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qwenlm/qwen

★ 21,109PythonAudience · developerComplexity · 4/5MaintainedLicenseSetup · moderate

TLDR

Alibaba's open-source family of large language models (1.8B, 72B parameters) trained on 3 trillion multilingual tokens, with chat versions for conversation, coding, math, and tool use.

Mindmap

mindmap
  root((Qwen))
    What it does
      Chat conversations
      Code generation
      Math solving
      Tool use and agents
    Model sizes
      1.8B parameters
      7B parameters
      14B parameters
      72B parameters
    How to use
      Inference quickstart
      Quantization
      Finetuning with LoRA
      Deploy with vLLM
    Training data
      3 trillion tokens
      Multilingual focus
      Chinese and English
    Integration options
      DashScope API
      Web demos
      CLI tools

Things people build with this

USE CASE 1

Build a chatbot or conversational AI assistant that understands Chinese and English.

USE CASE 2

Fine-tune a smaller Qwen model (1.8B or 7B) on your own data to solve domain-specific tasks.

USE CASE 3

Deploy a code-generation tool that writes and debugs code in multiple languages.

USE CASE 4

Create an AI agent that uses external tools and APIs to answer complex questions.

Tech stack

PythonPyTorchvLLMFastChatLoRA

Getting it running

Difficulty · moderate Time to first run · 30min

Requires downloading large model weights (1.8B, 72B GB) and PyTorch/vLLM setup; inference works locally but training/fine-tuning needs GPU.

Use freely for any purpose including commercial. Keep the notice and disclose changes to the patent grant.

In plain English

Qwen (通义千问) is the official open-source repository for the first generation of Qwen, a family of large language models from Alibaba Cloud. A large language model is the kind of AI that powers chatbots and writing assistants, you give it text, and it predicts more text. This repo bundles both base models and chat-tuned versions in several sizes: Qwen-1.8B, Qwen-7B, Qwen-14B, and Qwen-72B, where the numbers refer to how many billion parameters each model has. Bigger models are generally more capable but need much more memory to run. The README notes the repo is no longer actively maintained and points readers to a newer Qwen2 repository. The base models were pretrained on up to 3 trillion tokens of multilingual data, with a focus on Chinese and English. The chat models are aligned to human preferences using supervised fine-tuning and RLHF, and can hold a conversation, write content, summarize, translate, write code, solve maths, use tools, and act as agents or code interpreters. The repo explains how to do simple inference; how to use quantized versions (Int4, Int8, and GPTQ) to save memory; how to fine-tune with full-parameter training, LoRA, or Q-LoRA; and how to deploy with vLLM or FastChat. It also covers building a WebUI or CLI demo, the DashScope API service, and exposing your own model behind an OpenAI-style API. You would use this repo if you want to run Qwen models locally or on your own server, for example, to build a Chinese-English assistant, fine-tune on private data, or experiment with tool-using agents. The code is Python. Model weights are distributed via Hugging Face and ModelScope. The full README is longer than what was provided.

Copy-paste prompts

Prompt 1
How do I set up Qwen 7B for inference on my GPU? Walk me through the quickstart.
Prompt 2
I want to fine-tune Qwen 14B using LoRA on my custom dataset. What are the steps?
Prompt 3
Show me how to quantize a Qwen model so it runs on a smaller GPU with less memory.
Prompt 4
How do I deploy Qwen as a web demo using vLLM and FastChat?
Prompt 5
Can you explain how to enable tool use in Qwen chat models so they can call external APIs?
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Generated 2026-05-21 · Model: sonnet-4-6 · Verify against the repo before relying on details.