Thor-v1.1e-8b-1024k-i1-GGUF

Introduction

Thor-v1.1e-8b-1024k-i1-GGUF is a machine learning model that utilizes the GGUF format to provide various quantization options. This model is based on the MrRobotoAI/Thor-v1.1e-8b-1024k framework, offering enhanced performance through optimized quantization techniques.

Architecture

The model leverages the Transformers library and supports English language processing. It is designed with various quantization types to balance between size, speed, and quality, catering to different use cases and computational resources.

Training

Thor-v1.1e-8b-1024k-i1-GGUF is quantized by the user mradermacher, using techniques such as weighted and imatrix quantization. Static quantizations are also available for users requiring different performance characteristics.

Guide: Running Locally

1. Download the Model: Access the model and quantization files from the Hugging Face repository: Thor-v1.1e-8b-1024k-i1-GGUF.
2. Set Up Environment: Ensure your environment is configured with the Transformers library.
3. Run the Model: Follow TheBloke's README for guidance on using GGUF files and concatenating multi-part files.
4. Choose a Quantization: Select a quantization type based on your hardware capabilities and required performance. IQ-quants are generally recommended for quality.
5. Cloud GPUs: For enhanced performance, consider using cloud GPU services such as AWS, Google Cloud, or Azure.

License

The model's usage terms are subject to the licensing agreements specified on the Hugging Face platform. Please review the repository for detailed license information.