Daemontatox/Cogito-Maximus

Model Overview

This model, Cogito-Maximus, is a fine-tuned version of the unsloth/qwen2.5-72b-instruct-bnb-4bit base model, optimized for advanced text generation tasks. It leverages the power of Unsloth and Huggingface's TRL (Transformer Reinforcement Learning) library to achieve faster training and improved performance.

Key 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

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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.

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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.

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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.

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How to Use

Installation

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

pip install transformers torch bitsandbytes unsloth trl

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))

@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}}
}