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Basic Information

LLM-VM is an open-source backend and virtual machine for running and orchestrating large language models. It is presented as a highly optimized, opinionated interpreter for human language that coordinates between data, models, prompts and external tools to provide modern completion features in one place. The README describes it as a way to give LLMs additional capabilities such as tool usage, batch and architecture-aware inference optimization, student-teacher distillation, and a library-plus-server deployment model. It targets developers and researchers who want to run open models locally or via HTTP endpoints, experiment with model swapping, and integrate agents. The project is in beta and notes that development is currently on pause. Installation options include pip install and repository cloning for Mac, Linux and Windows.

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App Details

Features
The repository documents a collection of runtime and tooling features: a Python client library and standalone HTTP endpoints for completions, inference optimizations such as batching, sparse inference and quantization, student-teacher distillation and data synthesis, model-agnostic orchestration and colocation, load-balancing and multi-provider orchestration as a roadmap item, output templating and live data augmentation planned for the roadmap, persistent stateful memory planned, and built-in agent support with two example agents named FLAT and REBEL. It supports running a range of local and remote models and includes a web playground and CLI agent interface on the roadmap or implemented as examples.
Use Cases
LLM-VM helps developers reduce cost and complexity when building applications around open-source LLMs by centralizing model management, tool integration and inference optimization. It enables local experimentation with multiple model families, rapid switching between model configurations, and library access from Python code as well as HTTP API usage for service deployment. The project documents practical installation and quickstart examples, lists supported models and gives guidance on system requirements such as Python 3.10+ and typical RAM constraints. Features like batching, distillation and quantization are intended to improve throughput and lower compute cost. The included agent examples, CLI interface and community emphasis aim to accelerate AGI-oriented research and prototyping.

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