README.md

metadata

tags:
  - text-generation
  - llm
  - ai
  - artificial intelligence
  - large language model
  - math
  - coding
  - science
  - daily tasks
license: apache-2.0
library_name: transformers

Laxo Pro

Model Description

Laxo 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.

Laxo 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: Laxo 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

Laxo 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, Laxo 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

[Instructions on how to download, load, and use the model would go here. This is crucial for users to interact with your model. Include code examples using the transformers library.]

Example with transformers (replace with actual loading and usage code):

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "frameai/LaxoPro" # Replace with your model's name on Hugging Face
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage:
input_text = "What is the capital of France?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(decoded_output)