Thor v1.2 8b 1024k i1 G G U F

Introduction

THOR-V1.2-8B-1024K-I1-GGUF is a model variant created by mradermacher, based on the Thor-v1.2-8b-1024k model from MrRobotoAI. It has been quantized to improve performance and reduce resource requirements.

Architecture

The model is built using the Transformers library and supports various quantization methods, including GGUF, which helps in optimizing model size and inference speed. The quantization process involves creating versions with different levels of precision, suitable for diverse use cases and hardware capabilities.

Training

This model involves weighted/imatrix quantization techniques. Various quantized versions are available, each optimized for specific performance characteristics. The quantization types are categorized by size and quality, with IQ-quants often preferred over similar non-IQ quants.

Guide: Running Locally

1. Environment Setup: Ensure you have Python installed along with the Transformers library.
2. Model Download: Access the desired quantized model version from Hugging Face's model repository.
3. Run Inference: Utilize the model with your data, ensuring compatibility with GGUF files. Refer to TheBloke's READMEs for handling multi-part files.
4. Hardware Recommendations: Running locally may require substantial computational resources. Consider using cloud-based GPUs from providers like AWS, Google Cloud, or Azure for enhanced performance.

License

The model usage is subject to the licensing terms provided on the Hugging Face model card. Ensure compliance with these terms when deploying or modifying the model.