What are unrestricted ai models I can run locally

As of my last knowledge update in October 2023, there are several unrestricted AI models that you can run locally on your machine. These models vary in terms of their architecture, capabilities, and resource requirements. Here’s a list of some notable options: 1. **GPT-Neo and GPT-J**: - Developed by EleutherAI, these models are open-source alternatives to OpenAI's GPT-3. - GPT-Neo comes in various sizes, with the largest being 2.7 billion parameters, while GPT-J has 6 billion parameters. - You can find them on the Hugging Face Model Hub. 2. **LLaMA (Large Language Model Meta AI)**: - Developed by Meta (Facebook), the LLaMA models are also available in various sizes. - Note that access may be more restricted than fully open models, so check the latest availability. 3. **BLOOM**: - An open multilingual language model developed by BigScience, BLOOM is an open approach to large language models and is available for local deployment. - Different versions and sizes are available, including a 176 billion parameter model. 4. **Mistral**: - The Mistral team has developed open-weight models, including Mistral 7B, which can be run locally. 5. **T5 and T5.1.1**: - The Text-to-Text Transfer Transformer (T5) models from Google are versatile in handling various NLP tasks. - Available in different sizes, you might need considerable resources for larger variants. 6. **DistilBERT**: - A smaller, faster, and lighter version of BERT, which can be useful for many NLP tasks with fewer resources required compared to the original BERT model. 7. **RoBERTa**: - An optimized version of BERT, developed by Facebook AI, that can also be run locally and has demonstrated strong performance on various NLP tasks. 8. **Stable Diffusion**: - A model for generating images from text prompts, which is open-source and can run locally with the right hardware (ideally a decent GPU). **Requirements for Running Models Locally**: - **Hardware**: Most large models require powerful CPUs and/or GPUs. A GPU with a significant amount of VRAM (8GB or more recommended for larger models) is often necessary for efficient performance. - **Software**: You typically need Python, along with libraries like TensorFlow or PyTorch, and other dependencies specific to the model. - **Installation**: Models can often be found on repos like GitHub or model hubs like Hugging Face. Proper installation instructions or Docker images are usually provided. **Important Considerations**: - Depending on the model size and architecture, running some of these models may require substantial computational resources and technical expertise. - Licensing and terms of service should always be verified to ensure compliance when using or distributing AI models. Before starting any projects, it's a good idea to check the latest developments, community insights, and updates related to model accessibility and performance.