---
license: apache-2.0
tags:
- text-generation
- instruction-tuned
- llama
- gguf
- chatbot
- code
library_name: llama.cpp
language:
- en
- rm
datasets:
- custom
model-index:
- name: Corelyn NeoH
  results: []
base_model:
- bartowski/Llama-3.2-3B-Instruct-uncensored-GGUF
---


![logo](./image/neospecyn.png)
# Corelyn NeoH GGUF Model

## Specifications :
- Model Name: Corelyn NeoH
- Base Name: NeoH-3.2
- Type: Instruct / Fine-tuned
- Architecture: LLaMA
- Size: 3B parameters
- Organization: Corelyn

## Model Overview

Corelyn NeoH is a 3-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: LLaMA

- Parameter count: 3B

- Context length: 131,072 tokens

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

- Chatbots and conversational AI

- Knowledge retrieval and Q&A

- Code and text generation

- Instruction-following tasks

## Usage

Download from : [NeoH3.2](https://huggingface.co/CorelynAI/NeoH/resolve/main/NeoH3.2.gguf?download=true)

```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/NeoH3.2.gguf")

# Create chat completion
response = llm.create_chat_completion(
    messages=[{"role": "user", "content": "Create a Haiku about AI"}]
)

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


```