Project Introduction: Are Copilots Local Yet?
Summary
The "Are Copilots Local Yet?" project offers insights into the current status and trends of using local Language Learning Models (LLMs) to assist in coding. These local copilots are designed to complete code, generate projects, act as shell assistants, and even fix bugs automatically. At present, most are in a minimum viable product (MVP) phase, due to several challenges:
- Inferior Performance: Local models, as of now, generally do not perform as well as server-based copilots.
- Complex Setup: They often require complicated installation and configuration.
- Hardware Demand: Running these models needs substantial computing resources.
Despite these hurdles, there is optimism that as models advance and get better integrated into development environments, a revival of enhanced code-completion tools will emerge. This project serves as a comprehensive guide for enthusiasts and developers to explore these tools and understand the state-of-the-art in this vibrant field.
Background
Launched by GitHub in 2021, Copilot quickly became a favorite among developers. Amidst a wave of AI and LLM advancements, it seemed inevitable that local versions of Copilot would surface, operating directly on consumer-grade hardware. Local copilots offer several benefits over their cloud-based counterparts:
- Offline Functionality: They can be used without internet access, ensuring privacy.
- Responsive: Faster performance due to local processing.
- Contextual Awareness: Better understanding of the specific project environment.
- Customization: The opportunity to adjust models for specific languages or tasks.
- Security: More control over the output and data privacy.
Editor Extensions
Developers can use a variety of editor extensions to enhance coding productivity via LLMs. Some notable ones include:
- GitHub Copilot for VSCode and vim, though it is not open-source or local.
- Cursor and Tabby, which provide code completion support in VSCode and other editors.
- Fauxpilot and localpilot, efforts to localize the copilot experience within various coding environments.
Tools
Several tools aim to create projects or features from brief specifications and facilitate AI-assisted programming:
- gpt-engineer and gpt-pilot: These tools build projects by interacting with users to clarify requirements.
- aider: Focuses on pair programming directly in terminal environments.
- Refact.AI: A versatile platform providing code completion and chatting functionality.
Chat Interfaces
Chat interfaces with access to system functions like shell commands give users powerful controls similar to OpenAI's Code Interpreter:
- open-interpreter: An open-source version of the code interpreter.
- gptme: Supports open models, developed by Erik Bjare.
- octogen: Executes local code within a Docker environment.
Models
Recent models developed for local usage include:
- Phind CodeLlama v2 and WizardCoder, which cater to developers needing multi-language support or specific task tuning.
- Starcoder, offering extensive language handling capabilities.
Datasets
Validating and training these local models require comprehensive datasets like The Stack, known for its extensive language variety and size.
Miscellaneous Tools
Tools such as ollama help users get started with local LLMs, providing an accessible entry point for deploying large models on personal hardware.
Conclusion
The "Are Copilots Local Yet?" project acts as an evolving resource for those exploring the potential of integrating LLMs into local coding environments. By navigating through its sections, developers can stay informed about the latest advancements and available tools in this exciting field of AI-driven coding assistance.