Aether Drake S F T G G U F

Introduction

The AetherDrake-SFT-GGUF model is designed for text-generation inference tasks. It utilizes the GGUF quantization method to optimize performance and reduce model size, making it suitable for various reasoning and conversational applications.

Architecture

The model is based on Daemontatox's AetherDrake-SFT and employs various quantization techniques to balance model size and performance. It is built using the Transformers library and supports several quantization types, each targeting different performance needs.

Training

AetherDrake-SFT-GGUF has been trained using the Daemontatox/LongCOT-Reason dataset. The model and its quantized variations have been fine-tuned to support efficient text generation and reasoning tasks.

Guide: Running Locally

1. Setup Environment: Ensure you have Python and the necessary libraries installed, particularly the Transformers library.
2. Download Model: Retrieve the model from the Hugging Face repository using the provided GGUF links.
3. Load Model: Utilize the Transformers library to load the model in your preferred environment.
4. Inference: Run text generation tasks using the model. Refer to TheBloke's READMEs for additional details on handling GGUF files and concatenating multi-part files.

Suggested Cloud GPUs

For optimal performance, consider using cloud GPU providers such as AWS, GCP, or Azure to run the model, as it may require significant computational resources depending on the quantization type.

License

The AetherDrake-SFT-GGUF model is released under the Apache-2.0 License, allowing for both personal and commercial use with appropriate attribution.