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llama2_7b_chat_uncensored is a fine-tuned version of the Llama-2 7B model, created by George Sung. The model was fine-tuned on an uncensored/unfiltered Wizard-Vicuna conversation dataset ehartford/wizard_vicuna_70k_unfiltered using QLoRA. It was trained for one epoch on a 24GB GPU instance, taking around 19 hours. The model is available in a fp16 format on the Hugging Face platform. Bloke has also created GGML and GPTQ versions of the model for improved performance and lower resource usage on llama2_7b_chat_uncensored-GGML and llama2_7b_chat_uncensored-GPTQ respectively. Model inputs and outputs Inputs Text prompts**: The model is designed to accept text prompts in a conversational style, with the prompt structured as a human-response dialog. Outputs Text responses**: The model generates coherent and relevant text responses based on the provided prompt. Capabilities The llama2_7b_chat_uncensored model demonstrates strong conversational abilities, providing natural and informative responses to a wide range of prompts. It excels at engaging in open-ended discussions, answering questions, and generating text in a conversational style. What can I use it for? This model can be useful for building conversational AI assistants, chatbots, or interactive storytelling applications. Its uncensored nature and focus on open-ended conversation make it well-suited for applications where a more natural, unfiltered dialogue is desired, such as creative writing, roleplay, or exploring complex topics. Things to try One interesting aspect of this model is its approach to handling potentially sensitive topics or language. Unlike some models that attempt to censor or sanitize user input, the llama2_7b_chat_uncensored model provides direct and matter-of-fact responses without making assumptions about the user's intent or morality. This can lead to thought-provoking discussions about the role of AI in navigating complex social and ethical considerations.

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Updated 5/28/2024