Nexa AI suggests the Octopus V4-3B language model

Release of Octopus V4

We are pleased to notify you that Octopus v4 is now available on Google Cloud! With three billion parameters, Octopus-V4-3B is a robust open-source language model and the master node of Nexa AI's proposed language model network. This model efficiently translates user questions into formats that other models can process. It is specifically created for the MMLU benchmark subjects.

It performs a great job of matching these inquiries with the appropriate specialised model, ensuring precise and effective inquiry processing.

Quantized Octopus V4

To run the model on-device, Google Cloud has generated quantized models in guff format.

Summary

With three billion parameters, Octopus-V4-3B is a robust open-source language model and the master node of Nexa AI's proposed language model network. This model efficiently translates user questions into formats that other models can process. It is specifically created for the MMLU benchmark subjects. It performs a great job of matching these inquiries with the appropriate specialised model, ensuring precise and effective inquiry processing.

Octopus v4: A model of graph languages

Wei Chen and Zhiyuan Li

The most sophisticated language models are usually proprietary, despite the fact that they have shown to be helpful in a variety of situations.

For example, the various models from Anthropic and the GPT-4 from OpenAI are expensive and energy-intensive.

Conversely, Llama3 and other competing models are the product of the open-source community.

Furthermore, compared to their proprietary counterparts, smaller language models that are specialised to a particular industry, like those for legal, medical, or financial tasks, have performed better.
Using \textit{functional tokens}, this work proposes a novel way to integrate \textbf{multiple open-source models}, each optimised for certain tasks.

Their newly developed Octopus v4 model can intelligently route user requests to the best vertical model and reformat the query to maximise performance using \textit{functional tokens}.

The Octopus v4 model is an improvement over the Octopus v1, v2, and v3 models. It excels at selection, parameter interpretation, and data reformatting.

Additionally, Google Cloud explores the usage of graph as a versatile data structure that effectively organises numerous open-source models by leveraging the capabilities of the Octopus model and \textit{functional tokens}.

Please accept our apologies for a more general language model graph and try the Octopus v4 models (\url{this https URL}) on publicly accessible GitHub (\url{this https URL}).

By activating models with fewer than 10B parameters, Google was able to achieve a SOTA MMLU score of 74.8 among models of the same level.

Google Cloud is grateful to Mingyuan and Zoey for their exceptional contributions to this quantization project.

Comparison and Set of Data

MMLU questions were employed to evaluate the performances.
Evaluated via the Ollama llm-benchmark methodology.

Significant Features of Octopus v4:

Compact Size: Octopus-V4-3B's compact size allows it to operate on smart devices with speed and efficiency.
Accuracy: By employing a functional token architecture, Octopus-V4-3B is able to map user queries to the specialised model with accuracy, increasing precision.

Reformat inquiry: Octopus-V4-3B helps to convert unstructured human language into a more formal framework, improving the inquiry's description and yielding more precise responses.

The characteristics and abilities of Octopus V4

Despite being a complex system, Octopus V4's primary features and capabilities are as follows:

What is the Octopus V4?

It's an advanced language model developed by Nexa AI and available as open-source software.
It acts as a "master node" in a network of other open-source AI models.
Even while Octopus V4 doesn't directly generate text or code, it is crucial for directing user requests to the most suitable AI model in the network.
In what ways does the Octopus V4 work?
Classification of Queries: When evaluating your query, Octopus V4 determines which worker node (specialised AI model) is most suited to handle it.

Reformatting Query: Octopus V4 can reorganise your natural language query into a more formal and understandable shape for the worker model. This ensures that the worker model receives the information needed to produce accurate results.

Good Communication: It facilitates communication between you and the worker model, ensuring a smooth exchange of information and the retrieval of the required data.

Octopus V4 offers the following benefits: Enhanced Accuracy: By matching your query with the most appropriate AI model, it assists you in obtaining more relevant and accurate results.

Enhanced Efficiency: The query reformatting tool makes the AI interface faster and more efficient overall.

Increased Variety of Uses: It can perform more tasks and functions because it uses multiple AI models.

Applications of Octopus V4 Information Retrieval: Consider asking a difficult question on a scientific subject. When answering your question, Octopus V4 might make reference to a specialised AI model based on scientific data, which would produce a more accurate conclusion than a general-purpose language model.
Data analysis: If you have a large dataset and require insights, Octopus V4 can route your query to an AI model trained on data analysis tasks. This model will yield enlightening outcomes.
Code Generation: It can help with certain programming jobs by connecting programmers with AI models that are proficient in code generation.