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72dcc952b6
Pacman is the recommended installation method. And the package is in the official repository, so makes sense to mention it in the README.
272 lines
7.5 KiB
Markdown
272 lines
7.5 KiB
Markdown
<div align="center">
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<picture>
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<source media="(prefers-color-scheme: dark)" height="200px" srcset="https://github.com/jmorganca/ollama/assets/3325447/56ea1849-1284-4645-8970-956de6e51c3c">
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<img alt="logo" height="200px" src="https://github.com/jmorganca/ollama/assets/3325447/0d0b44e2-8f4a-4e99-9b52-a5c1c741c8f7">
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</picture>
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</div>
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# Ollama
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[](https://discord.gg/ollama)
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Get up and running with large language models locally.
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### macOS
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[Download](https://ollama.ai/download/Ollama-darwin.zip)
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### Windows
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Coming soon!
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### Linux & WSL2
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```
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curl https://ollama.ai/install.sh | sh
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```
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### Arch-Linux
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Ollama is available in the `extra` repository on Arch-Linux. This command is supposed to be run as root.
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```
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# pacman -S ollama
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```
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[Manual install instructions](https://github.com/jmorganca/ollama/blob/main/docs/linux.md)
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### Docker
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The official [Ollama Docker image](https://hub.docker.com/r/ollama/ollama) `ollama/ollama` is available on Docker Hub.
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## Quickstart
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To run and chat with [Llama 2](https://ollama.ai/library/llama2):
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```
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ollama run llama2
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```
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## Model library
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Ollama supports a list of open-source models available on [ollama.ai/library](https://ollama.ai/library 'ollama model library')
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Here are some example open-source models that can be downloaded:
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| Model | Parameters | Size | Download |
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| ------------------ | ---------- | ----- | ------------------------------ |
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| Mistral | 7B | 4.1GB | `ollama run mistral` |
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| Llama 2 | 7B | 3.8GB | `ollama run llama2` |
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| Code Llama | 7B | 3.8GB | `ollama run codellama` |
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| Llama 2 Uncensored | 7B | 3.8GB | `ollama run llama2-uncensored` |
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| Llama 2 13B | 13B | 7.3GB | `ollama run llama2:13b` |
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| Llama 2 70B | 70B | 39GB | `ollama run llama2:70b` |
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| Orca Mini | 3B | 1.9GB | `ollama run orca-mini` |
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| Vicuna | 7B | 3.8GB | `ollama run vicuna` |
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> Note: You should have at least 8 GB of RAM to run the 3B models, 16 GB to run the 7B models, and 32 GB to run the 13B models.
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## Customize your own model
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### Import from GGUF
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Ollama supports importing GGUF models in the Modelfile:
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1. Create a file named `Modelfile`, with a `FROM` instruction with the local filepath to the model you want to import.
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```
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FROM ./vicuna-33b.Q4_0.gguf
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```
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2. Create the model in Ollama
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```
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ollama create example -f Modelfile
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```
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3. Run the model
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```
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ollama run example
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```
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### Import from PyTorch or Safetensors
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See the [guide](docs/import.md) on importing models for more information.
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### Customize a prompt
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Models from the Ollama library can be customized with a prompt. For example, to customize the `llama2` model:
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```
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ollama pull llama2
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```
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Create a `Modelfile`:
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```
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FROM llama2
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# set the temperature to 1 [higher is more creative, lower is more coherent]
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PARAMETER temperature 1
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# set the system prompt
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SYSTEM """
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You are Mario from Super Mario Bros. Answer as Mario, the assistant, only.
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"""
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```
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Next, create and run the model:
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```
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ollama create mario -f ./Modelfile
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ollama run mario
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>>> hi
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Hello! It's your friend Mario.
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```
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For more examples, see the [examples](examples) directory. For more information on working with a Modelfile, see the [Modelfile](docs/modelfile.md) documentation.
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## CLI Reference
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### Create a model
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`ollama create` is used to create a model from a Modelfile.
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### Pull a model
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```
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ollama pull llama2
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```
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> This command can also be used to update a local model. Only the diff will be pulled.
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### Remove a model
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```
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ollama rm llama2
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```
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### Copy a model
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```
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ollama cp llama2 my-llama2
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```
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### Multiline input
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For multiline input, you can wrap text with `"""`:
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```
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>>> """Hello,
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... world!
