kamiwaza-mlx 0.2.5

Unified MLX server & CLI (language and vision) with OpenAI-compatible endpoints

Kamiwaza-MLX 📦

A simple openai (chat.completions) compatible mlx server that:

• Supports both vision models (via flag or model name detection) and text-only models
• Supports streaming boolean flag
• Has a --strip-thinking which will remove tag (in both streaming and not) - good for backwards compat
• Supports usage to the client in openai style
• Prints usage on the server side output
• Appears to deliver reasonably good performance across all paths (streaming/not, vision/not)
• Has a terminal client that works with the server, which also support syntax like image:/Users/matt/path/to/image.png Describe this image in detail
• Experimental multi-node execution via mlx.distributed when PAIRED_HOST is provided

Tested largely with Qwen2.5-VL and Qwen3 models

Note: Not specific to Kamiwaza (that is, you can use on any Mac, Kamiwaza not required)

pip install kamiwaza-mlx

# start the server
a) python -m kamiwaza_mlx.server -m ./path/to/model --port 18000
# or, if you enabled the optional entry-points during install
b) kamiwaza-mlx-server -m ./path/to/model --port 18000

# chat from another terminal (note: specify --host to match server port)
python -m kamiwaza_mlx.infer --host localhost:18000 -p "Say hello"

The remainder of this README documents the original features in more detail.

MLX-LM 🦙 — Drop-in OpenAI-style API for any local MLX model

A FastAPI micro-server (server.py) that speaks the OpenAI /v1/chat/completions dialect, plus a tiny CLI client (infer.py) for quick experiments. Ideal for poking at huge models like Dracarys-72B on an M4-Max/Studio, hacking on prompts, or piping the output straight into other tools that already understand the OpenAI schema.

---

✨ Highlight reel

Feature	Details
🔌 OpenAI compatible	Same request / response JSON (streaming too) – just change the base-URL.
📦 Zero-config	Point at a local folder or HuggingFace repo (-m /path/to/model).
🖼️ Vision-ready	Accepts {"type":"image_url", …} parts & base64 URLs – works with Qwen-VL & friends.
🎥 Video-aware	Auto-extracts N key-frames with ffmpeg and feeds them as images.
🧮 Usage metrics	Prompt / completion tokens + tokens-per-second in every response.
⚙️ CLI playground	infer.py gives you a REPL with reset (Ctrl-N), verbose mode, max-token flag…

---

🚀 Running the server

# minimal
python server.py -m /var/tmp/models/mlx-community/Dracarys2-72B-Instruct-4bit

# custom port / host
python server.py -m ./Qwen2.5-VL-72B-Instruct-6bit --host 0.0.0.0 --port 12345

Default host/port: 0.0.0.0:18000

Most useful flags:

Flag	Default	What it does
-m / --model	mlx-community/Qwen2-VL-2B-Instruct-4bit	Path or HF repo.
--host	0.0.0.0	Network interface to bind to.
--port	18000	TCP port to listen on.
-V / --vision	off	Force vision pipeline; otherwise auto-detect.
--strip-thinking	off	Removes <think>…</think> blocks from model output.
--enable-prefix-caching	True	Enable automatic prompt caching for text-only models. If enabled, the server attempts to load a cache from a model-specific file in --prompt-cache-dir. If not found, it creates one from the first processed prompt and saves it.
--prompt-cache-dir	./.cache/mlx_prompt_caches/	Directory to store/load automatic prompt cache files. Cache filenames are derived from the model name.

---

KV cache flags (all KV-related CLI knobs)

System-prefix cache (system-only)

Flag	Default	What it does
--enable-prefix-caching	True	Enable system-prefix caching for text-only models.
--prompt-cache-dir	./.cache/mlx_prompt_caches/	Directory for system-prefix cache files (.safetensors, .len, .hash).
--system-cache-max-tokens	2048	Max tokens to cache from the system prompt (0 = unlimited). If the system prompt exceeds this cap, system caching is skipped.
--prefix-cache-headroom	64	Extra tokens reserved beyond the system prompt length when sizing the system cache.

Conversation cache (simple global prefix-match)

Simple mode uses a single global prefix-match KV cache. If the incoming prompt shares the previous prompt as a prefix, we skip prefill for the cached portion; otherwise the cache is reset. Conversation IDs are used for logging/metadata only (they do not create separate caches).

Flag	Default	What it does
--disable-kv-cache	off	Disable all KV caching (prefix + conversation).
--kv-cache-max-tokens	0	Per-cache upper bound (0 = min(model context, 128k)).
--kv-cache-keep	4	Tokens to keep when trimming rotating caches.
--kv-cache-idle-release-seconds	0	Release KV caches after idle time (0 disables).
--kv-cache-hard-reserve	True	Fully reserve KV memory up to sizing target.
--kv-cache-warmup	False	Run a warm-up pass at startup to materialize KV shapes.
--kv-cache-warmup-tokens	0	Warmup tokens (0 = use resolved target size).
--retain-mx-cache	False	Keep MX allocator memory (disables mx.clear_cache).

---

Experimental multi-node via mlx.distributed

The server can bootstrap a two-node mesh using mlx.distributed. Set a rendezvous host via PAIRED_HOST (optionally in a .env file) and launch each node with matching ranks/world-size. The helper will automatically read .env files passed via --distributed-env-file or located beside the server script.

