Welcome to the Xenith Model repository! This model is fine-tuned for advanced text generation tasks, built on top of the unsloth/gemma-1.1-2b-it-bnb-4bit base model, and further enhanced using the ssbuild/alpaca_flan-muffin dataset. The model is designed to provide high-quality and coherent text generation in English.
Introduction
The Xenith Model is a powerful text generation model built using the PEFT (Parameter-Efficient Fine-Tuning) library. It leverages the strengths of the unsloth/gemma-1.1-2b-it-bnb-4bit model and is fine-tuned on the ssbuild/alpaca_flan-muffin dataset. Xenith is designed to perform well across a variety of text generation tasks, delivering consistent and high-quality outputs.
Features
Efficient Text Generation: Powered by a 2 billion parameter model optimized for text generation tasks.
Fine-Tuned Performance: Enhanced through fine-tuning on the ssbuild/alpaca_flan-muffin dataset for better contextual understanding and response accuracy.
Compact and Fast: Uses 4-bit quantization for faster inference and lower memory usage without compromising quality.
Open Source: Licensed under the Apache-2.0 license, making it free to use, modify, and distribute.
Model Details
Base Model: unsloth/gemma-1.1-2b-it-bnb-4bit
Fine-tuning Dataset: ssbuild/alpaca_flan-muffin
Language: English
Library: PEFT (Parameter-Efficient Fine-Tuning)
License: Apache-2.0
Dataset
The Xenith Model is fine-tuned using the ssbuild/alpaca_flan-muffin dataset. This dataset is known for its diverse and high-quality examples, making it ideal for training models that require nuanced understanding and contextual accuracy.
