neovalle/H4rmoniousBreezeDPO
neovalle/H4rmoniousBreezeDPO is a 7 billion parameter Mistral-based language model developed by Jorge Vallego and funded by Neovalle Ltd. It is fine-tuned from HuggingFaceH4/zephyr-7b-beta using Direct Preference Optimization (DPO) with the H4rmony_dpo dataset. This model is primarily intended as a proof-of-concept to demonstrate the effects of ecological alignment through ecolinguistics principles, focusing on specific testing and evaluation of the H4rmony dataset.
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Overview
neovalle/H4rmoniousBreezeDPO is a 7 billion parameter language model, fine-tuned by Jorge Vallego from the HuggingFaceH4/zephyr-7b-beta base model. This model utilizes Direct Preference Optimization (DPO) with the custom H4rmony_dpo dataset, which is designed to align the model with ecological values through ecolinguistics principles. It serves as a Proof of Concept (PoC) to showcase the impact of this specific dataset and fine-tuning approach on ecological alignment.
Key Capabilities
- Ecological Alignment Research: Primarily developed for testing and evaluating the effects of the H4rmony_dpo dataset on model responses.
- DPO Fine-tuning Demonstration: Illustrates the application of DPO for value alignment using a specialized dataset.
Good for
- Dataset Evaluation: Ideal for researchers and developers interested in assessing the H4rmony_dpo dataset's effectiveness.
- Ecological Linguistics Studies: Useful for exploring how ecolinguistics principles can be integrated into LLM fine-tuning.
- Proof-of-Concept Testing: Suitable for gaining insights into the continuous improvement of ecologically-aligned models.
Note: This model is currently under testing for a specific task (Ecological Alignment) and its direct use in production applications is not recommended.