Lumimaid Magnum v4 12 B i1 G G U F

Introduction

The Lumimaid-Magnum-v4-12B-i1-GGUF model is a quantized transformer model that supports English language processing. It is part of the GGUF library and utilizes mergekit for merging capabilities. This model is designed for conversational applications and provides various quantization options to meet different quality and size requirements.

Architecture

The base model for Lumimaid-Magnum-v4-12B-i1-GGUF is Undi95/Lumimaid-Magnum-v4-12B. It has been quantized by mradermacher to achieve different levels of model size and performance. The model employs the transformers library and supports weighted/imatrix quantizations, which are available as static quants.

Training

The model has been quantized into various formats, with size and quality variations to suit different use cases:

• IQ-quants ranging from 3.1 GB to 10.2 GB.
• Options such as i1-IQ1_S, i1-IQ3_M, and i1-Q6_K, among others, provide flexibility in terms of performance and storage requirements.

Guide: Running Locally

1. Prerequisites:

   • Install the Hugging Face transformers library.
   • Ensure you have Python installed on your system.

2. Setup:

   • Clone the repository or download the desired quantized GGUF file from the Hugging Face model page.
   • Follow the usage instructions provided in TheBloke’s READMEs to handle GGUF files and concatenate multi-part files if necessary.

3. Execution:

   • Load the model using the transformers library.
   • Run the model on your local machine or utilize cloud GPUs for enhanced performance. Suggested cloud GPU providers include AWS, Google Cloud, and Azure.

License

The model is hosted on Hugging Face and is subject to its usage terms and conditions. Specific licensing details can be found on the model's page or by contacting the contributor directly.