Tulu 3.1 8 B Super Nova i1 G G U F

Introduction

TULU-3.1-8B-SUPERNOVA-I1-GGUF is an English language model quantized by mradermacher using the Transformers library. This model offers a variety of quantized formats, optimized for different use cases, and is based on the bunnycore/Tulu-3.1-8B-SuperNova model.

Architecture

The model architecture is built upon the Tulu-3.1-8B-SuperNova base model and incorporates various quantized formats, primarily categorized by size and quality. These quantizations utilize the GGUF format and are designed to optimize performance across different hardware configurations.

Training

The quantized versions of this model are created using a combination of weighted and imatrix quant techniques. These methods are tailored to balance performance and model size, with the quantization process allowing for efficient inference.

Guide: Running Locally

To run the TULU-3.1-8B-SUPERNOVA-I1-GGUF model locally, follow these basic steps:

1. Install Dependencies: Ensure that you have Python and the Hugging Face Transformers library installed.
2. Download Model Files: Access the model files from the Hugging Face repository.
3. Load the Model: Use the Transformers library to load the model into your environment.
4. Inference: Run inference tasks using the loaded model.

Cloud GPUs: For optimal performance, consider using cloud-based GPUs such as those offered by AWS, Google Cloud, or Azure. These platforms can provide the necessary computational power to handle large-scale model inference efficiently.

License

The usage and distribution of this model are subject to the terms outlined by the Hugging Face platform and the individual contributors. Always check the specific licensing details provided in the repository for compliance.