Compare code model performance on benchmarks
Display and submit LLM benchmarks
Display model benchmark results
Evaluate AI-generated results for accuracy
Browse and filter ML model leaderboard data
Push a ML model to Hugging Face Hub
Explore and submit models using the LLM Leaderboard
Browse and filter machine learning models by category and modality
Calculate memory needed to train AI models
Measure execution times of BERT models using WebGPU and WASM
Request model evaluation on COCO val 2017 dataset
View LLM Performance Leaderboard
View and compare language model evaluations
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.