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

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
python -m kamiwaza_mlx.infer -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

🚀 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:

💬 Talking to it with the CLI

python infer.py --base-url http://localhost:18000/v1 -v --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/)
• 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.
• 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: For whole conversations, we reuse KV across turns and tokenize only the tail. We also honor message boundaries so rollbacks (dropping a turn) are fast: we trim to the prior boundary and continue.
• Enabling & behavior:
  • Conversation KV cache is on by default. Provide a conversation or conversation_id in the request body (or X-Conversation-Id header). If omitted, auto‑ID binds by client IP.
  • The server returns headers for every request (JSON & SSE):
    • X-Conv-Id (resolved ID), X-Conv-KV (fresh|hit|snapshot|none|disabled), X-Conv-Cached-Tokens, X-Conv-Processing-Tokens.
  • Non‑stream JSON also includes usage.input_tokens_details.cached_tokens and metadata.conversation_id.
  • Default capacity: --conversation-kv-max-tokens 131072 (128k; clamped to model context if detected). Snapshots: --conversation-snapshots 1.
• Save/Load (manual only):
  • Save a conversation KV & metadata for later: POST /v1/conv_kv/save with {conversation|conversation_id, title?}.
  • Load it back into memory: POST /v1/conv_kv/load with {conversation|conversation_id}.
  • List/delete saved KV: GET /v1/conv_kv/stats, DELETE /v1/conv_kv/{id}.
  • Safety: per‑save hard limit (--conversation-disk-max-gb, default 200 GiB) and 90% disk occupancy guard.

For a deeper dive (headers, examples, and endpoints), see kv-cache-dev-guide.md.

🛠️ 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!