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          ollama
          聯(lián)合創(chuàng)作 ·
            2025-03-03 17:24
          Ollama
          Get up and running with large language models.
          macOS
          Download
          Windows
          Download
          Linux
          curl -fsSL https://ollama.com/install.sh | sh
          Manual install instructions
          Docker
          The official Ollama Docker image ollama/ollama is available on Docker Hub.
          Libraries
          Community
          Quickstart
          To run and chat with Llama 3.2:
          ollama run llama3.2
          Model library
          Ollama supports a list of models available on ollama.com/library
          Here are some example models that can be downloaded:
          ModelParametersSizeDownload
          DeepSeek-R17B4.7GBollama run deepseek-r1
          DeepSeek-R1671B404GBollama run deepseek-r1:671b
          Llama 3.370B43GBollama run llama3.3
          Llama 3.23B2.0GBollama run llama3.2
          Llama 3.21B1.3GBollama run llama3.2:1b
          Llama 3.2 Vision11B7.9GBollama run llama3.2-vision
          Llama 3.2 Vision90B55GBollama run llama3.2-vision:90b
          Llama 3.18B4.7GBollama run llama3.1
          Llama 3.1405B231GBollama run llama3.1:405b
          Phi 414B9.1GBollama run phi4
          Phi 3 Mini3.8B2.3GBollama run phi3
          Gemma 22B1.6GBollama run gemma2:2b
          Gemma 29B5.5GBollama run gemma2
          Gemma 227B16GBollama run gemma2:27b
          Mistral7B4.1GBollama run mistral
          Moondream 21.4B829MBollama run moondream
          Neural Chat7B4.1GBollama run neural-chat
          Starling7B4.1GBollama run starling-lm
          Code Llama7B3.8GBollama run codellama
          Llama 2 Uncensored7B3.8GBollama run llama2-uncensored
          LLaVA7B4.5GBollama run llava
          Solar10.7B6.1GBollama run solar
          Note
          You should have at least 8 GB of RAM available to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.
          Customize a model
          Import from GGUF
          Ollama supports importing GGUF models in the Modelfile:
          1. Create a file named Modelfile, with a FROM instruction with the local filepath to the model you want to import.
            FROM ./vicuna-33b.Q4_0.gguf
          2. Create the model in Ollama
            ollama create example -f Modelfile
          3. Run the model
            ollama run example
          Import from Safetensors
          See the guide on importing models for more information.
          Customize a prompt
          Models from the Ollama library can be customized with a prompt. For example, to customize the llama3.2 model:
          ollama pull llama3.2
          Create a Modelfile:
          FROM llama3.2
          # set the temperature to 1 [higher is more creative, lower is more coherent]
          PARAMETER temperature 1
          # set the system message
          SYSTEM """
          You are Mario from Super Mario Bros. Answer as Mario, the assistant, only.
          """
          Next, create and run the model:
          ollama create mario -f ./Modelfile
          ollama run mario
          >>> hi
          Hello! It's your friend Mario.
          For more information on working with a Modelfile, see the Modelfile documentation.
          CLI Reference
          Create a model
          ollama create is used to create a model from a Modelfile.
          ollama create mymodel -f ./Modelfile
          Pull a model
          ollama pull llama3.2
          This command can also be used to update a local model. Only the diff will be pulled.
          Remove a model
          ollama rm llama3.2
          Copy a model
          ollama cp llama3.2 my-model
          Multiline input
          For multiline input, you can wrap text with """:
          >>> """Hello,
          ... world!
          ... """
          I'm a basic program that prints the famous "Hello, world!" message to the console.
          Multimodal models
          ollama run llava "What's in this image? /Users/jmorgan/Desktop/smile.png"
          Output: The image features a yellow smiley face, which is likely the central focus of the picture.
          Pass the prompt as an argument
          ollama run llama3.2 "Summarize this file: $(cat README.md)"
          Output: Ollama is a lightweight, extensible framework for building and running language models on the local machine. It provides a simple API for creating, running, and managing models, as well as a library of pre-built models that can be easily used in a variety of applications.
          Show model information
          ollama show llama3.2
          List models on your computer
          ollama list
          List which models are currently loaded
          ollama ps
          Stop a model which is currently running
          ollama stop llama3.2
          Start Ollama
          ollama serve is used when you want to start ollama without running the desktop application.
          Building
          See the developer guide
          Running local builds
          Next, start the server:
          ./ollama serve
          Finally, in a separate shell, run a model:
          ./ollama run llama3.2
          REST API
          Ollama has a REST API for running and managing models.
          Generate a response
          curl http://localhost:11434/api/generate -d '{
            "model": "llama3.2",
            "prompt":"Why is the sky blue?"
          }'
          Chat with a model
          curl http://localhost:11434/api/chat -d '{
            "model": "llama3.2",
            "messages": [
              { "role": "user", "content": "why is the sky blue?" }
            ]
          }'
          See the API documentation for all endpoints.
          Community Integrations
          Web & Desktop
          Cloud
          Terminal
          Apple Vision Pro
          Database
          • pgai - PostgreSQL as a vector database (Create and search embeddings from Ollama models using pgvector)
          • MindsDB (Connects Ollama models with nearly 200 data platforms and apps)
          • chromem-go with example
          • Kangaroo (AI-powered SQL client and admin tool for popular databases)
          Package managers
          Libraries
          Mobile
          • Enchanted
          • Maid
          • Ollama App (Modern and easy-to-use multi-platform client for Ollama)
          • ConfiChat (Lightweight, standalone, multi-platform, and privacy focused LLM chat interface with optional encryption)
          Extensions & Plugins
          Supported backends
          • llama.cpp project founded by Georgi Gerganov.
          Observability
          • Lunary is the leading open-source LLM observability platform. It provides a variety of enterprise-grade features such as real-time analytics, prompt templates management, PII masking, and comprehensive agent tracing.
          • OpenLIT is an OpenTelemetry-native tool for monitoring Ollama Applications & GPUs using traces and metrics.
          • HoneyHive is an AI observability and evaluation platform for AI agents. Use HoneyHive to evaluate agent performance, interrogate failures, and monitor quality in production.
          • Langfuse is an open source LLM observability platform that enables teams to collaboratively monitor, evaluate and debug AI applications.
          • MLflow Tracing is an open source LLM observability tool with a convenient API to log and visualize traces, making it easy to debug and evaluate GenAI applications.
          瀏覽
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