Taskweaver Locally - How To Run TaskWeaver With LMStudio

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AI Summary

Summary: Installing and Using Taskweaver with a Local LLM

• Objective: Install Taskweaver and use it with a local LLM via LM Studio.
• Installation:
  • Install LM Studio from LM Studio Ai website.
  • Choose and download a model (e.g., Mistral).
  • Start the server to run the model locally.
• Integration with Taskweaver:
  • Clone the Taskweaver GitHub repository.
  • Create a new Conda environment with Python 3.11.
  • Activate the environment and install requirements.
  • Open Visual Studio Code from the terminal.
  • Modify the Taskweaver config file:
    • Set llm API base to the local server address.
    • Set API key to null.
    • Choose the LLM model (e.g., Mistral, Starcoder).
    • Add a new line for llm response.
• Running Taskweaver:
  • Execute a provided line of code to start Taskweaver.
  • Enter a request (e.g., print all even numbers between 12 and 48).
  • Taskweaver interacts with the local LLM to process the request.
• Considerations:
  • Local LLMs may be less powerful than alternatives like GPT-4 or GPT-3.5 Turbo.
  • Local LLMs can be slower but are expected to improve over time.
  • Taskweaver is new and anticipated to develop rapidly.
• Recommendations:
  • Stay updated on Taskweaver by watching the GitHub repository.
  • Join Discord channels and follow discussions for early insights.
  • Follow the video creator’s channel for further exploration of Taskweaver and Autogen.
• Troubleshooting:
  • If issues arise, ensure LM Studio is updated to the latest version.
• Closing Notes:
  • The video demonstrates how to use Taskweaver locally.
  • The creator acknowledges the current limitations of local LLMs in terms of speed and power.
  • Future updates and improvements to local LLMs are anticipated.