neovalle/H4rmoniousBreeze

Model Overview

neovalle/H4rmoniousBreeze is a 7 billion parameter language model, developed by Jorge Vallego and funded by Neovalle Ltd. It is fine-tuned from the HuggingFaceH4/zephyr-7b-beta model, which is based on the Mistral architecture. The primary differentiator of this model is its fine-tuning using the H4rmony dataset, which aims to align the model with ecological values through ecolinguistics principles. This model is primarily English-language capable and operates under an MIT license.

Key Capabilities & Purpose

• Ecological Alignment: Fine-tuned specifically to incorporate ecological values and principles, making it unique among general-purpose LLMs.
• Proof-of-Concept: Serves as a testing ground to demonstrate the impact and effectiveness of the H4rmony dataset.
• Evaluation Tool: Intended for testing and continuous improvement of the H4rmony dataset itself, providing insights into ecological alignment.

Intended Use Cases

• Dataset Evaluation: Ideal for researchers and developers interested in evaluating the effects of the H4rmony dataset on model behavior and ecological alignment.
• Experimental Testing: Suitable for testing purposes to gain insights into ecolinguistics principles applied to LLMs.

Limitations

• Not for Direct Application: The model is currently under testing and is not recommended for direct use in production applications.
• Potential Biases: May exhibit biases inherited from its base model or unintentionally introduced during fine-tuning.

For those looking to get started, the model can be loaded and run in a Colab instance with high RAM, and comparative code is available on the Neovalle GitHub repository.