π€ Anarchy Labs' LLM-VM: Project Introduction
π About the Anarchy LLM-VM
π What is the Anarchy LLM-VM?
The Anarchy LLM-VM is a cutting-edge solution designed to revolutionize how large language models (LLMs) operate. It acts as both a virtual machine and an interpreter for human language, integrating data, models, user prompts, and various tools in a single, streamlined framework. This allows users to enhance their LLM capabilities with features like tool usage, stateful memory, data augmentation, and more, while optimizing costs by reducing the need for expensive distributed endpoints.
π€ Why Use the Anarchy LLM-VM?
Anarchy's LLM-VM is built to empower users by making artificial intelligence more accessible and cost-effective. It enables users to:
- Accelerate Development: Easily connect with the latest LLMs through a unified interface.
- Reduce Costs: Minimize expenses by running models on local environments.
- Enjoy Flexibility: Quickly switch between various models to identify the best fit for specific tasks.
- Engage with Community: Join a vibrant community committed to democratizing AI.
- Gain Transparency: Benefit from the open-source nature and focus on development.
π Features and Roadmap
- Implicit Agents: Use external tools through agents by providing tool descriptions.
- Inference Optimization: Benefit from advanced techniques to enhance performance efficiently.
- Task Auto-Optimization: Improve repetitive task handling with student-teacher distillation and data synthesis.
- Library Callable: Access the LLM-VM as a library in any Python-based project.
- HTTP Endpoints: Serve completion requests via a standalone HTTP server.
- Live Data Augmentation (Future): Continuously update data sets for model fine-tuning.
- Web Playground (Future): Test and interact with LLM-VM directly in the browser.
- Load-Balancing and Orchestration (Future): Smartly manage multiple LLMs to balance performance and cost.
- Output Templating (Future): Format LLM outputs in specific structures with ease.
- Persistent Stateful Memory (Future): Maintain conversation context for a more cohesive experience.
π Quickstart Guide
π₯Ή Requirements
- Installation Requirements: Works with Python 3.10 and later. Specific models may have additional requirements in terms of memory and compute resources.
π¨βπ» Installation
To install LLM-VM, execute pip install llm-vm in the Python environment or clone the GitHub repository and set it up with these commands:
> git clone https://github.com/anarchy-ai/LLM-VM.git
> cd LLM-VM
> ./setup.sh
For Windows, follow these simplified installation steps using PowerShell, ensuring RemoteSigned execution policy is set.
β Generating Completions
The LLM-VM allows immediate interaction with several popular LLMs. After installation, use the following Python snippet to generate text completions locally:
from llm_vm.client import Client
client = Client(big_model = 'chat_gpt')
response = client.complete(prompt = 'What is Anarchy?', context = '', openai_key = 'ENTER_YOUR_API_KEY')
print(response)
πββ Running LLMs Locally
To run models such as LlaMA 2 locally, consider the following example:
from llm_vm.client import Client
client = Client(big_model = 'llama2')
response = client.complete(prompt = 'What is Anarchy?', context = '')
print(response)
π Supported Models
Current support includes models like chat_gpt, neo, llama2, bloom, and more, detailed in the project's documentation.
π Tool Usage
Two core agents, FLAT and REBEL, can be run from specific agent directories or a command-line interface for streamlined operations.
π©· Contributing to LLM-VM
Join the Anarchy community of developers via Discord or contribute directly by addressing open issues on GitHub. Opportunities for jobs and bounties are available for active contributors. Specific contact details for key team members and links to their professional profiles are provided for direct engagement.
This introduction seeks to provide a clear overview of Anarchy Labs' LLM-VM project, illustrating its potential use cases, benefits, and pathways for community involvement.