Upload ML model to Hugging Face Hub
Quantize a model for faster inference
Convert a Stable Diffusion XL checkpoint to Diffusers and open a PR
Display benchmark results
Calculate memory needed to train AI models
Evaluate code generation with diverse feedback types
Track, rank and evaluate open LLMs and chatbots
Analyze model errors with interactive pages
Benchmark models using PyTorch and OpenVINO
Persian Text Embedding Benchmark
Evaluate reward models for math reasoning
Determine GPU requirements for large language models
Export Hugging Face models to ONNX
Push Model From Web is a tool designed for model benchmarking, enabling users to easily upload and manage machine learning models directly to the Hugging Face Hub. It simplifies the process of deploying and evaluating ML models, making it accessible for developers and researchers alike. The tool supports various machine learning frameworks and model types, providing a seamless experience for model sharing and collaboration.
• Model Deployment: Directly upload your machine learning model to Hugging Face Hub for easy access and sharing.
• Framework Support: Compatible with popular frameworks such as TensorFlow, PyTorch, and more.
• Model Benchmarking: Compare performance metrics of different models in a centralized environment.
• Community Integration: Share models with the broader machine learning community for feedback and collaboration.
• Version Control: Track different versions of your model for iterative development and testing.
1. What is the primary purpose of Push Model From Web?
The primary purpose is to simplify the deployment and sharing of machine learning models to the Hugging Face Hub, enabling benchmarking and collaboration.
2. Which machine learning frameworks are supported?
Push Model From Web supports models from popular frameworks like TensorFlow, PyTorch, and others, ensuring compatibility with a wide range of ML workflows.
3. Where are the uploaded models stored?
Uploaded models are stored on the Hugging Face Hub, a cloud-based platform designed for sharing and managing machine learning models.