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license: apache-2.0
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# GEM_Testing_Arsenal 

Welcome to ***GEM_Testing_Arsenal***, where groundbreaking research meets practical power! This repository unveils a novel architecture for On-Device Language Models (ODLMs), straight from our paper, ["Fragile Mastery: are domain-specific trade-offs undermining On-Device Language Models?"](./link_to_be_insterted). With just a few lines of code, our custom `gem_trainer.py` script lets you train ODLMs that are more accurate than ever, tracking accuracy and loss as you go.

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## Highlights:
- **Next-Level ODLMs**: Boosts accuracy with a new architecture from our research.
- **Easy Training**: Call run_gem_pipeline to train on your dataset in minutes.
- **Live Metrics**: Get accuracy and loss results as training unfolds.
- **Flexible Design**: Works with any compatible dataset—plug and play!

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## Prerequisites:
To dive in, you’ll need:
- **Python** `3.8+`

- Required libraries (go through [quick start](#quick-start) below 👇)
    
- **Git** *(to clone the repo)*

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## Quick Start:

1. **Clone the repository:**
    ```bash
    git clone https://huggingface.co/datasets/GEM025/GEM_Arsenal
    ```

2. **Install Dependencies:**
    ```pwsh
    pip install -r requirements.txt
    ```

3. **Train Your Model:**
Create a new python file and execute the code like:
    ```python
    from datasets import load_dataset
    from gem_trainer import run_gem_pipeline

    # Load a dataset (e.g., Banking77) {just replace the dataset here.}
    dataset = load_dataset("banking77") 

    # Train the ODLM
    results = run_gem_pipeline(dataset, num_classes=77)

    print(results)  # See accuracy and loss
    ```

> ***Boom—your ODLM is training with boosted accuracy!***
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## Running on Colab/Kaggle?

Well it's pretty similar to the local run.

```python
""" This is very recommended to run for clean ouput during trains...

import warnings 
warnings.filterwarnings('ignore')

"""

#@ Step 1: Clone the github repo 
! git clone https://huggingface.co/datasets/GEM025/GEM_Arsenal

#@ Step 2: Install all requirements 
!pip install -r /content/GEM_Arsenal/requirements.txt  #! For colab

"""

@! For kaggle:
!pip install  -r /kaggle/working/GEM_Arsenal/requirements.txt

"""

#@ Step 3: Add repo to path
import sys
sys.path.append('/content/GEM_Arsenal')  #! Or /kaggle/working/GEM_Arsenal (for kaggle)

#@ Step 4: Import and run function
from gem_trainer import run_gem_pipeline
from datasets import load_dataset

#@ Rest of the code as above
dataset = load_dataset("imdb")

result = run_gem_pipeline(dataset, num_classes=2, num_epochs=2)

print(result)
```

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## Customizing Training:
`run_gem_pipeline` keeps it simple, but you can tweak it! Dive into [`gem_trainer.py`](./gem_trainer.py) to adjust epochs, batch size, or other settings to fit your needs.

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## Contributing 💓 
Got ideas to make this even better? We’re all ears!
- Fork the repo.
- Branch off (`git checkout -b your-feature`).
- Submit a pull request with your magic.

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