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vLLM v0.6.6 Documentation
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Getting Started

 Sponsor  来源:vLLM 浏览 621 扫码 2025-02-10 14:13:09
  • Installation
  • Installation with ROCm
  • Installation with OpenVINO
  • Installation with CPU
  • Installation with Intel® Gaudi® AI Accelerators
  • Installation for ARM CPUs
  • Installation with Neuron
  • Installation with TPU
  • Installation with XPU
  • Quickstart
  • Debugging Tips
  • Examples
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  • vLLM v0.7.2 Documentation
  • vLLM v0.7.1 Documentation
  • vLLM v0.7.0 Documentation
  • vLLM v0.6.6 Documentation
  • vLLM v0.6.5 Documentation
  • vLLM v0.6.4 Documentation
  • vLLM v0.6.3 Documentation
  • vLLM v0.6.2 Documentation
  • vLLM v0.6.1 Documentation
  • vLLM v0.6.0 Documentation
  • vLLM v0.5.5 Documentation
  • vLLM v0.5.4 Documentation
  • vLLM v0.5.3 Documentation
  • vLLM v0.5.2 Documentation
  • vLLM v0.5.1 Documentation
  • vLLM v0.5.0 Documentation
  • vLLM v0.4.3 Documentation
  • vLLM v0.4.2 Documentation
  • vLLM v0.4.1 Documentation
  • Getting Started
    • Installation
    • Installation with ROCm
    • Installation with OpenVINO
    • Installation with CPU
    • Installation with Intel® Gaudi® AI Accelerators
    • Installation for ARM CPUs
    • Installation with Neuron
    • Installation with TPU
    • Installation with XPU
    • Quickstart
    • Debugging Tips
    • Examples
  • Serving
    • OpenAI Compatible Server
    • Deploying with Docker
    • Deploying with Kubernetes
    • Deploying with Helm
    • Deploying with Nginx Loadbalancer
    • Distributed Inference and Serving
    • Production Metrics
    • Integrations
    • Loading Models with CoreWeave’s Tensorizer
    • Loading Models with Run:ai Model Streamer
  • Models
    • Supported Models
    • Generative Models
    • Pooling Models
    • Adding a New Model
    • Enabling Multimodal Inputs
  • Usage
    • LoRA Adapters
    • Multimodal Inputs
    • Tool Calling
    • Structured Outputs
    • Speculative decoding
    • Compatibility Matrix
    • Performance and Tuning
    • Frequently Asked Questions
    • Engine Arguments
    • Environment Variables
    • Usage Stats Collection
    • Disaggregated prefilling (experimental)
  • Quantization
    • Supported Hardware for Quantization Kernels
    • AutoAWQ
    • BitsAndBytes
    • GGUF
    • INT8 W8A8
    • FP8 W8A8
    • FP8 E5M2 KV Cache
    • FP8 E4M3 KV Cache
  • Automatic Prefix Caching
    • Introduction
    • Implementation
  • Performance
    • Benchmark Suites
  • Community
    • vLLM Meetups
    • Sponsors
  • API Documentation
    • Sampling Parameters
    • Pooling Parameters
    • Offline Inference
    • vLLM Engine
  • Design
    • Architecture Overview
    • Integration with HuggingFace
    • vLLM’s Plugin System
    • Input Processing
    • vLLM Paged Attention
    • Multi-Modality
    • Python Multiprocessing
  • For Developers
    • Contributing to vLLM
    • Profiling vLLM
    • Dockerfile
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