---
library_name: transformers
tags:
- conversational
- chain-of-thought
- education
- llama-cpp
- gguf-my-repo
base_model: caedencode/Caeden-o1
---

# Triangle104/Caeden-o1-Q5_K_S-GGUF
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
	




	
		
	

		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)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Triangle104/Caeden-o1-Q5_K_S-GGUF --hf-file caeden-o1-q5_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Caeden-o1-Q5_K_S-GGUF --hf-file caeden-o1-q5_k_s.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```

Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```

Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Caeden-o1-Q5_K_S-GGUF --hf-file caeden-o1-q5_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Caeden-o1-Q5_K_S-GGUF --hf-file caeden-o1-q5_k_s.gguf -c 2048
```