Features
No-code fine-tuning of LLMs and SLMs
Support for H2O Danube3 and H2OVL Mississippi models
Automatic hyperparameter optimization
Built-in evaluation metrics and leaderboards
Import datasets in CSV, JSON, Parquet, and other formats
Export fine-tuned models for deployment
Integration with H2O MLOps for model lifecycle management
On-premise and air-gapped deployment
Multi-model support with cost controls
Preconfigured training recipes for common tasks
Visual interface for training configuration
Human-in-the-loop evaluation support
Fine-tune open-source LLMs (Llama 3, Mistral, Llama 2)
Automated hyperparameter optimization (Ludwig)
One-click deployment with autoscaling
Low-latency inference endpoints
Model evaluation and version comparison
Custom training data from S3/GCS
LoRA/QLoRA adapter-based fine-tuning
Per-request monitoring and logging
VPC deployment for data privacy
Support for multiple model architectures