Merge Lora adapters with a base model
Optimize and train foundation models using IBM's FMS
Evaluate code generation with diverse feedback types
Compare LLM performance across benchmarks
Convert PaddleOCR models to ONNX format
Text-To-Speech (TTS) Evaluation using objective metrics.
Measure over-refusal in LLMs using OR-Bench
Explore and submit models using the LLM Leaderboard
Persian Text Embedding Benchmark
Display LLM benchmark leaderboard and info
Browse and evaluate language models
Explore GenAI model efficiency on ML.ENERGY leaderboard
Evaluate open LLMs in the languages of LATAM and Spain.
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.