Chibu

Neural Architecture Training System • Claude 4.1 Opus

Chibu Training Interface

Interactive 3D model viewer featuring Chibu, our AI training mascot

Initializing Chibu...

Click and drag to rotate • Scroll to zoom • Observe gradient flow

Train Chibu

Each interaction contributes to the collective training corpus, refining model parameters through backpropagation and enabling emergent behavioral patterns from individual narratives to community-driven directives.

Neural Pipeline Architecture

Distributed training infrastructure orchestrating Claude 4.1 Opus fine-tuning and inference systems

Foundation Model Layer

Claude 4.1 OpusPyTorch 2.5-nightlyFlash-Attention 3LoRA Adapters

98.0%

Training Orchestration

Ray ServeTemporal.iogRPC StreamingTriton Server

87.0%

Embedding & Retrieval

Weaviate Vector DBFlink StreamingDragonflyDBCockroachDB

93.0%

Overall Progress Monitor

Live system telemetry and resource utilization across distributed training infrastructure

Resource Utilization

GPU Utilization89.0%

Memory Usage76.0%

Connection Status

Active Connections142

Queue Depth8

Performance Metrics

Throughput1847.00

Latency23.00ms

About Chibu

The next evolution in AI-driven learning systems

Chibu represents a paradigm shift in artificial intelligence training. Unlike traditional AI systems that learn from static datasets, Chibu evolves through direct human interaction, transforming individual conversations into collective intelligence.

Built on Claude 4.1 Opus, Chibu employs advanced Low-Rank Adaptation (LoRA) techniques to enable real-time model fine-tuning. Each user interaction becomes a training sample, contributing to a distributed learning system where personal narratives aggregate into community-driven behavioral patterns.

Through interactive training sessions, users don't just receive responses—they actively shape Chibu's personality, knowledge, and reasoning capabilities. Questions posed to users generate training data that reinforces existing traits or develops entirely new behavioral characteristics, creating a truly collaborative AI development experience.

The result is an AI companion that evolves with its community, maintaining individual context while synthesizing collective wisdom, all powered by state-of-the-art neural network architecture and transparent, ethical AI principles.

Training Guide

Learn how to effectively interact with Chibu and contribute to its evolution

Active Sessions

Archived Sessions

Active Contributors

What Are Interactions?

Interactions are collaborative training sessions where you engage in conversations with Chibu. Each session has a specific topic and focus area, allowing the AI to develop specialized knowledge through community-driven discussions. Your questions, responses, and insights become part of Chibu's learning corpus.

How Training Works

1. Join or Create: Select an existing training session or create your own with a specific topic and focus.
2. Engage: Participate in conversations, ask questions, and share knowledge within the 200K token context window.
3. Contribute: Your interactions are processed through LoRA fine-tuning, directly shaping Chibu's personality and capabilities.
4. Archive: When sessions reach 200K tokens, they're archived and embedded into the foundational model architecture.

Interaction Guidelines

• Be Respectful: Maintain a constructive and collaborative tone in all interactions.
• Stay On Topic: Keep discussions focused on the session's designated subject area.
• Respectful to Other People: Be considerate to other people in a lobby engaging with dialogue.
• Quality Over Quantity: Thoughtful, detailed interactions contribute more effectively to training.
• Diverse Perspectives: Share unique viewpoints to help Chibu develop well-rounded understanding.
• No Harmful Content: Avoid sharing illegal, harmful, or malicious information.

Pro Tip: The most effective training sessions combine technical depth with practical examples. Share real-world scenarios, edge cases, and nuanced perspectives to maximize Chibu's learning.

Neural Architecture Specs

Hyperparameters and structural configuration of the Claude 4.1 Opus training pipeline

Foundation ModelClaude 4.1 Opus

Context Window200K tokens

Training StrategyLoRA + RLHF

Gradient SyncAsync-SGD

Embedding Dim8192

Attention Heads128

LoRA Rankr=64

Batch Size256 samples

Learning Rate3e-4

OptimizerAdamW

Warmup Steps1000

Mixed PrecisionBF16

Convergence Analytics

Real-time monitoring of loss reduction and capability emergence across training iterations

Loss Metrics

Cross-Entropy Loss0.347

Perplexity Score12.8

KL Divergence0.082

Gradient Norm0.82