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README.md

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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.
-
----
-
-### 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](#quick-start) below 👇)
-    
-- **Git** *(to clone the repo)*
+## Welcome to GEM Space
+Greetings from GEM Space, the heart of innovation behind our upcoming paper, "FRAGILE MASTERY: ARE DOMAIN-SPECIFIC TRADE-OFFS UNDERMINING ON-DEVICE LANGUAGE MODELS?". We’re thrilled to invite you into our world of edge AI exploration! This repository, GEM_Testing_Arsenal, is a cornerstone of our efforts to redefine On-Device Language Models (ODLMs) through the Generalized Edge Model (GEM). Keep an eye out for the paper link once it’s published!
 
 ---
-### Quick Start:
+## About Our Paper
 
-1. **Clone the repository:**
-    ```bash
-    git clone https://github.com/Firojpaudel/GEM.git
-    ```
+***Abstract***
+The deployment of On-Device Language Models (ODLMs) on resource-constrained edge devices demands a delicate balance of efficiency, memory, power, and linguistic skill across diverse tasks. In "FRAGILE MASTERY", we explore the trade-offs between domain-specific optimization and cross-domain robustness, introducing the Generalized Edge Model (GEM). GEM integrates specialization and generalization using a Sparse Cross-Attention Router (SCAR), achieving a cross-domain F1 score of 0.89 with sub-100ms latency on platforms like Raspberry Pi 4 and Pixel 6. Across 47 benchmarks spanning eight domains—healthcare, legal, finance, STEM, and more—GEM boosts general-task performance by 7% over GPT-4 Lite while matching domain-specific results. With new metrics like the Domain Specialization Index (DSI) and a balanced distillation framework cutting catastrophic forgetting by 43%, this work offers a robust foundation for edge AI. [Paper link coming soon!]
 
-2. **Install Dependencies:**
-    ```pwsh
-    pip install -r requirements.txt
-    ```
+***Our Vision***
+At GEM Space, we’re on a mission to revolutionize edge intelligence. We’re striving to build On-Device Language Models that:
+- **Thrive Under Constraints**: Deliver exceptional accuracy and speed on low-power devices—from smartphones to custom NPUs—without compromise.
 
-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
+- **Master the Balance**: Seamlessly blend domain-specific expertise (think healthcare diagnostics or financial analysis) with robust, cross-domain adaptability.
 
-    # Load a dataset (e.g., Banking77) {just replace the dataset here.}
-    dataset = load_dataset("banking77") 
+- **Empower the Edge**: Bring advanced AI to the fingertips of real-world applications, making it fast, practical, and accessible wherever it’s needed.
 
-    # Train the ODLM
-    results = run_gem_pipeline(dataset, num_classes=77)
+The **GEM_Testing_Arsenal** embodies this ambition—a testing ground for GEM, our pioneering architecture designed to make ODLMs smarter, leaner, and more versatile. We’re here to push the limits of what’s possible and inspire a new era of edge AI innovation.
+Join the Journey
+We’re building more than models—we’re building a movement. Stay tuned for our paper, explore GEM Space, and join us in shaping the future of on-device intelligence. Reach out at [your-email@example.com (mailto:your-email@example.com)] with ideas or questions!
 
-    print(results)  # See accuracy and loss
-    ```
-
-> ***Boom—your ODLM is training with boosted accuracy!***
-
----
-### 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.
-
----
-### 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.
-
----
-Edit this `README.md` markdown file to author your organization card.