Core Concepts
Last updated
Last updated
How ModelFactory Works
Dataset Request & Access
Users submit dataset requests through OpenLedger’s repository.
Providers approve or deny access based on established policies.
Approved datasets are automatically linked to the user’s ModelFactory interface.
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.
Evaluation & Deployment
Built-in evaluation tools help analyze model performance.
Exported models can be deployed in user applications or shared within the ecosystem.
Chat Interface
Enables users to interact with fine-tuned models directly through the GUI.
Supports real-time question-and-answer sessions or task-specific interactions.
RAG Attribution
Provides retrieval-augmented generation (RAG) capabilities.
When users ask a question, the model responds with generated outputs alongside sources of citations.
Ensures proper attribution of information and enhances trust in generated outputs.