| Name | mlx-omni-server JSON |
| Version |
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| Summary | MLX Omni Server is a server that provides OpenAI-compatible APIs using Apple's MLX framework. |
| upload_time | 2025-08-20 01:48:36 |
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| author | None |
| requires_python | >=3.11 |
| license | None |
| keywords |
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ai
aigc
mlx
openai
server
stt
tts
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# MLX Omni Server
[](https://pypi.python.org/pypi/mlx-omni-server)
[](https://deepwiki.com/madroidmaq/mlx-omni-server)
MLX Omni Server is a local inference server powered by Apple's MLX framework, specifically designed for Apple Silicon (M-series) chips. It implements
OpenAI-compatible API endpoints, enabling seamless integration with existing OpenAI SDK clients while leveraging the power of local ML inference.
## Features
- 🚀 **Apple Silicon Optimized**: Built on MLX framework, optimized for M1/M2/M3/M4 series chips
- 🔌 **OpenAI API Compatible**: Drop-in replacement for OpenAI API endpoints
- 🎯 **Multiple AI Capabilities**:
- Audio Processing (TTS & STT)
- Chat Completion
- Image Generation
- ⚡ **High Performance**: Local inference with hardware acceleration
- 🔐 **Privacy-First**: All processing happens locally on your machine
- 🛠 **SDK Support**: Works with official OpenAI SDK and other compatible clients
## Supported API Endpoints
The server implements OpenAI-compatible endpoints:
- [Chat completions](https://platform.openai.com/docs/api-reference/chat): `/v1/chat/completions`
- ✅ Chat
- ✅ Tools, Function Calling
- ✅ Structured Output
- ✅ LogProbs
- 🚧 Vision
- [Audio](https://platform.openai.com/docs/api-reference/audio)
- ✅ `/v1/audio/speech` - Text-to-Speech
- ✅ `/v1/audio/transcriptions` - Speech-to-Text
- [Models](https://platform.openai.com/docs/api-reference/models/list)
- ✅ `/v1/models` - List models
- ✅ `/v1/models/{model}` - Retrieve or Delete model
- [Images](https://platform.openai.com/docs/api-reference/images)
- ✅ `/v1/images/generations` - Image generation
- [Embeddings](https://platform.openai.com/docs/api-reference/embeddings)
- ✅ `/v1/embeddings` - Create embeddings for text
## Quick Start
Follow these simple steps to get started with MLX Omni Server:
1. Install the package
```bash
pip install mlx-omni-server
```
2. Start the server
```bash
mlx-omni-server
```
3. Run a simple chat example using curl
```bash
curl http://localhost:10240/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "mlx-community/gemma-3-1b-it-4bit-DWQ",
"messages": [
{
"role": "user",
"content": "What can you do?"
}
]
}'
```
That's it! You're now running AI locally on your Mac. See [Advanced Usage](#advanced-usage) for more examples.
### Server Options
```bash
# Start with default settings (port 10240)
mlx-omni-server
# Or specify a custom port
mlx-omni-server --port 8000
# View all available options
mlx-omni-server --help
```
### Basic Client Setup
```python
from openai import OpenAI
# Connect to your local server
client = OpenAI(
base_url="http://localhost:10240/v1", # Point to local server
api_key="not-needed" # API key not required
)
# Make a simple chat request
response = client.chat.completions.create(
model="mlx-community/gemma-3-1b-it-4bit-DWQ",
messages=[{"role": "user", "content": "Hello, how are you?"}]
)
print(response.choices[0].message.content)
```
## Advanced Usage
MLX Omni Server supports multiple ways of interaction and various AI capabilities. Here's how to use each:
### API Usage Options
MLX Omni Server provides flexible ways to interact with AI capabilities:
#### REST API
Access the server directly using HTTP requests:
```bash
# Chat completions endpoint
curl http://localhost:10240/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "mlx-community/gemma-3-1b-it-4bit-DWQ",
"messages": [{"role": "user", "content": "Hello"}]
}'
# Get available models
curl http://localhost:10240/v1/models
```
#### OpenAI SDK
Use the official OpenAI Python SDK for seamless integration:
```python
from openai import OpenAI
client = OpenAI(
base_url="http://localhost:10240/v1", # Point to local server
api_key="not-needed" # API key not required for local server
)
```
See the FAQ section for information on using TestClient for development.
