t5 v1_1 xxl encoder gguf

Introduction
This document provides an overview and usage guide for the GGUF conversion of Google's T5 v1.1 XXL encoder model, which has been quantized by city96. The model is designed for English language processing and is compatible with GGUF libraries.

Architecture
The base model for this conversion is Google's T5 v1.1 XXL. The conversion utilizes the GGUF library, with the primary focus on encoding tasks. The quantization has been tailored to optimize performance while maintaining a balance between model size and resource efficiency.

Training
The model was originally developed by Google and later converted to GGUF format by city96. This conversion does not support imatrix creation for T5 models, which affects the quantization strategy. Users are advised to use Q5_K_M or larger quantization levels for optimal performance, although smaller models may suffice in limited resource environments.

Guide: Running Locally

1. Prerequisites: Ensure you have the GGUF library installed.
2. Model Setup: Download the model weights from the Hugging Face repository.
3. Usage:
   • Use the llama-embedding tool from GitHub for embeddings.
   • Alternatively, employ the ComfyUI-GGUF custom node for integration with image generation models, available on GitHub.

For optimal performance, especially for large-scale tasks, consider using cloud-based GPUs like those offered by AWS, Google Cloud, or Azure.

License
The model and its conversion are distributed under the Apache 2.0 license, permitting wide usage and modification rights with proper attribution.