kimi-expert-analyzer 0.1.1

# 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.

[![Crates.io](https://img.shields.io/crates/v/kimi-expert-analyzer.svg)](https://crates.io/crates/kimi-expert-analyzer)
[![Documentation](https://docs.rs/kimi-expert-analyzer/badge.svg)](https://docs.rs/kimi-expert-analyzer)
[![License](https://img.shields.io/badge/license-MIT%20OR%20Apache--2.0-blue.svg)](LICENSE)

## 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`:

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

## 📖 Usage

### Basic Analysis

```rust
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

```rust
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

```bash
# 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

```rust
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

```rust
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

```rust
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

```rust
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:

```bash
# 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

```bash
# 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](https://docs.rs/kimi-expert-analyzer)
- [CLI Guide](docs/cli.md)
- [Distillation Tutorial](docs/distillation.md)
- [Analysis Techniques](docs/analysis.md)

## 🤝 Contributing

Contributions are welcome! Please see our [Contributing Guide](CONTRIBUTING.md).

## 📄 License

Licensed under either of:

- Apache License, Version 2.0 ([LICENSE-APACHE](LICENSE-APACHE))
- MIT License ([LICENSE-MIT](LICENSE-MIT))

at your option.

## 🔗 Related Projects

- [Kimi-FANN-Core](https://crates.io/crates/kimi-fann-core)
- [Synaptic Neural Mesh](https://github.com/ruvnet/Synaptic-Neural-Mesh)
- [Kimi-K2 Integration](https://github.com/ruvnet/Synaptic-Neural-Mesh/tree/main/plans/Kimi-K2)

---

**Empowering efficient neural network conversion for the WASM ecosystem**