Nexaaidev
Models by this creator
🌀
Octopus-v2
813
Octopus-V2-2B is an advanced open-source language model with 2 billion parameters, representing a research breakthrough from Nexa AI in applying large language models (LLMs) for function calling, specifically tailored for Android APIs. Unlike Retrieval-Augmented Generation (RAG) methods, which require detailed descriptions of potential function arguments sometimes needing up to tens of thousands of input tokens, Octopus-V2-2B introduces a unique functional token strategy for both its training and inference stages. This approach not only allows it to achieve performance levels comparable to GPT-4 but also significantly enhances its inference speed beyond that of RAG-based methods, making it especially beneficial for edge computing devices. Similar models include the 2B instruct version of Google's Gemma model, the 7B instruct version of Google's Gemma model, and GPT-2B-001 from NVIDIA, all of which are large language models with similar capabilities. Model inputs and outputs Inputs Text**: The model can process a variety of text-based inputs, such as questions, prompts, or documents. Outputs Generated text**: The model outputs generated English-language text in response to the input, such as answers to questions, summaries of documents, or function call code. Capabilities Octopus-V2-2B is engineered to operate seamlessly on Android devices, extending its utility across a wide range of applications, from Android system management to the orchestration of multiple devices. Its key capabilities include high performance on function calling tasks, comparable to GPT-4, and significantly faster inference speed than RAG-based methods, making it well-suited for edge computing use cases. What can I use it for? The Octopus-V2-2B model can be used for a variety of text-based applications, such as: Content Creation and Communication**: Generating creative text formats like poems, scripts, marketing copy, or chatbot responses. Research and Education**: Powering NLP research, developing language learning tools, or assisting with knowledge exploration. The model's fast inference speed and Android-focused design make it particularly well-suited for mobile and edge computing applications, such as on-device system management or device coordination. Things to try One key capability of Octopus-V2-2B is its high performance on function calling tasks, which is achieved through its unique functional token strategy. This approach allows the model to generate accurate function call code without requiring long, detailed input descriptions, making it more efficient and practical for certain use cases. Developers and researchers may want to experiment with using Octopus-V2-2B for tasks that involve generating or manipulating code, such as automating Android API calls or creating custom device coordination scripts. The model's speed and accuracy on these types of tasks could make it a valuable tool for a range of edge computing and mobile development projects.
Updated 5/28/2024
🏅
octo-net
123
octo-net is an advanced open-source language model with 3 billion parameters, developed by NexaAIDev. It serves as the master node in Nexa AI's envisioned graph of language models, efficiently translating user queries into formats that specialized models can effectively process. octo-net excels at directing queries to the appropriate specialized model, ensuring precise and effective query handling. Compared to similar models like Octopus-v4 and Octopus-v2, octo-net is compact in size, enabling it to operate on smart devices efficiently and swiftly. It also accurately maps user queries to specialized models using a functional token design, enhancing its precision. Additionally, octo-net assists in converting natural human language into a more professional format, improving query description and resulting in more accurate responses. Model inputs and outputs octo-net is a text-to-text model that takes user queries as input and generates responses that direct the query to the appropriate specialized model for processing. Inputs User query**: The natural language query provided by the user. Outputs Reformatted query**: The user query converted into a more professional format that can be effectively processed by specialized models. Specialized model call**: The instructions to call the specialized model that can best handle the given query. Capabilities octo-net demonstrates impressive capabilities in translating user queries into a format that can be efficiently processed by specialized models. For example, when provided with the query "Tell me the result of derivative of x^3 when x is 2?", octo-net generates a response that calls the appropriate math-focused model to determine the derivative of the function f(x) = x^3 at x = 2. What can I use it for? octo-net can be particularly useful in building intelligent systems that require seamless integration of multiple specialized models. For example, a virtual assistant application could leverage octo-net to route user queries to the appropriate domain-specific models for tasks like answering math questions, providing medical advice, or retrieving business insights. By automating the process of selecting the right model for a given query, octo-net can help streamline the development of such complex AI-powered applications. Things to try One interesting aspect of octo-net is its ability to reformat user queries into a more professional format. Developers could experiment with providing octo-net with a variety of natural language queries and observe how it translates them into a format that is more easily processed by specialized models. This could lead to insights on how to improve the natural language understanding and query reformatting capabilities of the model. Additionally, exploring the model's performance on specialized tasks like math, science, or business-related queries could provide valuable feedback on the strengths and limitations of the octo-net approach. Developers could also investigate ways to fine-tune or customize octo-net to better suit their specific use cases.
Updated 9/19/2024
🎲
Octopus-v4
99
The Octopus-v4 is an advanced open-source language model with 3 billion parameters developed by NexaAIDev. It is designed to serve as the master node in NexaAI's envisioned graph of language models, efficiently translating user queries into formats that specialized models can process. The model is compact in size, enabling it to operate on smart devices. It uses a functional token design to accurately map user queries to specialized models, improving query description and resulting in more accurate responses. This is in contrast to Retrieval-Augmented Generation (RAG) methods, which require detailed function argument descriptions. Similar models include Octopus-v2, an on-device language model for Android APIs, and Nemotron-3-8B-Base-4k, a large language foundation model for enterprises. Model inputs and outputs Inputs Natural language queries from users Outputs Reformatted queries that specialized models can effectively process Capabilities The Octopus-v4 model is capable of efficiently translating user queries into formats that specialized models can process, enhancing the accuracy and effectiveness of those models. Its compact size allows it to operate on smart devices, making it suitable for a wide range of applications. What can I use it for? The Octopus-v4 model can be used as a routing agent to direct user queries to the appropriate specialized model, ensuring precise and effective query handling. This makes it beneficial for applications that require interaction with multiple AI models, such as Android system management, device orchestration, and enterprise AI solutions. Things to try Developers can experiment with using the Octopus-v4 model as a front-end for their AI applications, allowing users to interact with a wide range of specialized models through a single, easy-to-use interface. This can help streamline the user experience and improve the overall effectiveness of the AI system.
Updated 6/4/2024