Platform Documentation
Official operational manual for the Elite Framework.
Training Lab Manual
The Training Lab is your centralized hub for AI development. You can fine-tune existing agents, build custom neural architectures from scratch, or leverage external inference APIs.
Agent Fine-Tuning
Fine-tuning allows you to update existing AI agents with new intelligence from your organizational knowledge base.
- Target Selection: Choose an existing agent from the dropdown menu. Ensure the agent is currently 'active' and not already training.
- Training Directives: Optionally provide specific instructions (e.g., "Focus on newly uploaded AWS compliance playbooks"). These directives prioritize specific subsets of your knowledge base during the epoch cycles.
- Execution & Telemetry: Click "Initialize Training Sequence". The agent will ingest active Knowledge Documents. Monitor its progress via the Real-Time Telemetry panel, which displays loss metrics and ingestion status.
Custom Architecture Compiler
Build your own neural network from the ground up to create bespoke, isolated intelligence models.
1. Architectural Specifications: Define the core structure.
- Type: Choose between Transformers (Decoder/Encoder), LSTMs, or standard MLPs based on your objective.
- Depth: Specify the number of Hidden Layers and Attention Heads. Higher values increase computational complexity but allow for deeper reasoning capabilities.
2. Hyperparameter Tuning:
- Optimizer: Select from AdamW, SGD, or RMSprop.
- Learning Rate: Set the mathematical step size (e.g., 3e-4) for the gradient descent optimization.
3. Execution: Once parameters and the target dataset are selected, initialize training. The system allocates tensors and compiles the computational graph, updating you live as the model learns.
Hugging Face Inference Integration
Directly interface with the global open-source AI community by securely querying external models.
- Authentication: Click "Connect Account" to securely authorize your Hugging Face credentials via an OAuth popup. This ensures you are using your own API limits and private models securely.
- Model Selection: Input the exact repository ID (e.g.,
meta-llama/Llama-3.2-1B) of the model you wish to query. - Execution: Provide your input prompt and execute. The inference runs externally, and the output is securely streamed back into your localized console view.