View RL Benchmark Reports
Browse and submit LLM evaluations
Generate and view leaderboard for LLM evaluations
Determine GPU requirements for large language models
View and submit language model evaluations
Convert and upload model files for Stable Diffusion
Compare and rank LLMs using benchmark scores
Calculate VRAM requirements for LLM models
Explain GPU usage for model training
Text-To-Speech (TTS) Evaluation using objective metrics.
Submit deepfake detection models for evaluation
Measure over-refusal in LLMs using OR-Bench
Multilingual Text Embedding Model Pruner
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.