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Davidkim205

Models by this creator

Rhea-72b-v0.5

davidkim205

Total Score

95

The Rhea-72b-v0.5 model is a powerful language model developed by davidkim205 as part of the Rhea project, which conducts research on various learning methods to improve large language model (LLM) performance. This model was fine-tuned using the nox framework and a dataset created with a novel method called Self-Generated Dataset Creation for DPO Learning (SGD). The Rhea-72b-v0.5 model has ranked first on the HuggingFace Open LLM leaderboard. The Rhea project's SGD method proposes a technique where sentences generated by the model are compared with the actual correct answers from an existing dataset, and sentences where the model's generated results do not match the correct answers are added. This enables the model to autonomously create training data, thereby enhancing the performance of DPO (self-supervised learning) models. Model inputs and outputs Inputs Text prompts for the model to continue or generate Outputs Continued or generated text based on the input prompts Capabilities The Rhea-72b-v0.5 model demonstrates impressive performance on a variety of benchmark tasks, including the GPT4All, AGIEval, and BigBench datasets. It has achieved top rankings on several specific tasks, such as ARC-c, ARC-e, Hellaswag, and OpenBookQA. What can I use it for? The Rhea-72b-v0.5 model is a versatile language model that can be used for a wide range of text-based tasks, such as: Content generation (e.g., stories, articles, poems) Question answering Summarization Text-to-text translation Code generation and programming assistance Additionally, the model's strong performance on various benchmarks suggests it could be useful for more advanced applications, such as dialogue systems, task-oriented agents, and even general intelligence tasks. Things to try One key insight about the Rhea-72b-v0.5 model is its use of the SGD method for DPO learning. This approach of having the model autonomously generate its own training data is a novel and interesting technique that could lead to further advancements in self-supervised learning for language models. Researchers and developers may want to explore how this method can be applied to other model architectures or domains beyond language modeling.

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Updated 5/15/2024