> For the complete documentation index, see [llms.txt](https://openledger.gitbook.io/openledger/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://openledger.gitbook.io/openledger/model-factory/core-concepts.md).
# Core Concepts
### **How ModelFactory Works**
ModelFactory integrates dataset access control and model fine-tuning into a seamless workflow, ensuring data security and ownership integrity. The platform’s primary processes include:
### **Model Selection & Configuration**
* Users select from a wide range of LLMs (e.g., LLaMA, Mistral, DeepSeek).
* Hyperparameters like learning rate, batch size, and epochs are configured through the GUI.
### **Fine-Tuning Process**
* The fine-tuning engine supports methods such as LoRA and QLoRA.
* Real-time dashboards provide training progress insights.
### **Chat Interface**
* Enables users to interact with fine-tuned models directly through the GUI or through API.
* Supports real-time question-and-answer sessions or task-specific interactions.
