README.md

---
library_name: transformers
tags: [conversational, chain-of-thought, education]
---

# CaedenAI - O1

CaedenAI is a conversational AI model fine-tuned to provide detailed reasoning in its responses using the Chain-of-Thought (CoT) methodology. It is designed for educational use, enabling users to understand the reasoning process behind answers.

## Model Details

### Model Description

- **Developed by:** Caeden Rajoo
- **Model type:** Conversational AI with CoT reasoning
- **License:** Apache 2
- **Finetuned from model:** Qwen/Qwen2.5-1.5B
- **Primary Use Case:** Education and knowledge expansion

This model is fine-tuned for generating step-by-step reasoning for queries, making it an excellent tool for educational environments and learning applications.

## Uses

### Direct Use

This model can be directly applied in:
- Educational environments to help students learn with explanations.
- Applications where detailed reasoning is required for understanding answers.
- Conversational AI systems that prioritize reasoning over simple answers.

### Out-of-Scope Use

This model may not be suitable for:
- Scenarios requiring highly specialized domain knowledge not covered in the training data.
- Tasks requiring real-time response for critical systems (e.g., healthcare, safety).

## Bias, Risks, and Limitations

The model inherits limitations from its training data and base model. Users should consider potential biases or incomplete information in responses.

### Recommendations

- The model's output should be reviewed for accuracy in critical use cases.
- Users should ensure that ethical considerations are met when using the model in sensitive environments.

## How to Get Started with the Model

Here’s how you can load and use CaedenAI:

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("caedencode/Caeden-o1")
tokenizer = AutoTokenizer.from_pretrained("caedencode/Caeden-o1")

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)

def generate_answer(question):
    prompt = f"Question: {question}\nReasoning:\n"
    inputs = tokenizer(prompt, return_tensors="pt").to(device)
    outputs = model.generate(**inputs, max_length=200, num_beams=5, early_stopping=True)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

question = "What is the largest planet in our solar system?"
answer = generate_answer(question)
print(answer)

```