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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
---
# 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"])
``` |