Kimi-K2 Expert Analyzer

A comprehensive toolkit for analyzing Kimi-K2's mixture-of-experts architecture and creating lightweight micro-experts for Rust-WASM deployment.

Overview

The Kimi-K2 Expert Analyzer is designed to convert Kimi-K2's massive 1T parameter mixture-of-experts model into efficient micro-experts (1K-100K parameters each) that can run in WebAssembly environments. This enables deployment of Kimi-like intelligence in browsers, edge devices, and embedded systems.

✨ Key Features

• 🔍 Expert Analysis: Deep analysis of neural network architectures
• 🥃 Knowledge Distillation: Extract knowledge from large models to micro-experts
• 📊 Performance Profiling: Detailed performance analysis and optimization
• 🎯 Architecture Optimization: Suggest optimal architectures for WASM deployment
• 📈 Statistical Analysis: Comprehensive statistical analysis of model behavior
• 🔧 Conversion Tools: Tools for Kimi-K2 to Rust conversion

🛠️ Installation

Add this to your Cargo.toml:

[dependencies]
kimi-expert-analyzer = "0.1.0"

📖 Usage

Basic Analysis

use kimi_expert_analyzer::{Analyzer, AnalysisConfig};

// Create analyzer
let analyzer = Analyzer::new();

// Analyze a neural network
let analysis = analyzer
    .analyze_network(&model)
    .with_metrics(&["accuracy", "latency", "memory"])
    .run()?;

println!("Analysis Results: {:#?}", analysis);

Knowledge Distillation

use kimi_expert_analyzer::Distillation;

// Set up distillation
let distiller = Distillation::new()
    .teacher_model(&large_model)
    .student_config(student_config)
    .temperature(3.0)
    .alpha(0.7);

// Perform distillation
let micro_expert = distiller.distill(&training_data)?;

CLI Usage

# Analyze a model
kimi-analyzer analyze --model model.onnx --output analysis.json

# Distill knowledge
kimi-analyzer distill --teacher large_model.onnx --student config.json --output micro_expert.wasm

# Profile performance
kimi-analyzer profile --model model.wasm --benchmark performance_suite

🏗️ Architecture Analysis

Supported Analysis Types

• 🔬 Architecture Analysis: Layer analysis, parameter counting, computational complexity
• ⚡ Performance Analysis: Latency, throughput, memory usage, FLOPS
• 🎯 Optimization Analysis: Pruning opportunities, quantization potential
• 🧠 Knowledge Analysis: Information flow, attention patterns, feature importance

Distillation Strategies

use kimi_expert_analyzer::distillation::Strategy;

// Attention-based distillation
let strategy = Strategy::Attention {
    layers: vec![6, 8, 10],
    weight: 0.5,
};

// Feature-based distillation
let strategy = Strategy::Feature {
    intermediate_layers: true,
    feature_weight: 0.3,
};

// Response-based distillation
let strategy = Strategy::Response {
    temperature: 4.0,
    alpha: 0.8,
};

📊 Analysis Reports

Performance Metrics

use kimi_expert_analyzer::metrics::PerformanceReport;

let report = analyzer.generate_performance_report(&model)?;
println!("Inference Time: {} ms", report.avg_inference_time);
println!("Memory Usage: {} MB", report.peak_memory);
println!("WASM Bundle Size: {} KB", report.wasm_size);

Optimization Suggestions

let suggestions = analyzer.optimization_suggestions(&model)?;
for suggestion in suggestions {
    println!("Optimization: {}", suggestion.description);
    println!("Expected Speedup: {}x", suggestion.speedup_factor);
    println!("Memory Reduction: {}%", suggestion.memory_reduction);
}

🧪 Validation

Model Validation

use kimi_expert_analyzer::validation::Validator;

let validator = Validator::new()
    .with_test_suite(&test_data)
    .with_tolerance(0.01);

let validation_result = validator.validate_conversion(
    &original_model,
    &converted_model
)?;

assert!(validation_result.accuracy_preserved);
assert!(validation_result.performance_improved);

🎯 Features

• default - PyTorch support
• pytorch - PyTorch model analysis
• candle-support - Candle framework integration
• numpy-support - NumPy array support
• plotting - Visualization capabilities
• full - All features enabled

🔧 CLI Tool

The crate includes a powerful CLI tool:

# Installation
cargo install kimi-expert-analyzer

# Basic analysis
kimi-analyzer analyze --input model.pt --format pytorch

# Distillation workflow
kimi-analyzer workflow distill \
    --teacher large_model.pt \
    --config micro_expert_config.json \
    --output optimized_expert.wasm

# Batch processing
kimi-analyzer batch --input models/ --output analyzed/

📈 Benchmarks

# Run performance benchmarks
cargo bench

# Generate analysis reports
cargo run --bin kimi-analyzer -- benchmark --suite comprehensive

🔬 Research Applications

• Model Compression: Analyze compression techniques effectiveness
• Architecture Search: Find optimal micro-expert architectures
• Transfer Learning: Analyze knowledge transfer between models
• Deployment Optimization: Optimize for specific deployment targets

📚 Documentation

• API Documentation
• CLI Guide
• Distillation Tutorial
• Analysis Techniques

🤝 Contributing

Contributions are welcome! Please see our Contributing Guide.

📄 License

Licensed under either of:

• Apache License, Version 2.0 (LICENSE-APACHE)
• MIT License (LICENSE-MIT)

at your option.

🔗 Related Projects

• Kimi-FANN-Core
• Synaptic Neural Mesh
• Kimi-K2 Integration

Empowering efficient neural network conversion for the WASM ecosystem