S A I N E M O re M I X G G U F LLM Model

Introduction

SAINEMO-REMIX-GGUF is a quantized version of the SAINEMO-reMIX model, which has been created using the llama.cpp framework. This model is a result of merging several pre-trained language models to enhance its performance and capabilities in both English and Russian.

Architecture

The architecture of the SAINEMO-REMIX-GGUF model is based on the combination of the following models:

IlyaGusev/saiga_nemo_12b
elinas/Chronos-Gold-12B-1.0
Vikhrmodels/Vikhr-Nemo-12B-Instruct-R-21-09-24
MarinaraSpaghetti/NemoMix-Unleashed-12B

The model employs a merge method called della_linear to integrate these models, emphasizing balance between the languages and specific role-play capabilities.

Training

The SAINEMO-REMIX-GGUF model was trained using the following configuration:

Weight and Density Parameters:
- IlyaGusev/saiga_nemo_12b: Weight 0.55, Density 0.4
- MarinaraSpaghetti/NemoMix-Unleashed-12B: Weight 0.2, Density 0.4
- elinas/Chronos-Gold-12B-1.0: Weight 0.15, Density 0.4
- Vikhrmodels/Vikhr-Nemo-12B-Instruct-R-21-09-24: Weight 0.25, Density 0.4
Merge Method: della_linear
Epsilon: 0.05
Lambda: 1
Data Type: float16
Tokenizer Source: Base model

Guide: Running Locally

To run the SAINEMO-REMIX-GGUF model locally, follow these steps:

Clone the Repository:

git clone https://huggingface.co/QuantFactory/SAINEMO-reMIX-GGUF
cd SAINEMO-reMIX-GGUF

Install Dependencies: Ensure you have Python and the required libraries installed, such as transformers.
Download the Model: Use the Hugging Face Hub to download the model files.
Run the Model: Configure your environment for text generation using a compatible framework.
Use Cloud GPUs: For optimal performance, consider using cloud-based GPU services like AWS, Google Cloud, or Azure.

License

The SAINEMO-REMIX-GGUF model and its components are subject to the licenses of the original models used in the merge. Please refer to their respective repositories for specific licensing details. Ensure compliance with these licenses when using and distributing the model.

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