caedencode/Caeden-o1

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:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("caedencode/Caeden-o1")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-1.5B")

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)