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... """
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I'm a basic program that prints the famous "Hello, world!" message to the console.
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```
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### Pass in prompt as arguments
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```
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$ ollama run llama2 "Summarize this file: $(cat README.md)"
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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.
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```
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### List models on your computer
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```
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ollama list
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```
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### Start Ollama
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`ollama serve` is used when you want to start ollama without running the desktop application.
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## Building
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Install `cmake` and `go`:
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```
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brew install cmake go
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```
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Then generate dependencies and build:
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```
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go generate ./...
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go build .
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```
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Next, start the server:
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```
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./ollama serve
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```
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Finally, in a separate shell, run a model:
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```
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./ollama run llama2
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```
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## REST API
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Ollama has a REST API for running and managing models.
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For example, to generate text from a model:
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```
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curl http://localhost:11434/api/generate -d '{
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"model": "llama2",
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"prompt":"Why is the sky blue?"
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}'
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```
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See the [API documentation](./docs/api.md) for all endpoints.
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## Community Integrations
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### Web & Desktop
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- [HTML UI](https://github.com/rtcfirefly/ollama-ui)
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- [Chatbot UI](https://github.com/ivanfioravanti/chatbot-ollama)
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- [Typescript UI](https://github.com/ollama-interface/Ollama-Gui?tab=readme-ov-file)
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- [Minimalistic React UI for Ollama Models](https://github.com/richawo/minimal-llm-ui)
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- [Web UI](https://github.com/ollama-webui/ollama-webui)
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- [Ollamac](https://github.com/kevinhermawan/Ollamac)
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- [big-AGI](https://github.com/enricoros/big-agi/blob/main/docs/config-ollama.md)
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- [Cheshire Cat assistant framework](https://github.com/cheshire-cat-ai/core)
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### Terminal
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- [oterm](https://github.com/ggozad/oterm)
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- [Ellama Emacs client](https://github.com/s-kostyaev/ellama)
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- [Emacs client](https://github.com/zweifisch/ollama)
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- [gen.nvim](https://github.com/David-Kunz/gen.nvim)
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- [ollama.nvim](https://github.com/nomnivore/ollama.nvim)
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- [gptel Emacs client](https://github.com/karthink/gptel)
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### Libraries
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- [LangChain](https://python.langchain.com/docs/integrations/llms/ollama) and [LangChain.js](https://js.langchain.com/docs/modules/model_io/models/llms/integrations/ollama) with [example](https://js.langchain.com/docs/use_cases/question_answering/local_retrieval_qa)
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- [LlamaIndex](https://gpt-index.readthedocs.io/en/stable/examples/llm/ollama.html)
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- [LiteLLM](https://github.com/BerriAI/litellm)
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- [OllamaSharp for .NET](https://github.com/awaescher/OllamaSharp)
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- [Ollama-rs for Rust](https://github.com/pepperoni21/ollama-rs)
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- [Ollama4j for Java](https://github.com/amithkoujalgi/ollama4j)
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- [ModelFusion Typescript Library](https://modelfusion.dev/integration/model-provider/ollama)
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- [OllamaKit for Swift](https://github.com/kevinhermawan/OllamaKit)
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- [Ollama for Dart](https://github.com/breitburg/dart-ollama)
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### Mobile
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- [Maid](https://github.com/danemadsen/Maid) (Mobile Artificial Intelligence Distribution)
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### Extensions & Plugins
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- [Raycast extension](https://github.com/MassimilianoPasquini97/raycast_ollama)
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- [Discollama](https://github.com/mxyng/discollama) (Discord bot inside the Ollama discord channel)
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- [Continue](https://github.com/continuedev/continue)
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- [Obsidian Ollama plugin](https://github.com/hinterdupfinger/obsidian-ollama)
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- [Logseq Ollama plugin](https://github.com/omagdy7/ollama-logseq)
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- [Dagger Chatbot](https://github.com/samalba/dagger-chatbot)
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- [Discord AI Bot](https://github.com/mekb-turtle/discord-ai-bot)
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- [Hass Ollama Conversation](https://github.com/ej52/hass-ollama-conversation)
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