Compare code model performance on benchmarks
Track, rank and evaluate open LLMs and chatbots
View and submit language model evaluations
Upload a machine learning model to Hugging Face Hub
Request model evaluation on COCO val 2017 dataset
View LLM Performance Leaderboard
Create demo spaces for models on Hugging Face
Merge Lora adapters with a base model
Display benchmark results
Display model benchmark results
Determine GPU requirements for large language models
View and compare language model evaluations
Explore and benchmark visual document retrieval models
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.