Optimize and train foundation models using IBM's FMS
Submit deepfake detection models for evaluation
Export Hugging Face models to ONNX
Convert Hugging Face model repo to Safetensors
Create demo spaces for models on Hugging Face
Browse and submit model evaluations in LLM benchmarks
Load AI models and prepare your space
Evaluate model predictions with TruLens
Upload ML model to Hugging Face Hub
Visualize model performance on function calling tasks
Merge Lora adapters with a base model
Push a ML model to Hugging Face Hub
Convert Hugging Face models to OpenVINO format
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.