Compare and rank LLMs using benchmark scores
Display and filter leaderboard models
Explain GPU usage for model training
Merge Lora adapters with a base model
Browse and evaluate language models
Merge machine learning models using a YAML configuration file
Evaluate LLM over-refusal rates with OR-Bench
Browse and submit LLM evaluations
Explore and manage STM32 ML models with the STM32AI Model Zoo dashboard
Launch web-based model application
View and compare language model evaluations
Calculate memory usage for LLM models
View RL Benchmark Reports
Guerra LLM AI Leaderboard is a comprehensive platform for comparing and ranking large language models (LLMs). It provides users with benchmark scores and detailed insights to evaluate the performance of different models across various tasks and criteria. This tool is designed to help researchers, developers, and enthusiasts make informed decisions about which LLM best suits their needs. By leveraging transparent and consistent evaluation methodologies, Guerra LLM AI Leaderboard ensures accurate and unbiased comparisons of model capabilities.
• Real-time benchmark updates: Stay up-to-date with the latest performance metrics of leading LLMs.
• Customizable filters: Narrow down models based on criteria such as model size, architecture, or specific task performance.
• Interactive visualizations: Explore data through charts, graphs, and detailed reports to better understand model strengths and weaknesses.
• Historical tracking: View how models have improved or regressed over time with access to historical benchmark data.
• Cross-model comparisons: Directly compare multiple models side-by-side to identify the best fit for your use case.
• Integration with AI tools: Enhance your workflow by connecting with other AI development and analysis platforms.
• Transparent methodology: Clear explanations of how models are evaluated and scored ensure trust and reliability.
What criteria are used to rank models on Guerra LLM AI Leaderboard?
Guerra LLM AI Leaderboard uses a combination of benchmark scores, including accuracy, computational efficiency, and task-specific performance. The rankings are determined by a weighted average of these metrics to ensure a balanced evaluation.
How often are the benchmark scores updated?
Benchmark scores are updated regularly to reflect the latest developments in the field of LLMs. Updates are typically performed in response to new model releases or significant improvements in existing models.
Can I request the addition of a specific model to the leaderboard?
Yes! Users can submit feedback or requests through the platform’s support channel. The Guerra team reviews all suggestions and may include the model in future updates, provided it meets the benchmarking criteria.