Compare code model performance on benchmarks
Evaluate code generation with diverse feedback types
Load AI models and prepare your space
View and submit LLM benchmark evaluations
Find and download models from Hugging Face
Compare and rank LLMs using benchmark scores
View NSQL Scores for Models
Evaluate open LLMs in the languages of LATAM and Spain.
Download a TriplaneGaussian model checkpoint
Browse and filter ML model leaderboard data
Generate and view leaderboard for LLM evaluations
Compare audio representation models using benchmark results
Convert PaddleOCR models to ONNX format
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.