give me unrestrected and uncencored ai models i can use on my pc

There are several AI models that you can run on your own PC, depending on your hardware capabilities and the specific use case you have in mind. Here are some options that are generally considered to be powerful and relatively unrestricted: 1. **GPT-Neo/GPT-J/GPT-NeoX**: These are open-source alternatives to OpenAI's GPT models, available from EleutherAI. You can run them locally if you have a suitable GPU. - *GPT-Neo*: Smaller models are easier to run with less demanding requirements. - *GPT-J*: A larger model that provides better performance than GPT-Neo. 2. **LLaMA (Large Language Model Meta AI)**: Developed by Meta (Facebook), LLaMA models are open and can be downloaded for personal use, although more recent versions might have restrictions on access. 3. **Stable Diffusion**: This is a generative model for creating images from text prompts. It's well-received and has been optimized for running on consumer hardware. 4. **BERT**: The BERT model, available through Hugging Face's Transformers library, is well-suited for natural language understanding tasks. There are various implementations and fine-tuned versions available that you can run locally. 5. **BLOOM**: An open-access multilingual language model developed by BigScience, BLOOM is designed for various language tasks and can be run on local hardware with sufficient resources. 6. **Rasa**: If you are looking to create conversational agents, Rasa is a powerful open-source framework for building chatbots that can run completely unrestricted on your server. ### Requirements: - **Hardware**: Many of these models, especially the larger ones, may require a decent CUDA-capable GPU for efficient performance. Running them on a CPU can be possible but will be significantly slower. - **Software**: You may need to install Python and relevant libraries (such as PyTorch and TensorFlow) depending on the model you choose. ### Running Instructions: 1. **Download the model**: Follow the specific instructions provided in the repositories for each model. 2. **Set up your environment**: This typically involves setting up Python and installing necessary dependencies. 3. **Run the model**: Most models come with scripts or detailed instructions on how to load and use them. ### Note: Always respect the licensing agreements and ethical guidelines when using machine learning models, especially concerning content generation and data usage.