Failspy

Models by this creator

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llama-3-70B-Instruct-abliterated

failspy

Total Score

61

The llama-3-70B-Instruct-abliterated model is a large language model developed by the AI researcher failspy. It is based on the original Llama-3-70B-Instruct model, but has been modified to "inhibit" the model's ability to express refusal. According to the maintainer, this model has had certain weights manipulated in an attempt to reduce the model's tendency to refuse requests or lecture about ethics and safety. However, the maintainer notes that this is not guaranteed to completely prevent the model from refusing or lecturing, and it may still exhibit such behaviors. The model is intended for developers who want to experiment with this type of weight manipulation, but should be used with caution as the long-term effects are not fully known. Model inputs and outputs Inputs Text prompts Outputs Generated text responses Capabilities The llama-3-70B-Instruct-abliterated model is capable of generating human-like text responses to a variety of prompts. It can be used for tasks like conversational AI, text generation, and potentially other natural language processing applications. However, due to the experimental nature of the weight manipulation, the model's capabilities and behaviors may be unpredictable. What can I use it for? Developers interested in exploring methods to reduce language model refusal behavior could use the llama-3-70B-Instruct-abliterated model as a starting point for experimentation. The model could potentially be fine-tuned or used in conjunction with other safety mechanisms to develop conversational AI applications that are less likely to refuse requests or lecture users. However, great care should be taken when deploying such models in real-world applications, as the long-term effects of the weight manipulation are not well understood. Things to try Developers could try prompting the llama-3-70B-Instruct-abliterated model with a variety of requests, both benign and potentially sensitive, to observe how it responds. This could help identify any remaining biases or tendencies to refuse or lecture. Additionally, developers could experiment with techniques to further fine-tune or constrain the model's behavior, while monitoring for any unintended consequences or safety concerns.

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Updated 6/13/2024