View RL Benchmark Reports
Persian Text Embedding Benchmark
Evaluate model predictions with TruLens
Visualize model performance on function calling tasks
Display benchmark results
Convert Stable Diffusion checkpoint to Diffusers and open a PR
Convert PaddleOCR models to ONNX format
Submit deepfake detection models for evaluation
Evaluate open LLMs in the languages of LATAM and Spain.
Export Hugging Face models to ONNX
Pergel: A Unified Benchmark for Evaluating Turkish LLMs
Merge Lora adapters with a base model
Calculate VRAM requirements for LLM models
Ilovehf is a specialized tool designed for model benchmarking, particularly in the realm of reinforcement learning (RL). It provides a platform to view and analyze RL benchmark reports, enabling users to evaluate and compare the performance of different models across various environments and scenarios.
• Real-Time Monitoring: Track model performance metrics as they train. • Customizable Benchmarks: Define specific metrics and criteria for evaluation. • Data Visualization: Generate detailed charts and graphs to understand performance trends. • Cross-Environment Benchmarking: Compare models across multiple RL environments. • Exportable Results: Save and share benchmark results for further analysis. • Multi-Platform Support: Compatible with various operating systems and frameworks.
What systems are supported by Ilovehf?
Ilovehf is designed to work on Windows, macOS, and Linux systems, ensuring broad compatibility.
Can I customize the benchmarking metrics?
Yes, Ilovehf allows users to define custom metrics and criteria for benchmarking.
How do I export benchmark results?
Benchmark results can be exported in various formats, including CSV, JSON, and PDF, for easy sharing and further analysis.