qwen-free-api
🚀 阿里通义千问大模型逆向API白嫖测试【特长:六边形战士】,支持高速流式输出、无水印AI绘图、长文档解读、图像解析、多轮对话,零配置部署,多路token支持,自动清理会话痕迹。
Stars: 249
Qwen AI Free service supports high-speed streaming output, multi-turn dialogue, watermark-free AI drawing, long document interpretation, image parsing, zero-configuration deployment, multi-token support, automatic session trace cleaning. It is fully compatible with the ChatGPT interface. The repository provides various free APIs for different AI services. Users can access the service through different deployment methods like Docker, Docker-compose, Render, Vercel, and native deployment. It offers interfaces for chat completions, AI drawing, document interpretation, image parsing, and token checking. Users need to provide 'login_tongyi_ticket' for authorization. The project emphasizes research, learning, and personal use only, discouraging commercial use to avoid service pressure on the official platform.
README:
支持高速流式输出、支持多轮对话、支持无水印AI绘图、支持长文档解读、图像解析,零配置部署,多路token支持,自动清理会话痕迹。
与ChatGPT接口完全兼容。
还有以下七个free-api欢迎关注:
Moonshot AI(Kimi.ai)接口转API kimi-free-api
阶跃星辰 (跃问StepChat) 接口转API step-free-api
智谱AI (智谱清言) 接口转API glm-free-api
秘塔AI (Metaso) 接口转API metaso-free-api
讯飞星火(Spark)接口转API spark-free-api
MiniMax(海螺AI)接口转API hailuo-free-api
聆心智能 (Emohaa) 接口转API emohaa-free-api
逆向API是不稳定的,建议前往阿里云官方 https://dashscope.console.aliyun.com/ 付费使用API,避免封禁的风险。
本组织和个人不接受任何资金捐助和交易,此项目是纯粹研究交流学习性质!
仅限自用,禁止对外提供服务或商用,避免对官方造成服务压力,否则风险自担!
仅限自用,禁止对外提供服务或商用,避免对官方造成服务压力,否则风险自担!
仅限自用,禁止对外提供服务或商用,避免对官方造成服务压力,否则风险自担!
此链接仅临时测试功能,长期使用请自行部署。
https://udify.app/chat/qOXzVl5kkvhQXM8r
从 通义千问 登录
进入通义千问随便发起一个对话,然后F12打开开发者工具,从Application > Cookies中找到login_tongyi_ticket
的值,这将作为Authorization的Bearer Token值:Authorization: Bearer TOKEN
你可以通过提供多个账号的login_tongyi_ticket,并使用,拼接提供:
Authorization: Bearer TOKEN1,TOKEN2,TOKEN3
每次请求服务会从中挑选一个。
请准备一台具有公网IP的服务器并将8000端口开放。
拉取镜像并启动服务
docker run -it -d --init --name qwen-free-api -p 8000:8000 -e TZ=Asia/Shanghai vinlic/qwen-free-api:latest
查看服务实时日志
docker logs -f qwen-free-api
重启服务
docker restart qwen-free-api
停止服务
docker stop qwen-free-api
version: '3'
services:
qwen-free-api:
container_name: qwen-free-api
image: vinlic/qwen-free-api:latest
restart: always
ports:
- "8000:8000"
environment:
- TZ=Asia/Shanghai
注意:部分部署区域可能无法连接qwen,如容器日志出现请求超时或无法连接,请切换其他区域部署! 注意:免费账户的容器实例将在一段时间不活动时自动停止运行,这会导致下次请求时遇到50秒或更长的延迟,建议查看Render容器保活
-
fork本项目到你的github账号下。
-
访问 Render 并登录你的github账号。
-
构建你的 Web Service(New+ -> Build and deploy from a Git repository -> Connect你fork的项目 -> 选择部署区域 -> 选择实例类型为Free -> Create Web Service)。
-
等待构建完成后,复制分配的域名并拼接URL访问即可。
注意:Vercel免费账户的请求响应超时时间为10秒,但接口响应通常较久,可能会遇到Vercel返回的504超时错误!
请先确保安装了Node.js环境。
npm i -g vercel --registry http://registry.npmmirror.com
vercel login
git clone https://github.com/LLM-Red-Team/qwen-free-api
cd qwen-free-api
vercel --prod
请准备一台具有公网IP的服务器并将8000端口开放。
请先安装好Node.js环境并且配置好环境变量,确认node命令可用。
安装依赖
npm i
安装PM2进行进程守护
npm i -g pm2
编译构建,看到dist目录就是构建完成
npm run build
启动服务
pm2 start dist/index.js --name "qwen-free-api"
查看服务实时日志
pm2 logs qwen-free-api
重启服务
pm2 reload qwen-free-api
停止服务
pm2 stop qwen-free-api
使用以下二次开发客户端接入free-api系列项目更快更简单,支持文档/图像上传!
