metadata

language: en
license: apache-2.0

Shears Model Card: shears-mpt-7b-50-gsm8k-super

The super-adapter fine-tuned on sparsified 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.

Model Details

Information

Model name: shears-mpt-7b-50-gsm8k-super
Base model: IntelLabs/MPT-7B-sparsity50
Sparsity: 50%
Subnetwork version: Super
NNCF Configuration: 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

How to use

Refer to the illustrative example provided in 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:

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/MPT-7B-sparsity50", trust_remote_code=True)
model = PeftModel.from_pretrained(base_model, "IntelLabs/shears-mpt-7b-50-gsm8k-super")
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/MPT-7B-sparsity50", 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	50%	33.4

Model Sources

Repository: https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/Shears
Paper: Shears: Unstructured Sparsity with Neural Low-rank Adapter Search

Citation

@article{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)},
  year={2024}
}

License

Apache-2.0