Track, rank and evaluate open LLMs and chatbots
Explore and manage STM32 ML models with the STM32AI Model Zoo dashboard
Merge machine learning models using a YAML configuration file
Create demo spaces for models on Hugging Face
Submit deepfake detection models for evaluation
Calculate GPU requirements for running LLMs
View RL Benchmark Reports
Merge Lora adapters with a base model
Upload ML model to Hugging Face Hub
Display benchmark results
Find and download models from Hugging Face
Search for model performance across languages and benchmarks
Compare audio representation models using benchmark results
The Open LLM Leaderboard is a comprehensive tool designed to track, rank, and evaluate open-source Large Language Models (LLMs) and chatbots. It provides a transparent and standardized platform to compare models based on various benchmarks and metrics, helping developers, researchers, and users make informed decisions. By focusing on performance, efficiency, and capabilities, the Leaderboard serves as a go-to resource for understanding the evolution and advancements in the field of LLMs.
What metrics are used to rank LLMs? The Leaderboard uses a variety of metrics, including performance benchmarks, speed, memory usage, and specific task accuracy to ensure a holistic evaluation of each model.
Can I compare custom or non-listed models? Yes, the platform allows users to input custom models for comparison, providing flexibility for researchers and developers working on niche or proprietary LLMs.
How often is the Leaderboard updated? The Leaderboard is updated regularly to reflect new releases and improvements in existing models, ensuring users always have access to the latest information.