Optimize and train foundation models using IBM's FMS
Create demo spaces for models on Hugging Face
View LLM Performance Leaderboard
Find recent high-liked Hugging Face models
Browse and submit LLM evaluations
Benchmark models using PyTorch and OpenVINO
Merge machine learning models using a YAML configuration file
Convert PyTorch models to waifu2x-ios format
Generate and view leaderboard for LLM evaluations
Launch web-based model application
Calculate survival probability based on passenger details
Compare LLM performance across benchmarks
Calculate memory needed to train AI models
README is a tool designed to help users optimize and train foundation models using IBM's Foundation Model Services (FMS). It provides a streamlined approach to benchmarking, optimizing, and deploying models efficiently.
• Model Benchmarking: Compare performance across different foundation models. • Optimization: Use IBM's FMS to fine-tune models for specific tasks. • Scalability: Handles large-scale datasets and complex training workloads. • Integration: Seamless integration with IBM's ecosystem of AI tools. • Analytics: Gain insights into model performance with detailed metrics.
What is IBM's Foundation Model Services (FMS)?
IBM's FMS is a suite of tools designed to help developers train, deploy, and manage foundation models at scale.
Can I use README with any foundation model?
README is optimized for use with models supported by IBM's FMS. Check compatibility before use.
Do I need special hardware to run README?
README is designed to run on standard computing environments but may benefit from GPU acceleration for faster training and benchmarking.