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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
---
# 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"])
``` |