GEM025/GEM_Arsenal

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?". 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.

---

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!

---

Prerequisites:

To dive in, you’ll need:

• Python 3.8+

• Required libraries (go through quick start below 👇)

• Git (to clone the repo)

---

Quick Start:

1. Clone the repository:

   git clone https://huggingface.co/datasets/GEM025/GEM_Arsenal
   

2. Install Dependencies:

   pip install -r requirements.txt
   

3. Train Your Model: Create a new python file and execute the code like:

   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!

---

Running on Colab/Kaggle?

Well it's pretty similar to the local run.

""" 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)

---

Customizing Training:

run_gem_pipeline keeps it simple, but you can tweak it! Dive into gem_trainer.py to adjust epochs, batch size, or other settings to fit your needs.

---

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.

---