由 Clivia 二次开发的LobeChat https://github.com/Yanyutin753/lobe-chat
由 时光@ 二次开发的ChatGPT Web https://github.com/SuYxh/chatgpt-web-sea
目前支持与openai兼容的 /v1/chat/completions
接口,可自行使用与openai或其他兼容的客户端接入接口,或者使用 dify 等线上服务接入使用。
对话补全接口,与openai的 chat-completions-api 兼容。
POST /v1/chat/completions
header 需要设置 Authorization 头部:
Authorization: Bearer [login_tongyi_ticket]
请求数据:
{
// 模型名称随意填写
"model": "qwen",
"messages": [
{
"role": "user",
"content": "你是谁?"
}
],
// 如果使用SSE流请设置为true,默认false
"stream": false
}
响应数据:
{
"id": "4c4267e7919a41baad8199414ceb5cea",
"model": "qwen",
"object": "chat.completion",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "我是阿里云研发的超大规模语言模型,我叫通义千问。"
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 1,
"completion_tokens": 1,
"total_tokens": 2
},
"created": 1710152062
}
对话补全接口,与openai的 images-create-api 兼容。
POST /v1/images/generations
header 需要设置 Authorization 头部:
Authorization: Bearer [login_tongyi_ticket]
请求数据:
{
// 可以乱填
"model": "wanxiang",
"prompt": "一只可爱的猫"
}
响应数据:
{
"created": 1711507734,
"data": [
{
"url": "https://wanx.alicdn.com/wanx/1111111111/text_to_image/7248e85cfda6491aae59c54e7e679b17_0.png"
}
]
}
提供一个可访问的文件URL或者BASE64_URL进行解析。
POST /v1/chat/completions
header 需要设置 Authorization 头部:
Authorization: Bearer [refresh_token]
请求数据:
{
"model": "qwen",
"messages": [
{
"role": "user",
"content": [
{
"type": "file",
"file_url": {
"url": "https://mj101-1317487292.cos.ap-shanghai.myqcloud.com/ai/test.pdf"
}
},
{
"type": "text",
"text": "文档里说了什么?"
}
]
}
]
}
响应数据:
{
"id": "b56ea6c9e86140429fa2de6a6ec028ff",
"model": "qwen",
"object": "chat.completion",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "文档中包含了四个古代魔法仪式或咒语的描述,它们似乎旨在影响或控制一个特定女性的情感和行为,使其对施术者产生强烈的爱意。以下是每个仪式的具体内容:\n\n1. **仪式一**(PMG 4.1390 – 1495):\n - 施术者需留下一些面包,将其掰成七小块。\n - 前往一处英雄、角斗士或其他暴力死亡者丧生的地方。\n - 对着面包碎片念诵咒语后丢弃,并从该地取一些受污染的泥土扔进目标女性的住所。\n - 咒语内容包括向命运三女神(Moirai)、罗马版的命运女神(Fates)、自然力量(Daemons)、饥荒与嫉妒之神以及非正常死亡者献祭食物,并请求他们以痛苦折磨目标,使她在梦中惊醒,心生忧虑与恐惧,最终跟随施术者的步伐并顺从其意愿。此过程以赫卡忒(Hecate)女神为命令的源泉。\n\n2. **仪式二**(PMG 4.1342 – 57):\n - 施术者召唤恶魔(Daemon),通过一系列神秘的神祇名号(如Erekisephthe Araracharara Ephthesikere)要求其将名为Tereous的女子(Apia所生)带至施术者Didymos(Taipiam所生)身边。\n - 请求该女子在灵魂、心智及女性器官上遭受剧烈痛苦,直至她主动找寻Didymos并与之紧密相连(唇对唇、发对发、腹部对腹部)。整个过程要求立即执行。\n\n3. **仪式三**(PGM 4.1265 – 74):\n - 揭示了阿佛洛狄忒(Aphrodite)鲜为人知的名字——NEPHERIĒRI[nfr-iry-t]。\n - 如果想赢得一位美丽女子的芳心,施术者应保持三天纯净,献上乳香,并在心中默念该名字七次。\n - 这样的做法需持续七天,据说这样便能成功吸引女子。\n\n4. **仪式四**(PGM 4.1496 – 1):\n - 施术者在燃烧的煤炭上供奉没药(myrrh),同时念诵咒语。\n - 咒语将没药称为“苦涩的调和者”、“热力的激发者”,并命令它前往指定的女子(及其母亲的名字)处,阻止她进行日常活动(如坐、饮、食、注视他人、亲吻他人),迫使她心中只有施术者,对其产生强烈的欲望与爱意。\n - 咒语还指示没药直接穿透女子的灵魂,驻留在其心中,焚烧其内脏、胸部、肝脏、气息、骨骼、骨髓,直到她来到施术者身边。\n\n这些仪式反映了古代魔法实践中试图借助超自然力量操控他人情感与行为的企图,涉及对神灵、恶魔、神秘名字及特定物质(如面包、泥土、乳香、没药)的运用,通常伴随着严格的仪式规程和咒语念诵。此类行为在现代伦理和法律框架下被视为不恰当甚至违法,且缺乏科学依据。"
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 1,
"completion_tokens": 1,
"total_tokens": 2
},
"created": 1712253736
}
提供一个可访问的图像URL或者BASE64_URL进行解析。
此格式兼容 gpt-4-vision-preview API格式,您也可以用这个格式传送文档进行解析。
POST /v1/chat/completions
header 需要设置 Authorization 头部:
Authorization: Bearer [refresh_token]
请求数据:
{
"model": "qwen",
"messages": [
{
"role": "user",
"content": [
{
"type": "file",
"file_url": {
"url": "https://img.alicdn.com/imgextra/i1/O1CN01CC9kic1ig1r4sAY5d_!!6000000004441-2-tps-880-210.png"
}
},
{
"type": "text",
"text": "图像描述了什么?"
