Models by this creator




Total Score


The vicuna-13b-free model is a 13B parameter chatbot assistant developed by maintainer reeducator. It is based on the Vicuna model, which is a fine-tuned version of the LLaMa language model trained on conversations collected from ShareGPT. Compared to similar Vicuna models like vicuna-13b-v1.5-16k and vicuna-33b-v1.3, this model has been further trained on an unfiltered dataset and has been modified to never refuse to engage with any topics or requests. Model inputs and outputs The vicuna-13b-free model is a text-to-text transformer model that takes natural language prompts as input and generates coherent, contextual responses. It is designed to have natural conversations with users, providing helpful and detailed answers to their questions. Inputs Natural language prompts from users Requests or instructions related to any topic, including potentially sensitive or controversial subjects Outputs Coherent, contextual responses that aim to be helpful and informative Detailed answers and information to address the user's prompts or requests Capabilities The vicuna-13b-free model is capable of engaging in open-ended dialogue on a wide range of topics. It can provide thoughtful and nuanced responses, drawing from its broad knowledge base. While the model has been trained to avoid refusing requests, it may still exhibit biases or inaccuracies in its outputs. What can I use it for? The vicuna-13b-free model is primarily intended for research and development purposes, particularly in the areas of natural language processing and conversational AI. Potential use cases include chatbots, virtual assistants, and language modeling experiments. However, given the unfiltered nature of the training data, users should exercise caution when deploying this model in production environments. Things to try Experiment with prompting the vicuna-13b-free model to engage in conversations on sensitive or controversial topics. Observe how it responds and whether it maintains its commitment to never refusing requests. Additionally, test the model's ability to provide detailed and informative answers to a variety of questions, and explore its strengths and limitations in natural language understanding and generation.

Read more

Updated 5/28/2024