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  • Ollama
    Get up and running with large language models
  • Ollamas new engine for multimodal models · Ollama Blog
    As more multimodal models are released by major research labs, the task of supporting these models the way Ollama intends became more and more challenging We set out to support a new engine that makes multimodal models first-class citizens, and getting Ollama’s partners to contribute more directly to the community via the GGML tensor library
  • Download Ollama on Windows
    Download Ollama for Windows While Ollama downloads, sign up to get notified of new updates
  • qwen3 - ollama. com
    Download Ollama Qwen 3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models The flagship model, Qwen3-235B-A22B , achieves competitive results in benchmark evaluations of coding, math, general capabilities, etc , when compared to other top-tier models such
  • Ollama
    Get up and running with large language models Run Llama 3, Phi 3, Mistral, Gemma 2, and other models Customize and create your own
  • deepseek-r1 - ollama. com
    Note: to update the model from an older version, run ollama pull deepseek-r1 Distilled models DeepSeek team has demonstrated that the reasoning patterns of larger models can be distilled into smaller models, resulting in better performance compared to the reasoning patterns discovered through RL on small models
  • llama3. 3
    New state-of-the-art 70B model from Meta that offers similar performance compared to Llama 3 1 405B model The Meta Llama 3 3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in text out)
  • Ollama is now available as an official Docker image
    We are excited to share that Ollama is now available as an official Docker sponsored open-source image, making it simpler to get up and running with large language models using Docker containers With Ollama, all your interactions with large language models happen locally without sending private data to third-party services


















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