MarkBase Admin 4454f685f5
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Add complete model testing report (E4B, 12B, 31B, E2B)
Test Results Summary:
- E4B-MarkBase: 42 layers, 2560 hidden, multimodal (Audio+Vision), 42.8 tok/s
- 12B: 48 layers, 3840 hidden, pure text, ~26 tok/s
- 31B: 60 layers, 5376 hidden, 64 heads, largest model, stable
- E2B: 48 layers, 3840 hidden, per-layer architecture, Audio tower 12 layers

Performance:
- All models: 0 NaN (perfect stability)
- Speed ranking: E4B > 12B/E2B > 31B
- Capacity ranking: 31B > 12B/E2B > E4B

Recommendations:
- Multimodal → E4B-MarkBase (only option)
- Speed → E4B-MarkBase (42.8 tok/s)
- Quality → 31B (60 layers, highest capacity)
- Balance → 12B or E2B
- Code generation → Need specialized model

Tests: 15/15 passed (0 unexpected failures)
2026-06-23 20:48:29 +08:00

MarkBase

高性能 Swift Metal 多模態推理引擎,專為 Apple Silicon 優化。

功能特性

  • 純 Swift Metal - 無外部依賴
  • 4-bit 量化 - 高效內存使用
  • OpenAI 兼容 API - REST + SSE
  • 多模態支持 - 文本、圖片、音訊
  • 流式輸出 - 實時 token 生成
  • SIMD 優化 - 17x attention, 3x matmul 提升

快速開始

安裝

git clone <repository-url>
cd MarkBase12B
swift build

啟動服務器

# 基本啟動
swift run G12BServer ./model

# 指定端口和模型 ID
swift run G12BServer ./model 8080 markbase-12b

# 運行性能基準測試
swift run G12BServer ./model markbase --benchmark

API 使用

健康檢查

curl http://localhost:8080/health

文本生成

curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {"role": "user", "content": "Hello, how are you?"}
    ],
    "max_tokens": 100,
    "temperature": 0.7
  }'

流式輸出

curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {"role": "user", "content": "Tell me a story"}
    ],
    "stream": true
  }'

多模態(圖片)

curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {
        "role": "user",
        "content": [
          {"type": "text", "text": "描述這張圖片"},
          {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,..."}}
        ]
      }
    ]
  }'

Swift SDK 使用

import G12B

// 初始化引擎
let engine = try MarkBaseEngine(autoCompile: true)

// 加載模型
let model = try E4BModel(modelDir: "./model", engine: engine)

// 生成文本
let logits = try model.forward(tokenId: 0, position: 0)

性能

模型 速度 內存
E4B (4B) 19.7 tok/s ~3GB
12B 18.8 tok/s ~9GB

架構

MarkBase12B/
├── Sources/G12B/
│   ├── Engine.swift           # Metal 引擎
│   ├── Model.swift            # 模型實現
│   ├── Tokenizer/             # Tokenizer
│   ├── Generator/             # 文本生成
│   ├── Sampling/              # 採樣策略
│   ├── Audio/                 # 音訊塔
│   ├── Vision/                # 視覺塔
│   ├── Metal/                 # Metal Kernels
│   └── BufferPool.swift       # Buffer 池
├── Sources/G12BServer/
│   ├── APIServer.swift        # API 服務器
│   ├── MarkBaseServer.swift   # 服務器實現
│   ├── SSE.swift              # SSE 支持
│   ├── Errors.swift           # 錯誤處理
│   ├── MultimodalAPI.swift    # 多模態 API
│   └── PerformanceBenchmark.swift
└── Tests/G12BTests/

文檔

授權

MIT License

S
Description
MarkBaseEngine - Swift Metal multimodal inference engine for Apple Silicon
Readme 1.4 MiB
Languages
Swift 79%
Metal 20.7%
Shell 0.3%