README.md

---
base_model:
- Qwen/QwQ-32B-Preview
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
metrics:
- accuracy
new_version: Daemontatox/CogitoZ
library_name: transformers
---
![image](./image.webp)
# CogitoZ - Qwen2

## Model Overview

CogitoZ - Qwen2 is a state-of-the-art large language model fine-tuned to excel in advanced reasoning and real-time decision-making tasks. This enhanced version was trained using [Unsloth](https://github.com/unslothai/unsloth), achieving a 2x faster training process. Leveraging Hugging Face's TRL (Transformers Reinforcement Learning) library, CogitoZ combines efficiency with exceptional reasoning performance.

- **Developed by**: Daemontatox
- **License**: Apache 2.0
- **Base Model**: [Qwen/QwQ-32B-Preview](https://huggingface.co/Qwen/QwQ-32B-Preview)
- **Finetuned from**: [Daemontatox/CogitoZ](https://huggingface.co/Daemontatox/CogitoZ)

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

---

## Key Features

1. **Fast Training**: Optimized with Unsloth, achieving a 2x faster training cycle without compromising model quality.
2. **Enhanced Reasoning**: Utilizes advanced chain-of-thought (CoT) reasoning for solving complex problems.
3. **Quantization Ready**: Supports 8-bit and 4-bit quantization for deployment on resource-constrained devices.
4. **Scalable Inference**: Seamless integration with text-generation-inference tools for real-time applications.

---

## Intended Use

### Primary Use Cases
- **Education**: Real-time assistance for complex problem-solving, especially in mathematics and logic.
- **Business**: Supports decision-making, financial modeling, and operational strategy.
- **Healthcare**: Enhances diagnostic accuracy and supports structured clinical reasoning.
- **Legal Analysis**: Simplifies complex legal documents and constructs logical arguments.

### Limitations
- May produce biased outputs if the input prompts contain prejudicial or harmful content.
- Should not be used for real-time, high-stakes autonomous decisions (e.g., robotics or autonomous vehicles).

---

## Technical Details

- **Training Framework**: Hugging Face's Transformers and TRL libraries.
- **Optimization Framework**: Unsloth for faster and efficient training.
- **Language Support**: English.
- **Quantization**: Compatible with 8-bit and 4-bit inference modes for deployment on edge devices.

### Deployment Example

#### Using Hugging Face Transformers:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Daemontatox/CogitoZ"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "Explain the Pythagorean theorem step-by-step:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Optimized Inference:
Install the transformers and text-generation-inference libraries.
Deploy on servers or edge devices using quantized models for optimal performance.
Training Data
The fine-tuning process utilized reasoning-specific datasets, including:


 **MATH Dataset**: Focused on logical and mathematical problems.
 
 **Custom Corpora**: Tailored datasets for multi-domain reasoning and structured problem-solving.


## Ethical Considerations
**Bias Awareness** **->** The model reflects biases present in the training data. Users should carefully evaluate outputs in sensitive contexts.

**Safe Deployment** **->**  Not recommended for generating harmful or unethical content.

## Acknowledgments
This model was developed with contributions from Daemontatox and the Unsloth team, utilizing state-of-the-art techniques in fine-tuning and optimization.

For more information or collaboration inquiries, please contact:

Author: Daemontatox
GitHub: Daemontatox GitHub Profile
Unsloth: Unsloth GitHub