Ollama: A Command-Line Tool for Running Large Language Models Locally
Ollama is a command-line tool designed to run large language models locally on your computer. It supports downloading and running models such as Llama 2, Code Llama, and others, as well as allowing users to customize and create their own models. This free and open-source project is currently supported on macOS and Linux operating systems, with support for Windows expected in the future.
Ollama offers an official Docker image, which makes it easy to deploy large language models using Docker containers, ensuring all interactions with the models are done locally without sending private data to third-party services. Ollama also supports GPU acceleration on macOS and Linux, and provides a simple command-line interface (CLI) and REST API for interacting with the applications.
This tool is particularly useful for developers or researchers who need to run and experiment with large language models locally, without relying on external cloud services.
The Ollama library currently supports over 40 models, with more being added and updated regularly. Some example models that are supported and can be downloaded and run using Ollama include:
– Neural Chat: A chatbot training on a dataset of movie reviews
– Starling: A tool for tokenizing and tagging text
– Mistral: A tool for identifying and extracting named entities in text
– Llama 2: A language modeling tool with support for a wide range of natural language processing tasks
– Code Llama: A model that can be used to generate code from text descriptions
– Llama 2 Uncensored: A version of Llama 2 with an uncensored dataset
– Llama 2:13B: A large language model with a 13 billion word training corpus
– Orca Mini: A model for simulating conversation
– Vicuna: A model for generating text from a given prompt
In conclusion, Ollama is a powerful and flexible tool for running large language models locally on your computer, without the need for sending private data to third-party services. Its support for GPU acceleration, simple CLI, and REST API make it an ideal choice for developers and researchers who need to experiment with large language models on their local machines.
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