### API Examples
#### Chat Completion
```python
response = client.chat.completions.create(
model="mlx-community/Llama-3.2-3B-Instruct-4bit",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello!"}
],
temperature=0,
stream=True # this time, we set stream=True
)
for chunk in response:
print(chunk)
print(chunk.choices[0].delta.content)
print("****************")
```
<details>
<summary>Curl Example</summary>
```shell
curl http://localhost:10240/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "mlx-community/Llama-3.2-3B-Instruct-4bit",
"stream": true,
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "Hello!"
}
]
}'
```
</details>
#### Text-to-Speech
```python
speech_file_path = "mlx_example.wav"
response = client.audio.speech.create(
model="lucasnewman/f5-tts-mlx",
voice="alloy", # voice si not working for now
input="MLX project is awsome.",
)
response.stream_to_file(speech_file_path)
```
<details>
<summary>Curl Example</summary>
```shell
curl -X POST "http://localhost:10240/v1/audio/speech" \
-H "Content-Type: application/json" \
-d '{
"model": "lucasnewman/f5-tts-mlx",
"input": "MLX project is awsome",
"voice": "alloy"
}' \
--output ~/Desktop/mlx.wav
```
</details>
#### Speech-to-Text
```python
audio_file = open("speech.mp3", "rb")
transcript = client.audio.transcriptions.create(
model="mlx-community/whisper-large-v3-turbo",
file=audio_file
)
print(transcript.text)
```
<details>
<summary>Curl Example</summary>
```shell
curl -X POST "http://localhost:10240/v1/audio/transcriptions" \
-H "Content-Type: multipart/form-data" \
-F "file=@mlx_example.wav" \
-F "model=mlx-community/whisper-large-v3-turbo"
```
Response:
```json
{
"text": " MLX Project is awesome!"
}
```
</details>
#### Image Generation
```python
image_response = client.images.generate(
model="argmaxinc/mlx-FLUX.1-schnell",
prompt="A serene landscape with mountains and a lake",
n=1,
size="512x512"
)
```
<details>
<summary>Curl Example</summary>
```shell
curl http://localhost:10240/v1/images/generations \
-H "Content-Type: application/json" \
-d '{
"model": "argmaxinc/mlx-FLUX.1-schnell",
"prompt": "A cute baby sea otter",
"n": 1,
"size": "1024x1024"
}'
```
</details>
#### Embeddings
```python
# Generate embedding for a single text
response = client.embeddings.create(
model="mlx-community/all-MiniLM-L6-v2-4bit", input="I like reading"
)
# Examine the response structure
print(f"Response type: {type(response)}")
print(f"Model used: {response.model}")
print(f"Embedding dimension: {len(response.data[0].embedding)}")
```
<details>
<summary>Curl Example</summary>
```shell
curl http://localhost:10240/v1/embeddings \
-H "Content-Type: application/json" \
-d '{
"model": "mlx-community/all-MiniLM-L6-v2-4bit",
"input": ["Hello world!", "Embeddings are useful for semantic search."]
}'
```
</details>
For more detailed examples, check out the [examples](examples) directory.
