Merge Lora adapters with a base model
Visualize model performance on function calling tasks
Track, rank and evaluate open LLMs and chatbots
Evaluate open LLMs in the languages of LATAM and Spain.
Text-To-Speech (TTS) Evaluation using objective metrics.
Launch web-based model application
Compare audio representation models using benchmark results
Generate and view leaderboard for LLM evaluations
Explore and benchmark visual document retrieval models
Create and manage ML pipelines with ZenML Dashboard
Benchmark models using PyTorch and OpenVINO
Measure BERT model performance using WASM and WebGPU
Browse and submit LLM evaluations
Merge Lora is a tool designed for model benchmarking that enables users to merge LoRA (Low-Rank Adaptation) adapters with a base model. It provides a seamless way to combine multiple adapters, enhancing the model's capabilities while maintaining efficiency. Merge Lora is particularly useful for users working with large language models and seeking to integrate specialized adapters for diverse tasks.
What is the purpose of Merge Lora?
Merge Lora is designed to simplify the process of combining LoRA adapters with a base model, allowing users to leverage specialized adapters for various tasks without retraining the model from scratch.
Can I merge multiple adapters at once?
Yes, Merge Lora supports the merging of multiple LoRA adapters into a single model, provided they are compatible.
Is Merge Lora compatible with all LoRA adapters?
While Merge Lora is designed to work with most LoRA adapters, compatibility depends on the specific adapters and base models used. Always perform a compatibility check before merging.