-7B-Shears	50%	33.4

Model Sources

Repository: https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/Shears

Paper:

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. 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{munoz-etal-2024-shears,
    title = "Shears: Unstructured Sparsity with Neural Low-rank Adapter Search",
    author = "Mu{\~n}oz, J. Pablo  and
      Yuan, Jinjie  and
      Jain, Nilesh",
    editor = "Yang, Yi  and
      Davani, Aida  and
      Sil, Avi  and
      Kumar, Anoop",
    booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.naacl-industry.34",
    doi = "10.18653/v1/2024.naacl-industry.34",
    pages = "395--405",
}

License

Apache-2.0