Best uncensored ai stored on local device

If you're looking for an AI model that can be run locally, there are several options that you can consider. These models typically require a good amount of computational resources, especially if you want to run larger models effectively. Here are some popular choices: 1. **GPT-J or GPT-NeoX**: - Developed by EleutherAI, these are open-source models that can be fine-tuned and run on local machines. GPT-NeoX is particularly aimed at larger models and can deliver strong performance. 2. **LLaMA (Large Language Model Meta AI)**: - Developed by Meta (Facebook), LLaMA models are also open-source and designed to be run efficiently on local hardware. You may need to check the licensing and access terms. 3. **BLOOM**: - The BigScience project developed BLOOM, an open-access multilingual language model. It can be downloaded and run locally, though it may require significant resources. 4. **Alpaca**: - A fine-tuned version of LLaMA designed for instruction-following tasks. It's more lightweight and can be easier to run on consumer hardware. 5. **Stable Diffusion**: - If you're interested more in generative models for images rather than text, Stable Diffusion allows for powerful local image generation. ### Hardware Requirements Running these models efficiently often requires a modern GPU with ample VRAM. NVIDIA GPUs are generally recommended due to their support for CUDA, which is critical for many machine learning tasks. ### Installation and Usage To run these models locally, you will typically need: - Python installed on your machine. - Appropriate libraries such as TensorFlow or PyTorch. - Access to the model weights (often downloaded separately). ### Ethical Considerations When running uncensored models, it's important to be mindful of ethical implications, including the potential for generating harmful or misleading content. Always consider the impact and handle the AI responsibly. ### Conclusion Each of these options has strengths and weaknesses depending on your use case. If you're new to running AI models locally, it may take some experimentation to find the best fit for your needs. Always consult the documentation specific to each model for detailed installation and usage instructions.