AI tools for 女子仰躺，高举双腿.男人面向女子，跪在股後，双手棒扶女腿担在双肩上，使女双膝高度过胸，并略提起对方屁股脊背.
Related Tools:
sora-prompt-zh
Sora-prompt-zh is a repository providing guidance on using Sora in various scenarios, learning how to make it understand your commands, and exploring Sora's multiple applications. It offers AI models that can create realistic and imaginative scenes from OpenAI's text instructions. The repository includes prompts for generating videos, animations, video editing, image generation, and more. Users can find examples and generated videos based on different video styles and modify them as needed. Although Sora is not officially released yet, the repository aims to collect prompts to help users quickly start using Sora to generate desired videos.
stylellm_models
**stylellm** is a text style transfer project based on large language models (llms). The project utilizes large language models to learn the writing style of specific literary works (commonly used vocabulary, sentence structure, rhetoric, character dialogue, etc.), forming a series of specific style models. Using the **stylellm** model, the learned style can be transferred to other general texts, that is: input a piece of original text, the model can rewrite it, output text with the characteristics of that style, achieving the effect of text modification,润色or style imitation.
glm-free-api
GLM AI Free 服务 provides high-speed streaming output, multi-turn dialogue support, intelligent agent dialogue support, AI drawing support, online search support, long document interpretation support, image parsing support. It offers zero-configuration deployment, multi-token support, and automatic session trace cleaning. It is fully compatible with the ChatGPT interface. The repository also includes six other free APIs for various services like Moonshot AI, StepChat, Qwen, Metaso, Spark, and Emohaa. The tool supports tasks such as chat completions, AI drawing, document interpretation, image parsing, and refresh token survival check.
AMchat
AMchat is a large language model that integrates advanced math concepts, exercises, and solutions. The model is based on the InternLM2-Math-7B model and is specifically designed to answer advanced math problems. It provides a comprehensive dataset that combines Math and advanced math exercises and solutions. Users can download the model from ModelScope or OpenXLab, deploy it locally or using Docker, and even retrain it using XTuner for fine-tuning. The tool also supports LMDeploy for quantization, OpenCompass for evaluation, and various other features for model deployment and evaluation. The project contributors have provided detailed documentation and guides for users to utilize the tool effectively.
qwen-free-api
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.
promptulate
**Promptulate** is an AI Agent application development framework crafted by **Cogit Lab** , which offers developers an extremely concise and efficient way to build Agent applications through a Pythonic development paradigm. The core philosophy of Promptulate is to borrow and integrate the wisdom of the open-source community, incorporating the highlights of various development frameworks to lower the barrier to entry and unify the consensus among developers. With Promptulate, you can manipulate components like LLM, Agent, Tool, RAG, etc., with the most succinct code, as most tasks can be easily completed with just a few lines of code. 🚀
LLMeBench
LLMeBench is a flexible framework designed for accelerating benchmarking of Large Language Models (LLMs) in the field of Natural Language Processing (NLP). It supports evaluation of various NLP tasks using model providers like OpenAI, HuggingFace Inference API, and Petals. The framework is customizable for different NLP tasks, LLM models, and datasets across multiple languages. It features extensive caching capabilities, supports zero- and few-shot learning paradigms, and allows on-the-fly dataset download and caching. LLMeBench is open-source and continuously expanding to support new models accessible through APIs.