FastMLX is a high performance production ready API to host MLX models, including Vision Language Models (VLMs) and Language Models (LMs).
- Free software: Apache Software License 2.0
- Documentation: https://Blaizzy.github.io/fastmlx
- OpenAI-compatible API: Easily integrate with existing applications that use OpenAI's API.
- Dynamic Model Loading: Load MLX models on-the-fly or use pre-loaded models for better performance.
- Support for Multiple Model Types: Compatible with various MLX model architectures.
- Image Processing Capabilities: Handle both text and image inputs for versatile model interactions.
- Efficient Resource Management: Optimized for high-performance and scalability.
- Error Handling: Robust error management for production environments.
- Customizable: Easily extendable to accommodate specific use cases and model types.
-
Installation
pip install fastmlx
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Running the Server
Start the FastMLX server:
fastmlx
or
uvicorn fastmlx:app --reload --workers 0
[!WARNING] The
--reloadflag should not be used in production. It is only intended for development purposes.For improved performance and parallel processing capabilities, you can specify either the absolute number of worker processes or the fraction of CPU cores to use. This is particularly useful for handling multiple requests simultaneously.
You can also set the
FASTMLX_NUM_WORKERSenvironment variable to specify the number of workers or the fraction of CPU cores to use.workersdefaults to 2 if not passed explicitly or set via the environment variable.In order of precedence (highest to lowest), the number of workers is determined by the following:
- Explicitly passed as a command-line argument
--workers 4will set the number of workers to 4--workers 0.5will set the number of workers to half the number of CPU cores available (minimum of 1)
- Set via the
FASTMLX_NUM_WORKERSenvironment variable - Default value of 2
To use all available CPU cores, set the value to 1.0.
Example:
fastmlx --workers 4
or
uvicorn fastmlx:app --workers 4
[!NOTE]
--reloadflag is not compatible with multiple workers- The number of workers should typically not exceed the number of CPU cores available on your machine for optimal performance.
- Stateless Application: Ensure your FastMLX application is stateless, as each worker process operates independently.
- Database Connections: If your app uses a database, make sure your connection pooling is configured to handle multiple workers.
- Resource Usage: Monitor your system's resource usage to find the optimal number of workers for your specific hardware and application needs. Additionally, you can remove any unused models using the delete model endpoint.
- Load Balancing: When running with multiple workers, incoming requests are automatically load-balanced across the worker processes.
By leveraging multiple workers, you can significantly improve the throughput and responsiveness of your FastMLX application, especially under high load conditions.
- Explicitly passed as a command-line argument
-
Making API Calls
Use the API similar to OpenAI's chat completions:
Vision Language Model
import requests import json url = "http://localhost:8000/v1/chat/completions" headers = {"Content-Type": "application/json"} data = { "model": "mlx-community/nanoLLaVA-1.5-4bit", "image": "http://images.cocodataset.org/val2017/000000039769.jpg", "messages": [{"role": "user", "content": "What are these"}], "max_tokens": 100 } response = requests.post(url, headers=headers, data=json.dumps(data)) print(response.json())
With streaming:
import requests import json def process_sse_stream(url, headers, data): response = requests.post(url, headers=headers, json=data, stream=True) if response.status_code != 200: print(f"Error: Received status code {response.status_code}") print(response.text) return full_content = "" try: for line in response.iter_lines(): if line: line = line.decode('utf-8') if line.startswith('data: '): event_data = line[6:] # Remove 'data: ' prefix if event_data == '[DONE]': print("\nStream finished. ✅") break try: chunk_data = json.loads(event_data) content = chunk_data['choices'][0]['delta']['content'] full_content += content print(content, end='', flush=True) except json.JSONDecodeError: print(f"\nFailed to decode JSON: {event_data}") except KeyError: print(f"\nUnexpected data structure: {chunk_data}") except KeyboardInterrupt: print("\nStream interrupted by user.") except requests.exceptions.RequestException as e: print(f"\nAn error occurred: {e}") if __name__ == "__main__": url = "http://localhost:8000/v1/chat/completions" headers = {"Content-Type": "application/json"} data = { "model": "mlx-community/nanoLLaVA-1.5-4bit", "image": "http://images.cocodataset.org/val2017/000000039769.jpg", "messages": [{"role": "user", "content": "What are these?"}], "max_tokens": 500, "stream": True } process_sse_stream(url, headers, data)
