Local Copilot with SLM

Photo by ZHENYU LUO on Unsplash

What is a Copilot?

A copilot in the context of software development and artificial intelligence refers to an AI-powered assistant that helps users by providing suggestions, automating repetitive tasks, and enhancing productivity. These copilots can be integrated into various applications, such as code editors, customer service platforms, or personal productivity tools, to provide real-time assistance and insights.

Benefits of a Copilot

1. Increased Productivity:

• Copilots can automate repetitive tasks, allowing users to focus on more complex and creative aspects of their work.

1. Real-time Assistance:

• Provides instant suggestions and corrections, reducing the time spent on debugging and error correction.

1. Knowledge Enhancement:

• Offers context-aware suggestions that help users learn and apply best practices, improving their skills over time.

1. Consistency:

• Ensures consistent application of coding standards, style guides, and other best practices across projects.

What is a Local Copilot?

A local copilot is a variant of AI copilots that runs entirely on local compute resources rather than relying on cloud-based services. This setup involves deploying smaller, yet powerful, language models on local machines.

Benefits of a Local Copilot

1. Privacy and Security:

• Running models locally ensures that sensitive data does not leave the user's environment, mitigating risks associated with data breaches and unauthorized access.

1. Reduced Latency:

• Local execution eliminates the need for data transmission to and from remote servers, resulting in faster response times.

1. Offline Functionality:

• Local copilots can operate without an internet connection, making them reliable even in environments with limited or no internet access.

1. Cost Efficiency:

• Avoids the costs associated with cloud-based services and data storage.

How to Implement a Local Copilot

Implementing a local copilot involves selecting a smaller language model, optimizing it to fit on local hardware, and integrating it with a framework like LangChain to build and run AI agents. Here are the high-level steps:

1. Model Selection:

• Choose a language model that has 8 billion parameters or less.

1. Optimization with TensorRT:

• Quantize and optimize the model using NVIDIA TensorRT-LLM to reduce its size and ensure it fits on your GPU.

1. Integration with LangChain:

• Use the LangChain framework to build and manage the AI agents that will run locally.

1. Deployment:

• Deploy the optimized model on local compute resources, ensuring it can handle the tasks required by the copilot.

By leveraging local compute resources and optimized language models, you can create a robust, privacy-conscious, and efficient local copilot to assist with various tasks and enhance productivity.

To develop a local copilot using smaller language models with LangChain and NVIDIA TensorRT-LLM, follow these steps:

Step-by-Step Guide

1. Set Up Your Environment

2. Install Required Libraries:

Ensure you have Python installed and then install the necessary libraries:

   pip install langchain nvidia-pyindex nvidia-tensorrt

1. Prepare Your GPU:

Make sure your system has an NVIDIA GPU and CUDA drivers installed. You'll also need TensorRT libraries which can be installed via the NVIDIA package index:

   sudo apt-get install nvidia-cuda-toolkit

   sudo apt-get install tensorrt

1. Model Preparation

2. Select a Smaller Language Model:

Choose a language model that has 8 billion parameters or less. You can find many such models on platforms like Hugging Face.

1. Quantize the Model Using NVIDIA TensorRT-LLM:

Use TensorRT to optimize and quantize the model to make it fit on your GPU.

   import tensorrt as trt



   # Load your model here

   model = load_your_model_function()



   # Create a TensorRT engine

   builder = trt.Builder(trt.Logger(trt.Logger.WARNING))

   network = builder.create_network()

   parser = trt.OnnxParser(network, trt.Logger(trt.Logger.WARNING))



   with open("your_model.onnx", "rb") as f:

       parser.parse(f.read())



   engine = builder.build_cuda_engine(network)

1. Integrate with LangChain

2. Set Up LangChain:

Create a LangChain project and configure it to use your local model.

   from langchain import LangChain, LanguageModel



   # Assuming you have a function to load your TensorRT engine

   def load_trt_engine(engine_path):

       with open(engine_path, "rb") as f, trt.Runtime(trt.Logger(trt.Logger.WARNING)) as runtime:

           return runtime.deserialize_cuda_engine(f.read())



   trt_engine = load_trt_engine("your_model.trt")



   class LocalLanguageModel(LanguageModel):

       def __init__(self, engine):

           self.engine = engine



       def predict(self, input_text):

           # Implement prediction logic using TensorRT engine

           pass



   local_model = LocalLanguageModel(trt_engine)

1. Develop the Agent:

Use LangChain to develop your agent utilizing the local language model.

   from langchain.agents import Agent



   class LocalCopilotAgent(Agent):

       def __init__(self, model):

           self.model = model



       def respond(self, input_text):

           return self.model.predict(input_text)



   agent = LocalCopilotAgent(local_model)

1. Run the Agent Locally

2. Execute the Agent:

Run the agent locally to handle tasks as required.

   if __name__ == "__main__":

       user_input = "Enter your input here"

       response = agent.respond(user_input)

       print(response)

By following these steps, you can develop a local copilot using LangChain and NVIDIA TensorRT-LLM. This approach ensures privacy and security by running the model on local compute resources.