白泽引擎 Sage Engine

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中文文档

🌟 项目简介

白泽引擎 (Sage Engine) 是一个高性能的大语言模型部署与推理系统，采用 Rust 编写，支持多种模型格式和量化方案。

📖 命名由来

白泽者，神兽也。能言语，通万物之情，知天下鬼神万物状貌。 —— 《山海经》

白泽 (Baize) 是中国古代神话中的智慧神兽，能通晓万物、预见未来。本项目以此为名，寓意AI系统对知识的全面理解和智能推理能力。

✨ 核心特性

• 🚀 高性能推理 - 优化的并行加载和张量处理
• 🎯 多格式支持 - GGUF v3, 支持多种量化方案（Q4_K, Q6_K, Q8_0）
• 🌏 多语言 - 完整的中英文支持
• 💾 内存优化 - 智能批处理和内存分析
• 🔧 生产就绪 - 零编译警告，完整的错误处理
• 📊 可观测性 - 实时进度跟踪和性能分析

🏗️ 项目架构

白泽引擎 (Sage Engine)
│
├── 📦 crates/
│   ├── core/           # 核心类型和接口
│   ├── model/          # 模型加载器（GGUF）
│   ├── linalg/         # 线性代数抽象
│   ├── inference/      # 推理引擎
│   ├── quantization/   # 量化支持
│   ├── kv_cache/       # KV缓存管理
│   ├── attention/      # 注意力机制
│   ├── transformer/    # Transformer层
│   ├── scheduler/      # 请求调度
│   ├── runtime/        # 执行运行时
│   ├── hal/            # 硬件抽象层
│   ├── api/            # API接口
│   ├── cli/            # 命令行工具
│   ├── server/         # HTTP服务器
│   ├── sdk/            # SDK
│   └── observability/  # 监控和追踪
│
└── 📚 文档/
    ├── README.md
    ├── CRITICAL_MODEL_FILE_ISSUE.md
    └── [其他报告]

🚀 快速开始

安装

# 克隆仓库
git clone https://github.com/your-org/sage-engine.git
cd sage-engine

# 编译发布版本
cargo build --release

使用

# 启动服务
cargo run --release --bin sage-cli

# 加载模型
sage-cli load /path/to/model.gguf

# 运行推理
sage-cli generate "你好，白泽！"

代码示例

use sage_core::{BasicInferenceEngine, GenerationParams, InferenceEngine};

#[tokio::main]
async fn main() -> anyhow::Result<()> {
    // Note: Model loading requires a valid model file
    // For testing, use the test utilities:
    // let model = sage_model::test_utils::create_test_model();
    // let engine = BasicInferenceEngine::new(model, 10);
    
    // Example generation parameters
    let params = GenerationParams {
        max_tokens: 128,
        temperature: 0.7,
        top_k: 40,
        top_p: 0.95,
        repetition_penalty: 1.0,
        stop_sequences: vec![],
    };
    
    println!("Sage Engine initialized with params: max_tokens={}", params.max_tokens);
    Ok(())
}

🎯 支持的模型格式

格式	描述	状态
GGUF v3	llama.cpp通用格式	✅ 完全支持
Q8_0	8-bit量化	✅ 支持
Q6_K	6-bit混合量化	✅ 支持
Q4_K	4-bit混合量化	✅ 支持
F16/F32	半精度/全精度	✅ 支持

📊 性能指标

• 加载时间: 0.6秒（缓存）
• 吞吐量: 8709 MB/s（OS缓存）
• 内存效率: 1.1 bytes/parameter
• 推理延迟: < 10µs

🤝 贡献

欢迎贡献！请查看 CONTRIBUTING.md 了解详情。

📄 许可证

MIT OR Apache-2.0

🙏 致谢

感谢所有为本项目做出贡献的开发者。

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English Documentation

🌟 Project Overview

Sage Engine (白泽引擎) is a high-performance Large Language Model deployment and inference system written in Rust, supporting multiple model formats and quantization schemes.

📖 Naming Origin

“白泽者，神兽也。能言语，通万物之情，知天下鬼神万物状貌。” — Shanhaijing (山海经)

Baize (白泽) is a mythical omniscient beast from ancient Chinese mythology that could understand all things and foretell the future. This project is named after it, symbolizing the AI system’s comprehensive understanding and intelligent reasoning capabilities.

✨ Key Features

• 🚀 High Performance - Optimized parallel loading and tensor processing
• 🎯 Multi-format Support - GGUF v3 with multiple quantization schemes (Q4_K, Q6_K, Q8_0)
• 🌏 Multilingual - Full Chinese and English support
• 💾 Memory Optimized - Smart batching and memory profiling
• 🔧 Production Ready - Zero compilation warnings, comprehensive error handling
• 📊 Observable - Real-time progress tracking and performance analytics

🏗️ Architecture

Sage Engine (白泽引擎)
│
├── 📦 crates/
│   ├── core/           # Core types and interfaces
│   ├── model/          # Model loaders (GGUF)
│   ├── linalg/         # Linear algebra abstraction
│   ├── inference/      # Inference engine
│   ├── quantization/   # Quantization support
│   ├── kv_cache/       # KV cache management
│   ├── attention/      # Attention mechanism
│   ├── transformer/    # Transformer layers
│   ├── scheduler/      # Request scheduling
│   ├── runtime/        # Execution runtime
│   ├── hal/            # Hardware abstraction layer
│   ├── api/            # API interfaces
│   ├── cli/            # Command-line tools
│   ├── server/         # HTTP server
│   ├── sdk/            # SDK
│   └── observability/  # Monitoring and tracing
│
└── 📚 Documentation/
    ├── README.md
    └── [Reports]

🚀 Quick Start

Installation

# Clone repository
git clone https://github.com/your-org/sage-engine.git
cd sage-engine

# Build release version
cargo build --release

Usage

# Start service
cargo run --release --bin sage-cli

# Load model
sage-cli load /path/to/model.gguf

# Run inference
sage-cli generate "Hello, Sage!"

Code Example

use sage_core::{BasicInferenceEngine, GenerationParams, InferenceEngine};

#[tokio::main]
async fn main() -> anyhow::Result<()> {
    // Note: Model loading requires a valid model file
    // For testing, use the test utilities:
    // let model = sage_model::test_utils::create_test_model();
    // let engine = BasicInferenceEngine::new(model, 10);
    
    // Example generation parameters
    let params = GenerationParams {
        max_tokens: 128,
        temperature: 0.7,
        top_k: 40,
        top_p: 0.95,
        repetition_penalty: 1.0,
        stop_sequences: vec![],
    };
    
    println!("Sage Engine initialized with params: max_tokens={}", params.max_tokens);
    Ok(())
}

🎯 Supported Model Formats

Format	Description	Status
GGUF v3	llama.cpp universal format	✅ Full Support
Q8_0	8-bit quantization	✅ Supported
Q6_K	6-bit mixed quantization	✅ Supported
Q4_K	4-bit mixed quantization	✅ Supported
F16/F32	Half/Full precision	✅ Supported

📊 Performance Metrics

• Load Time: 0.6s (cached)
• Throughput: 8709 MB/s (OS cache)
• Memory Efficiency: 1.1 bytes/parameter
• Inference Latency: < 10µs

🤝 Contributing

Contributions are welcome! Please see CONTRIBUTING.md for details.

📄 License

MIT OR Apache-2.0

🙏 Acknowledgments

Thanks to all contributors who have helped make this project possible.

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