Qwen / vLLM Service Inference Path

ML & AI · flowchart diagram · MIT

Illustrates the deployment and inference pipeline for a Qwen model fine-tuned with LoRA, served via vLLM's OpenAI-compatible API, handling HTTP requests an

Source: https://github.com/jiaran-king/MicroLM/blob/782ae02f10c14b484a317f22115a066b3b10b91d/Readme/%E9%A1%B9%E7%9B%AE%E5%85%A8%E6%99%AF%E5%9B%BE/00-%E5%85%A8%E6%B5%81%E7%A8%8B%E5%88%86%E6%9E%90%EF%BC%88%E8%AE%AD%E7%BB%83%E3%80%81%E6%8E%A8%E7%90%86%E3%80%81%E8%AF%84%E6%B5%8B%E4%B8%8E%E9%83%A8%E7%BD%B2%EF%BC%89.md
Curated by jiaran-king
Qwen vLLM LoRA LLM Inference API Serving Machine Learning Deployment

Mermaid source

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flowchart TB
    M["Qwen base + LoRA adaptor"] --> EX["export_final_model.py<br>merge_and_unload"]
    EX --> HF["merged HF model dir"]
    HF --> SV["serve_vllm.sh<br>启动 OpenAI-compatible API"]
    SV --> REQ["HTTP request<br>messages / prompt / response_format"]
    REQ --> RT["vLLM runtime<br>tokenizer → prefill → decode"]
    RT --> RESP["JSON / text response"]

    classDef deploy fill:#f5f3ff,stroke:#8b5cf6,color:#0f172a;
    classDef api fill:#eff6ff,stroke:#60a5fa,color:#0f172a;
    classDef out fill:#f0fdf4,stroke:#22c55e,color:#0f172a;
    class M,EX,HF,SV,RT deploy;
    class REQ api;
    class RESP out;

What this diagram shows

This diagram details the full inference path for a Qwen base model with a LoRA adaptor, from merging the model and exporting it to a Hugging Face directory, to serving it via vLLM's OpenAI-compatible API. It covers the HTTP request and response flow, including tokenization, prefill, and decode stages within the vLLM runtime.

When to use it

Use this diagram to understand how to deploy and serve a fine-tuned Large Language Model (LLM) like Qwen using vLLM for high-throughput, low-latency inference. It is particularly relevant when an OpenAI-compatible API endpoint is desired for integration with other applications or services.

How to adapt it for your project

This pattern can be adapted for serving other Hugging Face-compatible LLMs with vLLM. The model merging step might vary depending on the specific fine-tuning method. The `response_format=json_object` feature highlights a key aspect for structured output scenarios, which can be customized or removed based on application needs.

Key concepts