Loxa Pro

Model Description

Loxa Pro is a high-quality Large Language Model (LLM) designed to excel in a wide range of tasks, including:

Mathematics: Solving mathematical problems, performing calculations, and providing mathematical explanations.
Coding: Generating code snippets, debugging code, and answering programming-related questions.
General Questions: Answering a wide variety of general knowledge questions accurately and comprehensively.
Science Questions: Providing information and explanations on various scientific topics.
Daily Tasks: Assisting with everyday tasks, such as writing emails, setting reminders, generating to-do lists, and more.

Loxa Pro employs the CA (Combine Architectures) method, which enables it to effectively address diverse queries and tasks. This model surpasses its predecessors, Loxa-4B and Loxa-3B, in terms of accuracy and performance.

Key Features

High Accuracy: Loxa Pro demonstrates superior accuracy compared to Loxa-4B and Loxa-3B, providing more reliable and precise responses.
Broad Capabilities: Handles a diverse range of tasks, from complex mathematical problems and coding challenges to general knowledge and everyday tasks.
Optimized for Efficiency: The model is well-optimized to run efficiently even on smaller GPUs, making it accessible for users with limited computational resources.
CA (Combine Architectures) Method: Leverages the CA method to effectively combine different architectural strengths, enhancing overall performance.

Intended Use

Loxa Pro is intended for a wide range of applications, including:

Research: As a tool for research in natural language processing, artificial intelligence, and related fields.
Education: As an educational aid for students and educators in various subjects.
Development: As a component in building intelligent applications, chatbots, and virtual assistants.
Personal Use: As a versatile tool to assist with daily tasks, answer questions, and provide information.

Limitations

Potential Biases: Like all LLMs, Loxa Pro may reflect biases present in its training data.
Factual Accuracy: While highly accurate, the model may occasionally generate incorrect or misleading information. It is always recommended to verify information from multiple sources.
Resource Requirements: Although optimized, the model still requires a certain level of computational resources to run effectively.

How to Use

Example with transformers.pipeline:

from transformers import pipeline

messages = [
    {"role": "user", "content": "Write softmax formula in math style for me"},
]
pipe = pipeline("text-generation", model="explorewithai/LoxaPro", device_map = "cuda")
pipe(messages)

frameai
/

LoxaPro