Merge Lora adapters with a base model
Text-To-Speech (TTS) Evaluation using objective metrics.
Evaluate open LLMs in the languages of LATAM and Spain.
Display model benchmark results
Predict customer churn based on input details
Optimize and train foundation models using IBM's FMS
Calculate VRAM requirements for LLM models
Push a ML model to Hugging Face Hub
Benchmark models using PyTorch and OpenVINO
Run benchmarks on prediction models
Download a TriplaneGaussian model checkpoint
Explore and manage STM32 ML models with the STM32AI Model Zoo dashboard
Convert and upload model files for Stable Diffusion
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.