Update README.md

README.md

@@ -13,6 +13,102 @@ base_model: caedencode/Caeden-o1
 This model was converted to GGUF format from [`caedencode/Caeden-o1`](https://huggingface.co/caedencode/Caeden-o1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/caedencode/Caeden-o1) for more details on the model.
 
+---
+Model details
+-
+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.
+
+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
+	
+
+
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+	
+		
+	
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+		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)
+
+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)