116 lines
5.3 KiB
Markdown
116 lines
5.3 KiB
Markdown
# MLX Server
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Native macOS app for running local LLMs on Apple Silicon via [MLX](https://github.com/ml-explore/mlx). Built with SwiftUI, it provides both a **chat UI** and an embedded **OpenAI-compatible API server**. Supports vision and tool use with automatic model swapping.
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## Supported Models
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| Alias | Model | Context | Capabilities |
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|-------|-------|---------|-------------|
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| `gemma` | `mlx-community/gemma-3-4b-it-4bit` | 128k | Vision, tool use (`tool_code` blocks) |
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| `qwen` | `mlx-community/Qwen3-VL-4B-Instruct-4bit` | 256k | Vision, tool use (`<tool_call>` tags) |
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## Quick Start
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Requires macOS 15+, Xcode 16.4+, and `xcodegen` (`brew install xcodegen`).
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```bash
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./build.sh # Debug build
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open "build/Debug/MLX Server.app"
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```
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## App Features
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- **Chat interface** with markdown rendering, image attachments (file picker, drag & drop, clipboard paste)
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- **Model picker** in toolbar with local/download status indicators
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- **Streaming responses** with live token display
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- **Status bar** showing model name, context window, tokens/sec, token counts, GPU memory, API server status
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- **Keyboard shortcuts**: `Cmd+N` (new chat), `Cmd+Return` (send), `Escape` (stop), `Cmd+1/2/3` (switch models)
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- **Settings** (`Cmd+,`): system prompt, API port, API auto-start
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## API Server
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The embedded API server (toggle in toolbar) runs on port 1234 by default. Standard OpenAI-compatible endpoints:
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- `GET /v1/models` — lists available models with `context_window` sizes
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- `POST /v1/chat/completions` — chat completions (streaming and non-streaming)
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- `GET /health` — health check
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### Model Swapping
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Send any model ID or alias in the `model` field. If it differs from the currently loaded model, the server swaps automatically:
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```bash
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curl http://localhost:1234/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{"model": "gemma", "messages": [{"role": "user", "content": "Hello"}]}'
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```
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### Vision
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Pass images as base64 data URIs in the `image_url` content part:
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```json
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{
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"model": "gemma",
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"messages": [{
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"role": "user",
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"content": [
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{"type": "text", "text": "What's in this image?"},
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{"type": "image_url", "image_url": {"url": "data:image/png;base64,..."}}
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]
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}]
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}
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```
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### Tool Use
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Pass tools in the `tools` field (OpenAI format). The server handles model-specific formatting (Gemma `tool_code` blocks, Qwen `<tool_call>` XML tags) and parses tool calls from output automatically. When tools are present during streaming, output is buffered to strip tool-call markup before sending to the client.
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## Project Structure
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```
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MLXServer/
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├── MLXServerApp.swift — App entry point, GPU cache config
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├── ContentView.swift — Main layout, toolbar, keyboard shortcuts
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├── Models/
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│ ├── ModelConfig.swift — Model definitions, alias/repoId resolution
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│ └── ChatMessage.swift — Chat message data model
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├── ViewModels/
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│ ├── ModelManager.swift — Model loading/switching via VLMModelFactory
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│ └── ChatViewModel.swift — Chat state, ChatSession, API server lifecycle
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├── Views/
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│ ├── ModelPickerView.swift — Toolbar model selector
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│ ├── ChatMessagesView.swift — Scrollable message list with markdown
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│ ├── ChatInputView.swift — Text input + image attach
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│ ├── StatusBarView.swift — Model info, tok/s, GPU memory, API status
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│ └── SettingsView.swift — System prompt + API settings
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├── Server/
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│ ├── APIServer.swift — NWListener HTTP server, SSE streaming, KV cache reuse
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│ ├── APIModels.swift — OpenAI-compatible Codable structs
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│ ├── ToolCallParser.swift — Parses tool calls from model output
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│ └── ToolPromptBuilder.swift — Model-specific tool prompt formatting
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└── Utilities/
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├── LocalModelResolver.swift — Offline-first HuggingFace cache resolution
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└── Preferences.swift — UserDefaults wrapper
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project.yml — xcodegen project spec (dependencies, settings, deployment target)
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build.sh — One-command build script (xcodegen + xcodebuild)
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```
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## Key Design Decisions
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- Uses `mlx-swift-lm` (`MLXVLM` / `VLMModelFactory`) for inference — supports both text and vision in a single model load
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- **Offline-first**: `LocalModelResolver` checks `~/.cache/huggingface/hub/` for locally-cached snapshots before downloading
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- **KV cache reuse** across API requests — reuses `ChatSession` when conversation history prefix matches
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- HTTP server built on `Network.framework` (`NWListener`) — no third-party server dependencies
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- Model-specific prompt formatting: Gemma uses `tool_code` blocks, Qwen uses `<tool_call>` XML tags
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- GPU cache limit set to 20 MB; cache cleared on model unload
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## Design Notes
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- Uses `mlx_vlm` (not `mlx_lm`) as the backend — supports both text and vision in a single model load
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- Offline-first: if the model is cached locally (`~/.cache/huggingface/hub/`), no network requests are made
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- Thread lock on generation — MLX models aren't safe for concurrent generation
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- KV prefix caching for multi-turn conversations
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- Context window read from each model's config (Gemma 3 4B: 128k, Qwen3-VL 4B: 256k) with automatic summarization fallback
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