Cogito Maximus

🚀 Cogito-Maximus

Cogito-Maximus is a fine - tuned model based on Qwen2.5 - 72B, optimized for advanced text generation tasks with faster training and improved performance.

🚀 Quick Start

Installation

To use this model, ensure you have the following libraries installed:

pip install transformers torch bitsandbytes unsloth trl

Usage Examples

Basic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the tokenizer and model
model_name = "Daemontatox/Cogito-Maximus"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_4bit=True)

# Generate text
input_text = "Explain the concept of machine learning in simple terms."
inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_length=100)

# Decode and print the output
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

✨ Features

• Base Model: unsloth/qwen2.5-72b-instruct
• Training Acceleration: Trained 2x faster using Unsloth.
• Fine - Tuning Framework: Utilizes Huggingface's TRL library.
• Optimized for Inference: Ready for deployment in text - generation tasks with efficient inference capabilities.
• License: Apache - 2.0

📚 Documentation

Model Details

Developed by

• Author: Daemontatox
• Organization: Independent Contributor

Tags

• Text Generation Inference
• Transformers
• Unsloth
• Qwen2
• TRL

Language

• English (en)

License

This model is released under the Apache - 2.0 License, which allows for free use, modification, and distribution, provided the original license and copyright notice are included.

Model Training

Base Model

The model is derived from the unsloth/qwen2.5-72b-instruct, a version of the Qwen2.5 - 72B instruction - tuned model. The base model is optimized for efficiency using bitsandbytes (bnb) 4 - bit quantization.

Training Process

• Framework: The model was fine - tuned using Unsloth, a library designed to accelerate the training of large language models.
• Acceleration: Training was completed 2x faster compared to traditional methods, thanks to Unsloth's optimizations.
• Reinforcement Learning: Fine - tuning incorporated techniques from Huggingface's TRL library, enabling advanced instruction - tuning and alignment with human preferences.

Intended Use

Primary Use Case

This model is designed for text generation tasks, including but not limited to:

• Instruction - following
• Question answering
• Content creation
• Dialogue systems

Limitations

• The model is trained primarily on English data and may not perform as well on other languages.
• While fine - tuned for instruction - following, outputs should be reviewed for accuracy and relevance in critical applications.

📄 License

This model is released under the Apache - 2.0 License, which allows for free use, modification, and distribution, provided the original license and copyright notice are included.

Citation

@misc{daemontatox_cogito_maximus,
  author = {Daemontatox},
  title = {Cogito-Maximus: Fine-tuned Qwen2.5-72B Instruct Model},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Hugging Face Model Repository},
  howpublished = {\url{https://huggingface.co/Daemontatox/Cogito-Maximus}}
}