caedencode
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Caeden-o1

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 ---
 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
+# Load the model and tokenizer
+model = AutoModelForCausalLM.from_pretrained("caedenai/fine-tuned")
+tokenizer = AutoTokenizer.from_pretrained("caedenai/fine-tuned")
+# Use appropriate device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
+# Generate an answer
+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)
+# Example usage
+question = "What is the largest planet in our solar system?"
+answer = generate_answer(question)
+print(answer)
+```