Tutorials

Follow these tutorials in order for a complete framework walkthrough.

• Tutorial 01: Compute API Basics
  • Step 1: Initialize compute
  • Step 2: Run FNN ops
  • Step 3: Run attention ops
  • Step 4: Run memory search
  • Step 5: Cleanup
• Tutorial 02: Build Your First Network
  • Step 1: Define the model
  • Step 2: Create synthetic data
  • Step 3: Forward pass
  • Step 4: Compute a basic loss
  • Step 5: Build output gradient
• Tutorial 03: End-to-End Training Loop
  • Step 1: Setup model and optimizer
  • Step 2: Create training batch
  • Step 3: Forward
  • Step 4: Backward
  • Step 5: Parameter update
  • Step 6: Repeat over epochs
• Tutorial 04: Attention and Memory Workflow
  • Step 1: Prepare attention inputs
  • Step 2: Compute attention output
  • Step 3: Build memory database
  • Step 4: Retrieve nearest vectors
  • Step 5: Use retrieved context
  • Step 6: Cleanup
• Tutorial 05: Experimental Language Ingestion
  • Step 1: Create SVC entries
  • Step 2: Initialize InstantLanguage
  • Step 3: Ingest entries
  • Step 4: Query similar sentences
  • Step 5: Integrate with controller
• Tutorial 06: Convolution Pipeline
  • Step 1: Create modules
  • Step 2: Prepare image batch
  • Step 3: Run feature extraction block
  • Step 4: Attach a classifier head
  • Step 5: Extend to training
• Tutorial 07: Spiking Neurons and Activity
  • Step 1: High-level SNNCompute path
  • Step 2: Module-level LIF neuron
  • Step 3: Run a small time simulation
  • Step 4: Add synaptic learning layers
• Tutorial 08: Vector Search and Routing
  • Step 1: Initialize backend and vectors
  • Step 2: Compute distances
  • Step 3: Get top-k neighbors
  • Step 4: Build retrieval context
  • Step 5: Optional symbolic routing with bipolar keys
• Tutorial 09: LoRA Adaptation Workflow
  • Step 1: Create a LoRA config
  • Step 2: Build a LoRA layer
  • Step 3: Run forward
  • Step 4: Merge for inference
  • Step 5: Manage multi-layer adapters
• Tutorial 10: Multimodal Fusion
  • Step 1: Prepare modality tensors
  • Step 2: Bottleneck fusion
  • Step 3: Cross-modal attention fusion
  • Step 4: Perceiver IO on variable input
  • Step 5: Expand to full VLM stack
• Tutorial 11: Ingestion Checkpoints and Reproducibility
  • Step 1: Build a controller
  • Step 2: Ingest a tiny SVC batch
  • Step 3: Save checkpoint
  • Step 4: Inspect without full restore
  • Step 5: Load into a fresh controller
  • Step 6: Stable hash usage
• Tutorial 12: Cognitive Controller End-to-End
  • Step 1: Initialize controller
  • Step 2: Add baseline world knowledge
  • Step 3: Ingest structured language data
  • Step 4: Understand input
  • Step 5: Generate a response
  • Step 6: Inspect reasoning trace