## FAQ
### How are models managed?
MLX Omni Server uses Hugging Face for model downloading and management. When you specify a model ID that hasn't been downloaded yet, the framework will automatically download it. However, since download times can vary significantly:
- It's recommended to pre-download models through Hugging Face before using them in your service
- To use a locally downloaded model, simply set the `model` parameter to the local model path
```python
# Using a model from Hugging Face
response = client.chat.completions.create(
model="mlx-community/gemma-3-1b-it-4bit-DWQ", # Will download if not available
messages=[{"role": "user", "content": "Hello"}]
)
# Using a local model
response = client.chat.completions.create(
model="/path/to/your/local/model", # Local model path
messages=[{"role": "user", "content": "Hello"}]
)
```
The models currently supported on the machine can also be accessed through the following methods
```bash
curl http://localhost:10240/v1/models
```
### How do I specify which model to use?
Use the `model` parameter when creating a request:
```python
response = client.chat.completions.create(
model="mlx-community/gemma-3-1b-it-4bit-DWQ", # Specify model here
messages=[{"role": "user", "content": "Hello"}]
)
```
### Can I use TestClient for development?
Yes, TestClient allows you to use the OpenAI client without starting a local server. This is particularly useful for development and testing scenarios:
```python
from openai import OpenAI
from fastapi.testclient import TestClient
from mlx_omni_server.main import app
# Use TestClient directly - no network service needed
client = OpenAI(
http_client=TestClient(app)
)
# Now you can use the client just like with a running server
response = client.chat.completions.create(
model="mlx-community/gemma-3-1b-it-4bit-DWQ",
messages=[{"role": "user", "content": "Hello"}]
)
```
This approach bypasses the HTTP server entirely, making it ideal for unit testing and quick development iterations.
### What if I get errors when starting the server?
- Confirm you're using an Apple Silicon Mac (M1/M2/M3/M4)
- Check that your Python version is 3.9 or higher
- Verify you have the latest version of mlx-omni-server installed
- Check the log output for more detailed error information
## Contributing
We welcome contributions! If you're interested in contributing to MLX Omni Server, please check out our [Development Guide](docs/development_guide.md)
for detailed information about:
- Setting up the development environment
- Running the server in development mode
- Contributing guidelines
- Testing and documentation
For major changes, please open an issue first to discuss what you would like to change.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Acknowledgments
- Built with [MLX](https://github.com/ml-explore/mlx) by Apple
- API design inspired by [OpenAI](https://openai.com)
- Uses [FastAPI](https://fastapi.tiangolo.com/) for the server implementation
- Chat(text generation) by [mlx-lm](https://github.com/ml-explore/mlx-examples/tree/main/llms/mlx_lm)
- Image generation by [mflux](https://github.com/filipstrand/mflux)
- Text-to-Speech by [lucasnewman/f5-tts-mlx](https://github.com/lucasnewman/f5-tts-mlx) & [Blaizzy/mlx-audio](https://github.com/Blaizzy/mlx-audio)
- Speech-to-Text by [mlx-whisper](https://github.com/ml-explore/mlx-examples/blob/main/whisper/README.md)
- Embeddings by [mlx-embeddings](https://github.com/Blaizzy/mlx-embeddings)
## Disclaimer
This project is not affiliated with or endorsed by OpenAI or Apple. It's an independent implementation that provides OpenAI-compatible APIs using
Apple's MLX framework.
