AI code generation

PromptChainer is a tool designed to improve the output quality of large language models. By automating the generation of prompt chains, it helps users break down complex tasks into manageable small steps, thereby obtaining more accurate and high-quality results. It is particularly suitable for tasks that require multiple steps and/or a lot of context and knowledge.

Gemma-7B-IT is a 7B parameter instruction adjustment model developed by Google. It adopts Gemini architecture and is designed to improve mathematics, logical reasoning and code generation capabilities. This model can be run on an ordinary laptop, does not require a lot of AI computing power, and is suitable for a variety of application scenarios.

Boss Copilot GPT4-128K GPT4-Vision is an auxiliary tool that can help create LLM applications to complete tasks through multiple agents that can interact with each other. Supported AI includes GPT4 128K, GPT4 Vision, ChatGPT, Microsoft Azure AI, and supported roles include engineers, scientists, planners, executors, critics, etc. Provides support for over 100 tasks and workflows, customizable according to your prompt instructions.

Screenshot to Code is a simple application that uses GPT-4 Vision to generate code and DALL-E 3 to generate similar images. The application has a React/Vite frontend and FastAPI backend, you need to have an OpenAI API key to access the GPT-4 Vision API.

Octopus is a visual language programming tool based on environmental feedback that can efficiently parse the agent's visual and textual task goals, formulate complex action sequences, and generate executable code. Octopus' design allows agents to handle a wide range of tasks, from everyday chores in simulators to complex interactions in complex video games. Octopus is trained in our experimental environment OctoVerse by leveraging GPT-4 to control the exploration agent to generate training data, namely action blueprints and corresponding executable code. We also collect feedback to allow reinforcement training schemes for reinforcement learning with environmental feedback (RLEF). Through a series of experiments, we elucidate the functionality of Octopus and present convincing results, and the proposed RLEF demonstrates the effectiveness of improving agent decision-making. By open sourcing our model architecture, simulators, and datasets, we hope to inspire more innovation and promote collaborative applications within the broader Experiential AI community.