Compare code model performance on benchmarks
Demo of the new, massively multilingual leaderboard
Explore and submit models using the LLM Leaderboard
Evaluate model predictions with TruLens
Generate and view leaderboard for LLM evaluations
Submit models for evaluation and view leaderboard
Evaluate open LLMs in the languages of LATAM and Spain.
Compare audio representation models using benchmark results
View and submit language model evaluations
Pergel: A Unified Benchmark for Evaluating Turkish LLMs
Rank machines based on LLaMA 7B v2 benchmark results
Optimize and train foundation models using IBM's FMS
Compare LLM performance across benchmarks
The Memorization Or Generation Of Big Code Model Leaderboard is a tool designed to compare and benchmark the performance of large code models. It focuses on evaluating models based on their ability to memorize and generate code, providing insights into their capabilities across various programming tasks. This leaderboard is essential for researchers and developers to understand model performance on code-specific benchmarks such as code completion, bug fixing, and code translation. It helps users identify the most suitable model for their specific needs.
1. What is the purpose of the Memorization Or Generation Of Big Code Model Leaderboard?
The leaderboard is designed to help users compare and evaluate the performance of large code models on specific coding tasks, enabling informed decisions for their projects.
2. How are models evaluated on the leaderboard?
Models are evaluated based on their performance on predefined benchmarks, focusing on their ability to memorize and generate code accurately and efficiently.
3. Can I use the leaderboard to compare models for a specific programming language?
Yes, the leaderboard allows users to filter results by programming language, making it easier to find the best model for their language of choice.