Key Features of Ollama
Easy to Use & User-Friendly Interface: Quickly download and use open-source LLMs with a straightforward setup process.
Versatile: Supports a wide variety of models, including those for text, images, audio, and multimodal tasks.
Cross-Platform Compatibility: Available on macOS, Windows, and Linux.
Offline Models: Operate large language models without needing a continuous internet connection.
High Performance: Built over llama.cpp, which offers state-of-the-art performance on a wide variety of hardware, both locally and in the cloud. It efficiently utilizes the available resources, such as your GPU or MPS for Apple.
Cost-Effective: Save on compute costs by executing models locally.
Privacy: Local processing ensures your data remains secure and private.
Limitations
While Ollama offers numerous benefits, it’s important to be aware of its limitations:
Inference Only: Ollama is designed solely for model inference. For training or fine-tuning models, you will need to use tools like Hugging Face, TensorFlow, or PyTorch.
Setup and Advanced Functionalities: For detailed configuration for model inference or training, other libraries such as Hugging Face and PyTorch are necessary.
Performance: Although Ollama is based on llama.cpp, it may still be slower than using it directly.
references:
OpenAI
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