Neural DareDevil 8B ๐Ÿ˜ˆ: The fastest LLama3 8B Finetune on earth!

The text discusses the release of the Neural Daredevil 8B model by developer Maxim Lebon in the open-source LLM space, claiming to have the highest MML score among 8 billion parameter models on the open LLM leaderboard. The model, a merge of nine versions of the Llama 3 model, utilizes innovative techniques like merging algorithms and fine-tuning methods such as DPO and obliteration to enhance performance in role-playing tasks, spatial reasoning, algebra, and programming questions.

This text discusses the release of a new fine-tuned model called Neural Daredevil 8B by developer Maxim Lebon in the open-source LLM space. This model claims to have the highest MML score among 8 billion parameter models on the open LLM leaderboard. The model is a merge of nine different versions of the Llama 3 model, utilizing specific fine-tuning techniques like DPO and obliteration to achieve impressive performance numbers. The focus of this model is on maximizing the MML score as much as possible, showcasing advancements in merging algorithms and performance improvement methods.

The use of merging algorithms like Dare and Ties for combining different versions of Llama 3 models is highlighted as an innovative approach in improving model performance. The text delves into the concept of obliteration, which involves selectively removing parts of an AI model to enhance its performance. The author emphasizes that DPO fine-tuning can recover performance losses caused by the obliteration process, showcasing a unique way to improve uncensored fine-tuned models.

The modelโ€™s intended application is to be a higher-performing version of the Instruct models of Llama 3, without requiring alignment. It is designed to be easy to use for role-playing tasks and already has available quants like GGF EXL 2 and Olama quants. Maxim Lebon aimed to secure the top spots on the open LLM leaderboard for 8 billion parameter models, further emphasizing the modelโ€™s capabilities. The evaluations of the model extend to the No Evaluation Suite, indicating its versatility and potential for various tasks.

The text explores the modelโ€™s performance in various tasks, such as role-playing scenarios, spatial reasoning, basic algebra, and programming questions. The model demonstrates a mix of creativity, memorization, and problem-solving skills, showcasing its potential for a wide range of applications. Despite some instances of confusion, the modelโ€™s responses indicate a level of understanding and capability to handle different types of inquiries. Overall, the Neural Daredevil 8B model presents a significant advancement in the open-source LLM space, with promising performance metrics and diverse application possibilities.