Merge Lora adapters with a base model
Compare and rank LLMs using benchmark scores
Find recent high-liked Hugging Face models
Optimize and train foundation models using IBM's FMS
Download a TriplaneGaussian model checkpoint
Run benchmarks on prediction models
Evaluate AI-generated results for accuracy
Calculate memory needed to train AI models
Explain GPU usage for model training
Evaluate and submit AI model results for Frugal AI Challenge
Visualize model performance on function calling tasks
Measure BERT model performance using WASM and WebGPU
Merge machine learning models using a YAML configuration file
Merge Lora is a specialized tool designed for model benchmarking. It allows users to merge LoRA (Low-Rank Adaptation) adapters with a base model, enabling efficient model customization and adaptation for specific tasks. This tool simplifies the process of combining multiple adapters into a single model, making it easier to deploy and test adapted models.
• Adapter Merging: Seamlessly combine multiple LoRA adapters into a single model. • Compatibility: Works with a variety of base models and LoRA adapters. • Efficiency: Streamlines the adaptation process for model benchmarking. • Flexibility: Supports customization for different downstream tasks.
What is a LoRA adapter?
A LoRA adapter is a lightweight modification to a large language model that allows it to adapt to specific tasks without requiring full model fine-tuning.
Can I merge multiple LoRA adapters at once?
Yes, Merge Lora supports merging multiple LoRA adapters into a single model, enabling combined adaptation capabilities.
Is Merge Lora compatible with all base models?
Merge Lora is designed to work with most common base models, but compatibility may vary depending on the specific model architecture and adapter implementation.