base on Access large language models from the command-line # LLM [](https://pypi.org/project/llm/) [](https://llm.datasette.io/) [](https://llm.datasette.io/en/stable/changelog.html) [](https://github.com/simonw/llm/actions?query=workflow%3ATest) [](https://github.com/simonw/llm/blob/main/LICENSE) [](https://datasette.io/discord-llm) [](https://formulae.brew.sh/formula/llm) A CLI utility and Python library for interacting with Large Language Models, both via remote APIs and models that can be installed and run on your own machine. [Run prompts from the command-line](https://llm.datasette.io/en/stable/usage.html#executing-a-prompt), [store the results in SQLite](https://llm.datasette.io/en/stable/logging.html), [generate embeddings](https://llm.datasette.io/en/stable/embeddings/index.html) and more. Consult the **[LLM plugins directory](https://llm.datasette.io/en/stable/plugins/directory.html)** for plugins that provide access to remote and local models. Full documentation: **[llm.datasette.io](https://llm.datasette.io/)** Background on this project: - [llm, ttok and strip-tags—CLI tools for working with ChatGPT and other LLMs](https://simonwillison.net/2023/May/18/cli-tools-for-llms/) - [The LLM CLI tool now supports self-hosted language models via plugins](https://simonwillison.net/2023/Jul/12/llm/) - [Accessing Llama 2 from the command-line with the llm-replicate plugin](https://simonwillison.net/2023/Jul/18/accessing-llama-2/) - [Run Llama 2 on your own Mac using LLM and Homebrew](https://simonwillison.net/2023/Aug/1/llama-2-mac/) - [Catching up on the weird world of LLMs](https://simonwillison.net/2023/Aug/3/weird-world-of-llms/) - [LLM now provides tools for working with embeddings](https://simonwillison.net/2023/Sep/4/llm-embeddings/) - [Build an image search engine with llm-clip, chat with models with llm chat](https://simonwillison.net/2023/Sep/12/llm-clip-and-chat/) - [Many options for running Mistral models in your terminal using LLM](https://simonwillison.net/2023/Dec/18/mistral/) ## Installation Install this tool using `pip`: ```bash pip install llm ``` Or using [Homebrew](https://brew.sh/): ```bash brew install llm ``` [Detailed installation instructions](https://llm.datasette.io/en/stable/setup.html). ## Getting started If you have an [OpenAI API key](https://platform.openai.com/api-keys) you can get started using the OpenAI models right away. As an alternative to OpenAI, you can [install plugins](https://llm.datasette.io/en/stable/plugins/installing-plugins.html) to access models by other providers, including models that can be installed and run on your own device. Save your OpenAI API key like this: ```bash llm keys set openai ``` This will prompt you for your key like so: ``` Enter key: <paste here> ``` Now that you've saved a key you can run a prompt like this: ```bash llm "Five cute names for a pet penguin" ``` ``` 1. Waddles 2. Pebbles 3. Bubbles 4. Flappy 5. Chilly ``` Read the [usage instructions](https://llm.datasette.io/en/stable/usage.html) for more. ## Installing a model that runs on your own machine [LLM plugins](https://llm.datasette.io/en/stable/plugins/index.html) can add support for alternative models, including models that run on your own machine. To download and run Mistral 7B Instruct locally, you can install the [llm-gpt4all](https://github.com/simonw/llm-gpt4all) plugin: ```bash llm install llm-gpt4all ``` Then run this command to see which models it makes available: ```bash llm models ``` ``` gpt4all: all-MiniLM-L6-v2-f16 - SBert, 43.76MB download, needs 1GB RAM gpt4all: orca-mini-3b-gguf2-q4_0 - Mini Orca (Small), 1.84GB download, needs 4GB RAM gpt4all: mistral-7b-instruct-v0 - Mistral Instruct, 3.83GB download, needs 8GB RAM ... ``` Each model file will be downloaded once the first time you use it. Try Mistral out like this: ```bash llm -m mistral-7b-instruct-v0 'difference between a pelican and a walrus' ``` You can also start a chat session with the model using the `llm chat` command: ```bash llm chat -m mistral-7b-instruct-v0 ``` ``` Chatting with mistral-7b-instruct-v0 Type 'exit' or 'quit' to exit Type '!multi' to enter multiple lines, then '!end' to finish > ``` ## Using a system prompt You can use the `-s/--system` option to set a system prompt, providing instructions for processing other input to the tool. To describe how the code in a file works, try this: ```bash cat mycode.py | llm -s "Explain this code" ``` ## Help For help, run: llm --help You can also use: python -m llm --help ", Assign "at most 3 tags" to the expected json: {"id":"7327","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"