---
license: apache-2.0
tags:
- text-generation
- instruction-tuned
- maincoder
- gguf
- chatbot
library_name: llama.cpp
language: en
datasets:
- custom
model-index:
- name: Corelyn Leonicity Leon
  results: []
base_model:
- yourGGUF/Maincoder-1B_GGUF
---

![logo](./images/leon.png)

# Corelyn Leon GGUF Model

## Specifications :
- Model Name: Corelyn Leonicity Leon
- Base Name: Leon_1B
- Type: Instruct / Fine-tuned
- Architecture: Maincoder
- Size: 1B parameters
- Organization: Corelyn

## Model Overview

Corelyn Leonicity Leon is a 1-billion parameter LLaMA-based instruction-tuned model, designed for general-purpose assistant tasks and knowledge extraction. It is a fine-tuned variant optimized for instruction-following use cases.

- Fine-tuning type: Instruct

- Base architecture: Maincoder

- Parameter count: 3B


### This model is suitable for applications such as:

- Algorithms

- Websites

- Python, JavaScript, Java...

- Code and text generation

## Usage

Download from : [LeonCode_1B](https://huggingface.co/CorelynAI/LeonCode/blob/main/LeonCode_1B.gguf)

```python

# pip install pip install llama-cpp-python

from llama_cpp import Llama

# Load the model (update the path to where your .gguf file is)
llm = Llama(model_path="path/to/the/file/LeonCode_1B.gguf")

# Create chat completion
response = llm.create_chat_completion(
    messages=[{"role": "user", "content": "Create a python sorting algorithm"}]
)

# Print the generated text
print(response.choices[0].message["content"])


```