---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen2.5-72B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- reasoning
- logic
- cot
- text-generation-inference
new_version: Daemontatox/Cogito-Maximus
---

![image](./image.webp)


## **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](https://github.com/unslothai/unsloth).
- **Fine-Tuning Framework:** Utilizes Huggingface's [TRL](https://github.com/huggingface/trl) library.
- **Optimized for Inference:** Ready for deployment in text-generation tasks with efficient inference capabilities.
- **License:** Apache-2.0

---

## **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.

---

## **How to Use**

### **Installation**
To use this model, ensure you have the following libraries installed:
```bash
pip install transformers torch bitsandbytes unsloth trl
```




```python
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}}
}
```