## Star History 🌟
[](https://star-history.com/#madroidmaq/mlx-omni-server&Date)
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"description": "# MLX Omni Server\n\n[](https://pypi.python.org/pypi/mlx-omni-server)\n[](https://deepwiki.com/madroidmaq/mlx-omni-server)\n\n\n\nMLX Omni Server is a local inference server powered by Apple's MLX framework, specifically designed for Apple Silicon (M-series) chips. It implements\nOpenAI-compatible API endpoints, enabling seamless integration with existing OpenAI SDK clients while leveraging the power of local ML inference.\n\n## Features\n\n- \ud83d\ude80 **Apple Silicon Optimized**: Built on MLX framework, optimized for M1/M2/M3/M4 series chips\n- \ud83d\udd0c **OpenAI API Compatible**: Drop-in replacement for OpenAI API endpoints\n- \ud83c\udfaf **Multiple AI Capabilities**:\n - Audio Processing (TTS & STT)\n - Chat Completion\n - Image Generation\n- \u26a1 **High Performance**: Local inference with hardware acceleration\n- \ud83d\udd10 **Privacy-First**: All processing happens locally on your machine\n- \ud83d\udee0 **SDK Support**: Works with official OpenAI SDK and other compatible clients\n\n## Supported API Endpoints\n\nThe server implements OpenAI-compatible endpoints:\n\n- [Chat completions](https://platform.openai.com/docs/api-reference/chat): `/v1/chat/completions`\n - \u2705 Chat\n - \u2705 Tools, Function Calling\n - \u2705 Structured Output\n - \u2705 LogProbs\n - \ud83d\udea7 Vision\n- [Audio](https://platform.openai.com/docs/api-reference/audio)\n - \u2705 `/v1/audio/speech` - Text-to-Speech\n - \u2705 `/v1/audio/transcriptions` - Speech-to-Text\n- [Models](https://platform.openai.com/docs/api-reference/models/list)\n - \u2705 `/v1/models` - List models\n - \u2705 `/v1/models/{model}` - Retrieve or Delete model\n- [Images](https://platform.openai.com/docs/api-reference/images)\n - \u2705 `/v1/images/generations` - Image generation\n- [Embeddings](https://platform.openai.com/docs/api-reference/embeddings)\n - \u2705 `/v1/embeddings` - Create embeddings for text\n\n\n\n## Quick Start\n\nFollow these simple steps to get started with MLX Omni Server:\n\n1. Install the package\n\n```bash\npip install mlx-omni-server\n```\n\n2. Start the server\n\n```bash\nmlx-omni-server\n```\n\n3. Run a simple chat example using curl\n\n```bash\ncurl http://localhost:10240/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"mlx-community/gemma-3-1b-it-4bit-DWQ\",\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": \"What can you do?\"\n }\n ]\n }'\n```\n\nThat's it! You're now running AI locally on your Mac. See [Advanced Usage](#advanced-usage) for more examples.\n\n### Server Options\n\n```bash\n# Start with default settings (port 10240)\nmlx-omni-server\n\n# Or specify a custom port\nmlx-omni-server --port 8000\n\n# View all available options\nmlx-omni-server --help\n```\n\n### Basic Client Setup\n\n```python\nfrom openai import OpenAI\n\n# Connect to your local server\nclient = OpenAI(\n base_url=\"http://localhost:10240/v1\", # Point to local server\n api_key=\"not-needed\" # API key not required\n)\n\n# Make a simple chat request\nresponse = client.chat.completions.create(\n model=\"mlx-community/gemma-3-1b-it-4bit-DWQ\",\n messages=[{\"role\": \"user\", \"content\": \"Hello, how are you?\"}]\n)\nprint(response.choices[0].message.content)\n```\n\n## Advanced Usage\n\nMLX Omni Server supports multiple ways of interaction and various AI capabilities. Here's how to use each:\n\n### API Usage Options\n\nMLX Omni Server provides flexible ways to interact with AI capabilities:\n\n#### REST API\n\nAccess the server directly using HTTP requests:\n\n```bash\n# Chat completions endpoint\ncurl http://localhost:10240/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"mlx-community/gemma-3-1b-it-4bit-DWQ\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Hello\"}]\n }'\n\n# Get available models\ncurl http://localhost:10240/v1/models\n```\n\n#### OpenAI SDK\n\nUse the official OpenAI Python SDK for seamless integration:\n\n```python\nfrom openai import OpenAI\n\nclient = OpenAI(\n base_url=\"http://localhost:10240/v1\", # Point to local server\n api_key=\"not-needed\" # API key not required for local server\n)\n```\n\nSee the FAQ section for information on using TestClient for development.