# shared settings (either export or place in .env)
PAIRED_HOST=10.0.0.2
PAIRED_PORT=17863
WORLD_SIZE=2

# leader node (rank 0 hosts FastAPI)
RANK=0 python -m kamiwaza_mlx.server --distributed-env-file .env -m ./model

# worker node (rank 1 participates in mlx.distributed but does not bind HTTP)
RANK=1 python -m kamiwaza_mlx.server --distributed-env-file .env -m ./model

Useful knobs:

• --distributed – force-enable/disable distributed mode (auto when PAIRED_HOST or WORLD_SIZE>1).
• --distributed-rank / --distributed-world-size – override RANK/WORLD_SIZE env vars.
• --distributed-host / --distributed-port – override PAIRED_HOST / PAIRED_PORT.
• --distributed-server-rank – choose which rank should host the HTTP server (defaults to 0).

Non-leader ranks simply keep the MLX runtime alive for collective ops once the model weights are synchronized.

---

💬 Talking to it with the CLI

python kamiwaza_mlx/infer.py --host localhost:18000 --max_new_tokens 2048

Interactive keys

• Ctrl-N: reset conversation
• Ctrl-C: quit

---

🌐 HTTP API

GET /v1/models

Returns a list with the currently loaded model:

{
  "object": "list",
  "data": [
    {
      "id": "Dracarys2-72B-Instruct-4bit",
      "object": "model",
      "created": 1727389042,
      "owned_by": "kamiwaza"
    }
  ]
}

The created field is set when the server starts and mirrors the OpenAI API's timestamp.

POST /v1/chat/completions

{
  "model": "Dracarys2-72B-Instruct-4bit",
  "messages": [
    { "role": "user",
      "content": [
        { "type": "text", "text": "Describe this image." },
        { "type": "image_url",
          "image_url": { "url": "data:image/jpeg;base64,..." } }
      ]
    }
  ],
  "max_tokens": 512,
  "stream": false
}

Response (truncated):

{
  "id": "chatcmpl-d4c5…",
  "object": "chat.completion",
  "created": 1715242800,
  "model": "Dracarys2-72B-Instruct-4bit",
  "choices": [
    {
      "index": 0,
      "message": { "role": "assistant", "content": "The image shows…" },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 143,
    "completion_tokens": 87,
    "total_tokens": 230,
    "tokens_per_second": 32.1
  }
}

Add "stream": true and you'll get Server-Sent Events chunks followed by data: [DONE].

System Prefix Caching (Text-Only Models):

• Purpose: Dramatically speed up repeated queries that share the same system context (e.g., large document in role: system). The server caches only the system message(s), not the whole prompt, so subsequent turns process only new user tokens.
• Flags:
  • --enable-prefix-caching (default True)
  • --prompt-cache-dir (default ./.cache/mlx_prompt_caches/)
  • --system-cache-max-tokens (default 2048, 0 disables the cap)
  • --prefix-cache-headroom (default 64)
• How it works (high‑level):
  1. On first request with a system message, the server builds a KV cache for just the system portion and saves three files under --prompt-cache-dir:
     • <model>.safetensors (KV), <model>.safetensors.len (token count), <model>.safetensors.hash (SHA256 over token IDs)
  2. On subsequent requests with the same system text (hash matches), the server deep‑copies the cached KV and processes only new user/assistant tokens.
  3. If the system message changes, the old cache is discarded and replaced automatically.
  4. If the system prompt exceeds --system-cache-max-tokens, system caching is skipped and the full prompt is prefetched normally.
• Example: A 10,000‑token system document is processed once; later questions only process the user tokens.
• Notes: text‑only models; fully transparent to clients (no special fields needed).

Conversation KV Caching (Long chats, fast follow‑ups):

• Rationale: Reuse KV across turns so only the tail of the prompt is prefetched.
• Behavior:
  • Provide conversation or conversation_id (or X-Conversation-Id) if you want IDs reflected in logs/metadata. If omitted, the server uses default.
  • The server returns headers for every request (JSON & SSE):
    • X-Conv-Id (resolved ID), X-Conv-KV (fresh|hit|reset|none|busy|disabled), X-Conv-Cached-Tokens, X-Conv-Processing-Tokens.
  • Non‑stream JSON also includes usage.input_tokens_details.cached_tokens and metadata.conversation_id.
  • The global cache is capped at min(model context, 128k) unless overridden by --kv-cache-max-tokens.
  • Concurrency: only one request at a time uses KV caches. When the cache is in use, other concurrent requests run with caching disabled (X-Conv-KV: busy).
• Breaking change: the legacy /v1/conv_kv/* endpoints were removed in this branch.

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🛠️ Internals (two-sentence tour)

• server.py – loads the model with mlx-vlm, converts incoming OpenAI vision messages to the model's chat-template, handles images / video frames, and streams tokens back. For text-only models, if enabled via server flags, it automatically manages a system message cache to speed up processing when multiple queries reference the same system context.
• infer.py – lightweight REPL that keeps conversation context and shows latency / TPS stats.

That's it – drop it in front of any MLX model and start chatting!