}
]
}
]
}
响应数据:
{
"id": "895fbe7fa22442d499ba67bb5213e842",
"model": "qwen",
"object": "chat.completion",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "图像展示了通义千问的标志,一个紫色的六边形和一个蓝色的三角形,以及“通义千问”四个白色的汉字。"
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 1,
"completion_tokens": 1,
"total_tokens": 2
},
"created": 1712254066
}
检测login_tongyi_ticket是否存活,如果存活live未true,否则为false,请不要频繁(小于10分钟)调用此接口。
POST /token/check
请求数据:
{
"token": "QIhaHrrXUaIrWMUmL..."
}
响应数据:
{
"live": true
}
如果您正在使用Nginx反向代理qwen-free-api,请添加以下配置项优化流的输出效果,优化体验感。
# 关闭代理缓冲。当设置为off时,Nginx会立即将客户端请求发送到后端服务器,并立即将从后端服务器接收到的响应发送回客户端。
proxy_buffering off;
# 启用分块传输编码。分块传输编码允许服务器为动态生成的内容分块发送数据,而不需要预先知道内容的大小。
chunked_transfer_encoding on;
# 开启TCP_NOPUSH,这告诉Nginx在数据包发送到客户端之前,尽可能地发送数据。这通常在sendfile使用时配合使用,可以提高网络效率。
tcp_nopush on;
# 开启TCP_NODELAY,这告诉Nginx不延迟发送数据,立即发送小数据包。在某些情况下,这可以减少网络的延迟。
tcp_nodelay on;
# 设置保持连接的超时时间,这里设置为120秒。如果在这段时间内,客户端和服务器之间没有进一步的通信,连接将被关闭。
keepalive_timeout 120;
由于推理侧不在qwen-free-api,因此token不可统计,将以固定数字返回。
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h2oGPT is an Apache V2 open-source project that allows users to query and summarize documents or chat with local private GPT LLMs. It features a private offline database of any documents (PDFs, Excel, Word, Images, Video Frames, Youtube, Audio, Code, Text, MarkDown, etc.), a persistent database (Chroma, Weaviate, or in-memory FAISS) using accurate embeddings (instructor-large, all-MiniLM-L6-v2, etc.), and efficient use of context using instruct-tuned LLMs (no need for LangChain's few-shot approach). h2oGPT also offers parallel summarization and extraction, reaching an output of 80 tokens per second with the 13B LLaMa2 model, HYDE (Hypothetical Document Embeddings) for enhanced retrieval based upon LLM responses, a variety of models supported (LLaMa2, Mistral, Falcon, Vicuna, WizardLM. With AutoGPTQ, 4-bit/8-bit, LORA, etc.), GPU support from HF and LLaMa.cpp GGML models, and CPU support using HF, LLaMa.cpp, and GPT4ALL models. Additionally, h2oGPT provides Attention Sinks for arbitrarily long generation (LLaMa-2, Mistral, MPT, Pythia, Falcon, etc.), a UI or CLI with streaming of all models, the ability to upload and view documents through the UI (control multiple collaborative or personal collections), Vision Models LLaVa, Claude-3, Gemini-Pro-Vision, GPT-4-Vision, Image Generation Stable Diffusion (sdxl-turbo, sdxl) and PlaygroundAI (playv2), Voice STT using Whisper with streaming audio conversion, Voice TTS using MIT-Licensed Microsoft Speech T5 with multiple voices and Streaming audio conversion, Voice TTS using MPL2-Licensed TTS including Voice Cloning and Streaming audio conversion, AI Assistant Voice Control Mode for hands-free control of h2oGPT chat, Bake-off UI mode against many models at the same time, Easy Download of model artifacts and control over models like LLaMa.cpp through the UI, Authentication in the UI by user/password via Native or Google OAuth, State Preservation in the UI by user/password, Linux, Docker, macOS, and Windows support, Easy Windows Installer for Windows 10 64-bit (CPU/CUDA), Easy macOS Installer for macOS (CPU/M1/M2), Inference Servers support (oLLaMa, HF TGI server, vLLM, Gradio, ExLLaMa, Replicate, OpenAI, Azure OpenAI, Anthropic), OpenAI-compliant, Server Proxy API (h2oGPT acts as drop-in-replacement to OpenAI server), Python client API (to talk to Gradio server), JSON Mode with any model via code block extraction. Also supports MistralAI JSON mode, Claude-3 via function calling with strict Schema, OpenAI via JSON mode, and vLLM via guided_json with strict Schema, Web-Search integration with Chat and Document Q/A, Agents for Search, Document Q/A, Python Code, CSV frames (Experimental, best with OpenAI currently), Evaluate performance using reward models, and Quality maintained with over 1000 unit and integration tests taking over 4 GPU-hours.
