---
base_model:
- unsloth/QwQ-32B-Preview
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- reason
- Chain-of-Thought
- deep thinking
license: apache-2.0
language:
- en
datasets:
- bespokelabs/Bespoke-Stratos-17k
- Daemontatox/Deepthinking-COT
- Daemontatox/Qwqloncotam
- Daemontatox/Reasoning_am
library_name: transformers
new_version: Daemontatox/PathfinderAI5.0
pipeline_tag: text-generation
---
![image](./image.webp)
# **PathfinderAI 5.0**

## **Model Overview**

This model is a fine-tuned version of**unsloth/QwQ-32B-Preview**, based on the **Qwen2.5** architecture. It has been optimized using **Unsloth** for significantly improved training efficiency, reducing compute time by **2x** while maintaining high performance across various NLP benchmarks. 

Fine-tuning was performed using **Hugging Face’s TRL (Transformers Reinforcement Learning) library**, ensuring adaptability for **complex reasoning, natural language generation (NLG), and conversational AI** tasks.

## **Model Details**

- **Developed by:** Daemontatox  
- **Base Model:** unsloth/QwQ-32B-Preview
- **License:** Apache-2.0  
- **Model Type:** Qwen2-based large-scale transformer  
- **Optimization Framework:** [Unsloth](https://github.com/unslothai/unsloth)  
- **Fine-tuning Methodology:** LoRA (Low-Rank Adaptation) & Full Fine-Tuning  
- **Quantization Support:** 4-bit and 8-bit for deployment on resource-constrained devices  
- **Training Library:** [Hugging Face TRL](https://huggingface.co/docs/trl/)  

---

## **Training & Fine-Tuning Details**

### **Optimization with Unsloth**
Unsloth significantly accelerates fine-tuning by reducing memory overhead and improving hardware utilization. The model was fine-tuned **twice as fast** as conventional methods, leveraging **Flash Attention 2** and **PagedAttention** for enhanced performance.



### **Fine-Tuning Method**
The model was fine-tuned using **parameter-efficient techniques**, including:
- **QLoRA (Quantized LoRA)** for reduced memory usage.  
- **Full fine-tuning** on select layers to maintain original capabilities while improving specific tasks.  
- **RLHF (Reinforcement Learning with Human Feedback)** for improved alignment with human preferences.  

---


---

## **Intended Use & Applications**

### **Primary Use Cases**
- **Conversational AI**: Enhances chatbot interactions with **better contextual awareness** and logical coherence.  
- **Text Generation & Completion**: Ideal for **content creation**, **report writing**, and **creative writing**.  
- **Mathematical & Logical Reasoning**: Can assist in **education**, **problem-solving**, and **automated theorem proving**.  
- **Research & Development**: Useful for **scientific research**, **data analysis**, and **language modeling experiments**.  

### **Deployment**
The model supports **4-bit and 8-bit quantization**, making it **deployable on resource-constrained devices** while maintaining high performance.

---

## **Limitations & Ethical Considerations**

### **Limitations**
- **Bias & Hallucination**: The model may still **generate biased or hallucinated outputs**, especially in **highly subjective** or **low-resource** domains.  
- **Computation Requirements**: While optimized, the model **still requires significant GPU resources** for inference at full precision.  
- **Context Length Constraints**: Long-context understanding is improved, but **performance may degrade** on extremely long prompts.

### **Ethical Considerations**
- **Use responsibly**: The model should not be used for **misinformation**, **deepfake generation**, or **harmful AI applications**.  
- **Bias Mitigation**: Efforts have been made to **reduce bias**, but users should **validate outputs** in sensitive applications.  

---

## **How to Use the Model**

### **Example Code for Inference**

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Daemontatox/PathFinderAI5.0"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "Explain the significance of reinforcement learning in AI."
inputs = tokenizer(input_text, return_tensors="pt")

output = model.generate(**inputs, max_length=200)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Using with Unsloth (Optimized LoRA Inference)

from unsloth import FastAutoModelForCausalLM

model = FastAutoModelForCausalLM.from_pretrained(model_name,
    load_in_4bit=True  # Efficient deployment
)


---
```

## Acknowledgments

Special thanks to:

**Unsloth AI** for their efficient fine-tuning framework.

The open-source AI community for continuous innovation.


---