CogitoZ - 32B

Model Overview

CogitoZ - 32B 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, 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
• Finetuned To: Daemontatox/CogitoZ

---

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:

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.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here! Summarized results can be found here!

Metric	Value (%)
Average	38.36
IFEval (0-Shot)	39.67
BBH (3-Shot)	53.89
MATH Lvl 5 (4-Shot)	46.30
GPQA (0-shot)	19.35
MuSR (0-shot)	19.94
MMLU-PRO (5-shot)	51.03