Optimize and train foundation models using IBM's FMS
Launch web-based model application
Retrain models for new data at edge devices
Evaluate open LLMs in the languages of LATAM and Spain.
Browse and filter machine learning models by category and modality
Benchmark models using PyTorch and OpenVINO
Compare LLM performance across benchmarks
Upload ML model to Hugging Face Hub
Submit deepfake detection models for evaluation
Generate leaderboard comparing DNA models
Explore GenAI model efficiency on ML.ENERGY leaderboard
Explore and submit models using the LLM Leaderboard
Track, rank and evaluate open LLMs and chatbots
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.