Hello everyone,
Ever stepped into the bustling bazaar of Large Language Models (LLMs) and felt a tad overwhelmed? Well, fret not! I'm going to walk you through my creation: EnsembleX — your own navigator across the seas of LLMs.
What's It All About 🤔
EnsembleX utilizes a novel approach to help Technology Leaders, enterprise architects, and decision-makers evaluate and select the best LLMs for their use-cases. Drawing from the concept of the knapsack problem, EnsembleX aids in finding quality-cost trade-offs by suggesting optimal model combinations based on user-defined criteria.
Workflow Diagram 🗺️
The Open-llm-leaderboard benchmark data using the below API from the Hugging face community!
Understanding the Knapsack Algorithm 🛍️
Imagine you're at the store with a bag that can only hold so much—what goes in the bag? Only the most valuable items! Similarly, the knapsack optimization algorithm helps you fit the most valuable LLMs into your organization's 'bag', balancing cost and quality.
The knapsack algorithm is a classic problem-solving approach in computer science, often used for maximizing the total value of items without exceeding a specific weight limit. When applied to the selection of LLMs, the "items" are the models, and the "weight limit" represents the resource constraints of the organization.
Here's a glimpse into the heart of EnsembleX
Knapsack Algorithm in Action: Enabled by the power of Streamlit and Python, the web app applies the knapsack algorithm. This optimization technique uses the set criteria to assess and indicate the best ensemble of LLMs without overstepping the constraints of resources.
Transparent Evaluation: EnsembleX employs a multifaceted data analysis, drawing from a range of test results like AI2, HellaSwag, and MMLU, along with real-world criteria that impact organizational fit.
Decision-Making Support: Users receive customized recommendations, balancing LLM performance with practicality. This ensures that model selection aligns with organizational constraints and priorities.
Additional Criteria: Beyond the Basics 📈
By reviewing OpenAI’s LLM leaderboard, we grasp the performance through criteria like reasoning, commonsense inference, multitask accuracy, and truthfulness. While these are great, modern enterprises need more. Like choosing a car, you wouldn’t focus solely on the engine, right? Other factors also play a crucial part that are mentioned below
Cost-effectiveness 💸
Organization/Domain Fit 🎓
Business Value 🏦
Security & Privacy Impact 🔒
Implementation Complexity 🛠️
Challenges⚖️
While EnsembleX brings a streamlined process to the forefront, one must consider the trade-offs of adopting a model based purely on available data. This initial framework uses accuracy and proposed equal weightage (cost) for all the models as benchmarks due to the data scarcity but may require more detailed, company-specific metrics (as mentioned in the additional criteria) for comprehensive decision-making.
Future Pathways 🔭🌅
To reach its full potential, EnsembleX awaits more criteria and data to provide a well-rounded selection framework. The current model is an excellent starting point, pointing towards a more data-inclusive future.
Join the Journey🌱
Dear reader, EnsembleX isn't just a tool. It's a bridge between tech and decision-making, sculpted to ease your load. It's currently in its chrysalis, waiting to bloom with more data and criteria.
Check out the repo and join in refining this endeavor — because, as Helen Keller said, "Alone we can do so little; together we can do so much."😊
Embark on this adventure at GitHub - EnsembleX and let's shape the future together💪
Final Thoughts 🌠
Selecting the right LLM ensemble is akin to choosing a vehicle, where one doesn’t merely look under the hood but also considers a myriad of features that cater to distinct needs. As EnsembleX evolves, it promises a transparent, tailored, and strategic approach to the optimization of language models in the GenAI era.
Feel free to comment your thoughts & don't forget to check out the app at https://ensemblex.streamlit.app 💖
Until next time, pour your boundless curiosity into your projects and continue to explore the depths of AI and ML. We're on this journey together .Stay Curious, Keep Exploring 🎉🚢
Peace 🕊️
Vidhya Varshany
I am glad to connect with new people in linkedin.com/in/vidhyavarshany🤗.
Until then, Stay tuned to **Get Vibe With BrainiacSpace🌌🧠 To Fill Your TechSpace✔️.**Cheers🍻