Language Model
import requests import json url = "http://localhost:8000/v1/chat/completions" headers = {"Content-Type": "application/json"} data = { "model": "mlx-community/gemma-2-9b-it-4bit", "messages": [{"role": "user", "content": "What is the capital of France?"}], "max_tokens": 100 } response = requests.post(url, headers=headers, data=json.dumps(data)) print(response.json())
With streaming:
import requests import json def process_sse_stream(url, headers, data): response = requests.post(url, headers=headers, json=data, stream=True) if response.status_code != 200: print(f"Error: Received status code {response.status_code}") print(response.text) return full_content = "" try: for line in response.iter_lines(): if line: line = line.decode('utf-8') if line.startswith('data: '): event_data = line[6:] # Remove 'data: ' prefix if event_data == '[DONE]': print("\nStream finished. ✅") break try: chunk_data = json.loads(event_data) content = chunk_data['choices'][0]['delta']['content'] full_content += content print(content, end='', flush=True) except json.JSONDecodeError: print(f"\nFailed to decode JSON: {event_data}") except KeyError: print(f"\nUnexpected data structure: {chunk_data}") except KeyboardInterrupt: print("\nStream interrupted by user.") except requests.exceptions.RequestException as e: print(f"\nAn error occurred: {e}") if __name__ == "__main__": url = "http://localhost:8000/v1/chat/completions" headers = {"Content-Type": "application/json"} data = { "model": "mlx-community/gemma-2-9b-it-4bit", "messages": [{"role": "user", "content": "Hi, how are you?"}], "max_tokens": 500, "stream": True } process_sse_stream(url, headers, data)
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Function Calling
FastMLX now supports tool calling in accordance with the OpenAI API specification. This feature is available for the following models:
- Llama 3.1
- Arcee Agent
- C4ai-Command-R-Plus
- Firefunction
- xLAM
Supported modes:
- Without Streaming
- Parallel Tool Calling
Note: Tool choice and OpenAI-compliant streaming for function calling are currently under development.
Here's an example of how to use function calling with FastMLX:
import requests import json url = "http://localhost:8000/v1/chat/completions" headers = {"Content-Type": "application/json"} data = { "model": "mlx-community/Meta-Llama-3.1-8B-Instruct-8bit", "messages": [ { "role": "user", "content": "What's the weather like in San Francisco and Washington?" } ], "tools": [ { "name": "get_current_weather", "description": "Get the current weather", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA" }, "format": { "type": "string", "enum": ["celsius", "fahrenheit"], "description": "The temperature unit to use. Infer this from the user's location." } }, "required": ["location", "format"] } } ], "max_tokens": 150, "temperature": 0.7, "stream": False, } response = requests.post(url, headers=headers, data=json.dumps(data)) print(response.json())
This example demonstrates how to use the
get_current_weathertool with the Llama 3.1 model. The API will process the user's question and use the provided tool to fetch the required information.Please note that while streaming is available for regular text generation, the streaming implementation for function calling is still in development and does not yet fully comply with the OpenAI specification.
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Listing Available Models
To see all vision and language models supported by MLX:
import requests url = "http://localhost:8000/v1/supported_models" response = requests.get(url) print(response.json())
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List Available Models
You can add new models to the API:
import requests url = "http://localhost:8000/v1/models" params = { "model_name": "hf-repo-or-path", } response = requests.post(url, params=params) print(response.json())
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Listing Available Models
To see all available models:
import requests url = "http://localhost:8000/v1/models" response = requests.get(url) print(response.json())
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Delete Models
To remove any models loaded to memory:
import requests url = "http://localhost:8000/v1/models" params = { "model_name": "hf-repo-or-path", } response = requests.delete(url, params=params) print(response)
For more detailed usage instructions and API documentation, please refer to the full documentation.
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