\n\n\n\n### API Examples\n\n#### Chat Completion\n\n```python\nresponse = client.chat.completions.create(\n model=\"mlx-community/Llama-3.2-3B-Instruct-4bit\",\n messages=[\n {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n {\"role\": \"user\", \"content\": \"Hello!\"}\n ],\n temperature=0,\n stream=True # this time, we set stream=True\n)\n\nfor chunk in response:\n print(chunk)\n print(chunk.choices[0].delta.content)\n print(\"****************\")\n```\n\n<details>\n<summary>Curl Example</summary>\n\n```shell\ncurl http://localhost:10240/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"mlx-community/Llama-3.2-3B-Instruct-4bit\",\n \"stream\": true,\n \"messages\": [\n {\n \"role\": \"system\",\n \"content\": \"You are a helpful assistant.\"\n },\n {\n \"role\": \"user\",\n \"content\": \"Hello!\"\n }\n ]\n }'\n```\n\n</details>\n\n#### Text-to-Speech\n\n```python\nspeech_file_path = \"mlx_example.wav\"\nresponse = client.audio.speech.create(\n model=\"lucasnewman/f5-tts-mlx\",\n voice=\"alloy\", # voice si not working for now\n input=\"MLX project is awsome.\",\n)\nresponse.stream_to_file(speech_file_path)\n```\n\n\n<details>\n<summary>Curl Example</summary>\n\n```shell\ncurl -X POST \"http://localhost:10240/v1/audio/speech\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"lucasnewman/f5-tts-mlx\",\n \"input\": \"MLX project is awsome\",\n \"voice\": \"alloy\"\n }' \\\n --output ~/Desktop/mlx.wav\n```\n\n</details>\n\n#### Speech-to-Text\n\n```python\naudio_file = open(\"speech.mp3\", \"rb\")\ntranscript = client.audio.transcriptions.create(\n model=\"mlx-community/whisper-large-v3-turbo\",\n file=audio_file\n)\n\nprint(transcript.text)\n```\n\n<details>\n<summary>Curl Example</summary>\n\n```shell\ncurl -X POST \"http://localhost:10240/v1/audio/transcriptions\" \\\n -H \"Content-Type: multipart/form-data\" \\\n -F \"file=@mlx_example.wav\" \\\n -F \"model=mlx-community/whisper-large-v3-turbo\"\n```\n\nResponse:\n\n```json\n{\n \"text\": \" MLX Project is awesome!\"\n}\n```\n\n</details>\n\n\n#### Image Generation\n\n```python\nimage_response = client.images.generate(\n model=\"argmaxinc/mlx-FLUX.1-schnell\",\n prompt=\"A serene landscape with mountains and a lake\",\n n=1,\n size=\"512x512\"\n)\n\n```\n\n<details>\n<summary>Curl Example</summary>\n\n```shell\ncurl http://localhost:10240/v1/images/generations \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"argmaxinc/mlx-FLUX.1-schnell\",\n \"prompt\": \"A cute baby sea otter\",\n \"n\": 1,\n \"size\": \"1024x1024\"\n }'\n\n```\n\n</details>\n\n#### Embeddings\n\n```python\n# Generate embedding for a single text\nresponse = client.embeddings.create(\n model=\"mlx-community/all-MiniLM-L6-v2-4bit\", input=\"I like reading\"\n)\n\n# Examine the response structure\nprint(f\"Response type: {type(response)}\")\nprint(f\"Model used: {response.model}\")\nprint(f\"Embedding dimension: {len(response.data[0].embedding)}\")\n```\n\n<details>\n<summary>Curl Example</summary>\n\n```shell\ncurl http://localhost:10240/v1/embeddings \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"mlx-community/all-MiniLM-L6-v2-4bit\",\n \"input\": [\"Hello world!\", \"Embeddings are useful for semantic search.\"]\n }'\n```\n\n</details>\n\n\nFor more detailed examples, check out the [examples](examples) directory.\n\n## FAQ\n\n\n### How are models managed?