# Key Features #### User-Friendly Interface * Fully GUI-based; eliminates the need for CLI or API interactions. * Simple workflows for non-technical users. #### Secure Dataset Management * Permission-based dataset access ensures data security. * Seamless integration with OpenLedger’s dataset repository. #### Comprehensive Model Support * Compatible with a broad range of LLMs. * Optimized for various fine-tuning techniques (LoRA, QLoRA, full fine-tuning). #### Live Training Analytics * Interactive dashboards for monitoring training metrics. #### Chat Interface for Fine-Tuned Models * Direct interaction with models for testing or deployment scenarios. * Real-time response capabilities for various tasks. #### RAG Attribution for Source Transparency * Combines retrieval-based methods with generated outputs. * Displays sources for user queries to ensure transparency and accountability. #### Scalable & Modular Architecture * Extensible modules for dataset access, training, and evaluation. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://openledger.gitbook.io/openledger/model-factory/key-features.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.