Find recent high-liked Hugging Face models
Compare audio representation models using benchmark results
Determine GPU requirements for large language models
Calculate survival probability based on passenger details
Track, rank and evaluate open LLMs and chatbots
Download a TriplaneGaussian model checkpoint
Text-To-Speech (TTS) Evaluation using objective metrics.
Evaluate code generation with diverse feedback types
Benchmark models using PyTorch and OpenVINO
Browse and submit model evaluations in LLM benchmarks
Submit models for evaluation and view leaderboard
Display leaderboard for earthquake intent classification models
Display benchmark results
Model Drops Tracker is a tool designed to help users track and discover recent high-liked Hugging Face models. It simplifies the process of staying updated with the latest models in the machine learning community, focusing on models that have gained significant popularity or traction.
What models are included in Model Drops Tracker?
Model Drops Tracker focuses on models from the Hugging Face Model Hub, particularly those that have received high likes or engagement within the community.
How often is the model list updated?
The list is updated in real-time to reflect the latest models and their popularity rankings.
Can I use Model Drops Tracker for commercial purposes?
Yes, Model Drops Tracker is designed to be used by researchers, developers, and businesses alike, with a focus on open-source accessibility.