\n\nMLX Omni Server uses Hugging Face for model downloading and management. When you specify a model ID that hasn't been downloaded yet, the framework will automatically download it. However, since download times can vary significantly:\n\n- It's recommended to pre-download models through Hugging Face before using them in your service\n- To use a locally downloaded model, simply set the `model` parameter to the local model path\n\n```python\n# Using a model from Hugging Face\nresponse = client.chat.completions.create(\n model=\"mlx-community/gemma-3-1b-it-4bit-DWQ\", # Will download if not available\n messages=[{\"role\": \"user\", \"content\": \"Hello\"}]\n)\n\n# Using a local model\nresponse = client.chat.completions.create(\n model=\"/path/to/your/local/model\", # Local model path\n messages=[{\"role\": \"user\", \"content\": \"Hello\"}]\n)\n```\n\nThe models currently supported on the machine can also be accessed through the following methods\n\n```bash\ncurl http://localhost:10240/v1/models\n```\n\n\n### How do I specify which model to use?\n\nUse the `model` parameter when creating a request:\n\n```python\nresponse = client.chat.completions.create(\n model=\"mlx-community/gemma-3-1b-it-4bit-DWQ\", # Specify model here\n messages=[{\"role\": \"user\", \"content\": \"Hello\"}]\n)\n```\n\n\n### Can I use TestClient for development?\n\nYes, TestClient allows you to use the OpenAI client without starting a local server. This is particularly useful for development and testing scenarios:\n\n```python\nfrom openai import OpenAI\nfrom fastapi.testclient import TestClient\nfrom mlx_omni_server.main import app\n\n# Use TestClient directly - no network service needed\nclient = OpenAI(\n http_client=TestClient(app)\n)\n\n# Now you can use the client just like with a running server\nresponse = client.chat.completions.create(\n model=\"mlx-community/gemma-3-1b-it-4bit-DWQ\",\n messages=[{\"role\": \"user\", \"content\": \"Hello\"}]\n)\n```\n\nThis approach bypasses the HTTP server entirely, making it ideal for unit testing and quick development iterations.\n\n\n### What if I get errors when starting the server?\n\n- Confirm you're using an Apple Silicon Mac (M1/M2/M3/M4)\n- Check that your Python version is 3.9 or higher\n- Verify you have the latest version of mlx-omni-server installed\n- Check the log output for more detailed error information\n\n\n## Contributing\n\nWe welcome contributions! If you're interested in contributing to MLX Omni Server, please check out our [Development Guide](docs/development_guide.md)\nfor detailed information about:\n\n- Setting up the development environment\n- Running the server in development mode\n- Contributing guidelines\n- Testing and documentation\n\nFor major changes, please open an issue first to discuss what you would like to change.\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## Acknowledgments\n\n- Built with [MLX](https://github.com/ml-explore/mlx) by Apple\n- API design inspired by [OpenAI](https://openai.com)\n- Uses [FastAPI](https://fastapi.tiangolo.com/) for the server implementation\n- Chat(text generation) by [mlx-lm](https://github.com/ml-explore/mlx-examples/tree/main/llms/mlx_lm)\n- Image generation by [mflux](https://github.com/filipstrand/mflux)\n- Text-to-Speech by [lucasnewman/f5-tts-mlx](https://github.com/lucasnewman/f5-tts-mlx) & [Blaizzy/mlx-audio](https://github.com/Blaizzy/mlx-audio)\n- Speech-to-Text by [mlx-whisper](https://github.com/ml-explore/mlx-examples/blob/main/whisper/README.md)\n- Embeddings by [mlx-embeddings](https://github.com/Blaizzy/mlx-embeddings)\n\n## Disclaimer\n\nThis project is not affiliated with or endorsed by OpenAI or Apple. It's an independent implementation that provides OpenAI-compatible APIs using\nApple's MLX framework.\n\n## Star History \ud83c\udf1f\n\n[](https://star-history.com/#madroidmaq/mlx-omni-server&Date)\n",
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