0xSero/vllm-studio provides a robust framework for managing the lifecycle of large language models (LLMs) deployed via vLLM and SGLang inference servers. It targets engineers and researchers needing streamlined model deployment, configuration, and advanced interaction capabilities, offering benefits like simplified model orchestration and enhanced reasoning/tool-calling features.

How It Works

The project employs a controller-based architecture where a FastAPI application manages model operations, interacting with vLLM or SGLang backends. It introduces "recipes" for defining and reusing complex model configurations, including parameters for parallelism, context length, and quantization. Novelty lies in its auto-detection of parsers for advanced reasoning (e.g., GLM, INTELLECT-3) and native function calling, simplifying integration with models that support these features.

Quick Start & Requirements

Installation involves pip install -e . for the controller. The frontend requires cd frontend && npm install && npm run dev. Docker is recommended for optional LiteLLM API gateway and Temporal workflow orchestration. No specific hardware requirements beyond those for running vLLM/SGLang are detailed, nor are explicit links to official quick-start guides or demos provided in the README.

Highlighted Details

• Model Lifecycle Management: Launch and evict models via API or UI.
• Reusable Configurations: "Recipes" store full model parameters for consistent deployments.
• Advanced Reasoning & Tooling: Auto-detection for reasoning parsers (GLM, INTELLECT-3, MiniMax)