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:

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.

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