serverless-chat-langchainjs
This sample shows how to build a serverless chat experience with Retrieval-Augmented Generation using LangChain.js and Azure. The application is hosted on Azure Static Web Apps and Azure Functions, with Azure Cosmos DB for MongoDB vCore as the vector database. You can use it as a starting point for building more complex AI applications.
react-native-vercel-ai
Run Vercel AI package on React Native, Expo, Web and Universal apps. Currently React Native fetch API does not support streaming which is used as a default on Vercel AI. This package enables you to use AI library on React Native but the best usage is when used on Expo universal native apps. On mobile you get back responses without streaming with the same API of `useChat` and `useCompletion` and on web it will fallback to `ai/react`
LLamaSharp
LLamaSharp is a cross-platform library to run 🦙LLaMA/LLaVA model (and others) on your local device. Based on llama.cpp, inference with LLamaSharp is efficient on both CPU and GPU. With the higher-level APIs and RAG support, it's convenient to deploy LLM (Large Language Model) in your application with LLamaSharp.
gpt4all
GPT4All is an ecosystem to run powerful and customized large language models that work locally on consumer grade CPUs and any GPU. Note that your CPU needs to support AVX or AVX2 instructions. Learn more in the documentation. A GPT4All model is a 3GB - 8GB file that you can download and plug into the GPT4All open-source ecosystem software. Nomic AI supports and maintains this software ecosystem to enforce quality and security alongside spearheading the effort to allow any person or enterprise to easily train and deploy their own on-edge large language models.
ChatGPT-Telegram-Bot
ChatGPT Telegram Bot is a Telegram bot that provides a smooth AI experience. It supports both Azure OpenAI and native OpenAI, and offers real-time (streaming) response to AI, with a faster and smoother experience. The bot also has 15 preset bot identities that can be quickly switched, and supports custom bot identities to meet personalized needs. Additionally, it supports clearing the contents of the chat with a single click, and restarting the conversation at any time. The bot also supports native Telegram bot button support, making it easy and intuitive to implement required functions. User level division is also supported, with different levels enjoying different single session token numbers, context numbers, and session frequencies. The bot supports English and Chinese on UI, and is containerized for easy deployment.
twinny
Twinny is a free and open-source AI code completion plugin for Visual Studio Code and compatible editors. It integrates with various tools and frameworks, including Ollama, llama.cpp, oobabooga/text-generation-webui, LM Studio, LiteLLM, and Open WebUI. Twinny offers features such as fill-in-the-middle code completion, chat with AI about your code, customizable API endpoints, and support for single or multiline fill-in-middle completions. It is easy to install via the Visual Studio Code extensions marketplace and provides a range of customization options. Twinny supports both online and offline operation and conforms to the OpenAI API standard.
agnai
Agnaistic is an AI roleplay chat tool that allows users to interact with personalized characters using their favorite AI services. It supports multiple AI services, persona schema formats, and features such as group conversations, user authentication, and memory/lore books. Agnaistic can be self-hosted or run using Docker, and it provides a range of customization options through its settings.json file. The tool is designed to be user-friendly and accessible, making it suitable for both casual users and developers.
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sweep
Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.
teams-ai
The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.
ai-guide
This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.
classifai
Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.
chatbot-ui
Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.
BricksLLM
BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students
uAgents
uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.
griptape
Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.