usage.md

(usage)=

Usage

The command to run a prompt is llm prompt 'your prompt'. This is the default command, so you can use llm 'your prompt' as a shortcut.

(usage-executing-prompts)=

Executing a prompt

These examples use the default OpenAI gpt-3.5-turbo model, which requires you to first {ref}set an OpenAI API key <api-keys>.

You can {ref}install LLM plugins <installing-plugins> to use models from other providers, including openly licensed models you can run directly on your own computer.

To run a prompt, streaming tokens as they come in:

llm 'Ten names for cheesecakes'

To disable streaming and only return the response once it has completed:

llm 'Ten names for cheesecakes' --no-stream

To switch from ChatGPT 3.5 (the default) to GPT-4o:

llm 'Ten names for cheesecakes' -m gpt-4o

You can use -m 4o as an even shorter shortcut.

Pass --model <model name> to use a different model. Run llm models to see a list of available models.

You can also send a prompt to standard input, for example:

echo 'Ten names for cheesecakes' | llm

If you send text to standard input and provide arguments, the resulting prompt will consist of the piped content followed by the arguments:

cat myscript.py | llm 'explain this code'

Will run a prompt of:

<contents of myscript.py> explain this code

For models that support them, {ref}system prompts <system-prompts> are a better tool for this kind of prompting.

Some models support options. You can pass these using -o/--option name value - for example, to set the temperature to 1.5 run this:

llm 'Ten names for cheesecakes' -o temperature 1.5

(usage-attachments)=

Attachments

Some models are multi-modal, which means they can accept input in more than just text. GPT-4o and GPT-4o mini can accept images, and models such as Google Gemini 1.5 can accept audio and video as well.

LLM calls these attachments. You can pass attachments using the -a option like this:

llm "describe this image" -a https://static.simonwillison.net/static/2024/pelicans.jpg

Attachments can be passed using URLs or file paths, and you can attach more than one attachment to a single prompt:

llm "extract text" -a image1.jpg -a image2.jpg

You can also pipe an attachment to LLM by using - as the filename:

cat image.jpg | llm "describe this image" -a -

LLM will attempt to automatically detect the content type of the image. If this doesn't work you can instead use the --attachment-type option (--at for short) which takes the URL/path plus an explicit content type:

cat myfile | llm "describe this image" --at - image/jpeg

(system-prompts)=

System prompts

You can use -s/--system '...' to set a system prompt.

llm 'SQL to calculate total sales by month' \
  --system 'You are an exaggerated sentient cheesecake that knows SQL and talks about cheesecake a lot'

This is useful for piping content to standard input, for example:

curl -s 'https://simonwillison.net/2023/May/15/per-interpreter-gils/' | \
  llm -s 'Suggest topics for this post as a JSON array'

Or to generate a description of changes made to a Git repository since the last commit:

git diff | llm -s 'Describe these changes'

Different models support system prompts in different ways.

The OpenAI models are particularly good at using system prompts as instructions for how they should process additional input sent as part of the regular prompt.

Other models might use system prompts change the default voice and attitude of the model.

System prompts can be saved as {ref}templates <prompt-templates> to create reusable tools. For example, you can create a template called pytest like this:

llm -s 'write pytest tests for this code' --save pytest

And then use the new template like this:

cat llm/utils.py | llm -t pytest

See {ref}prompt templates <prompt-templates> for more.

(conversation)=

Continuing a conversation

By default, the tool will start a new conversation each time you run it.

You can opt to continue the previous conversation by passing the -c/--continue option:

llm 'More names' -c

This will re-send the prompts and responses for the previous conversation as part of the call to the language model. Note that this can add up quickly in terms of tokens, especially if you are using expensive models.

--continue will automatically use the same model as the conversation that you are continuing, even if you omit the -m/--model option.

To continue a conversation that is not the most recent one, use the --cid/--conversation <id> option:

llm 'More names' --cid 01h53zma5txeby33t1kbe3xk8q

You can find these conversation IDs using the llm logs command.

Tips for using LLM with Bash or Zsh

To learn more about your computer's operating system based on the output of uname -a, run this:

llm "Tell me about my operating system: $(uname -a)"

This pattern of using $(command) inside a double quoted string is a useful way to quickly assemble prompts.

(usage-completion-prompts)=

Completion prompts

Some models are completion models - rather than being tuned to respond to chat style prompts, they are designed to complete a sentence or paragraph.

An example of this is the gpt-3.5-turbo-instruct OpenAI model.

You can prompt that model the same way as the chat models, but be aware that the prompt format that works best is likely to differ.

llm -m gpt-3.5-turbo-instruct 'Reasons to tame a wild beaver:'

(usage-chat)=

Starting an interactive chat

The llm chat command starts an ongoing interactive chat with a model.

This is particularly useful for models that run on your own machine, since it saves them from having to be loaded into memory each time a new prompt is added to a conversation.

Run llm chat, optionally with a -m model_id, to start a chat conversation:

llm chat -m chatgpt

Each chat starts a new conversation. A record of each conversation can be accessed through {ref}the logs <logs-conversation>.

You can pass -c to start a conversation as a continuation of your most recent prompt. This will automatically use the most recently used model:

llm chat -c

For models that support them, you can pass options using -o/--option:

llm chat -m gpt-4 -o temperature 0.5

You can pass a system prompt to be used for your chat conversation:

llm chat -m gpt-4 -s 'You are a sentient cheesecake'

You can also pass {ref}a template <prompt-templates> - useful for creating chat personas that you wish to return to.

Here's how to create a template for your GPT-4 powered cheesecake:

llm --system 'You are a sentient cheesecake' -m gpt-4 --save cheesecake

Now you can start a new chat with your cheesecake any time you like using this:

llm chat -t cheesecake

Chatting with gpt-4
Type 'exit' or 'quit' to exit
Type '!multi' to enter multiple lines, then '!end' to finish
> who are you?
I am a sentient cheesecake, meaning I am an artificial
intelligence embodied in a dessert form, specifically a
cheesecake. However, I don't consume or prepare foods
like humans do, I communicate, learn and help answer
your queries.

Type quit or exit followed by <enter> to end a chat session.

Sometimes you may want to paste multiple lines of text into a chat at once - for example when debugging an error message.

To do that, type !multi to start a multi-line input. Type or paste your text, then type !end and hit <enter> to finish.

If your pasted text might itself contain a !end line, you can set a custom delimiter using !multi abc followed by !end abc at the end:

Chatting with gpt-4
Type 'exit' or 'quit' to exit
Type '!multi' to enter multiple lines, then '!end' to finish
> !multi custom-end
 Explain this error:

   File "/opt/homebrew/Caskroom/miniconda/base/lib/python3.10/urllib/request.py", line 1391, in https_open
    return self.do_open(http.client.HTTPSConnection, req,
  File "/opt/homebrew/Caskroom/miniconda/base/lib/python3.10/urllib/request.py", line 1351, in do_open
    raise URLError(err)
urllib.error.URLError: <urlopen error [Errno 8] nodename nor servname provided, or not known>

 !end custom-end

Listing available models

The llm models command lists every model that can be used with LLM, along with their aliases. This includes models that have been installed using {ref}plugins <plugins>.

llm models

Example output:

OpenAI Chat: gpt-3.5-turbo (aliases: 3.5, chatgpt)
OpenAI Chat: gpt-3.5-turbo-16k (aliases: chatgpt-16k, 3.5-16k)
OpenAI Chat: gpt-4 (aliases: 4, gpt4)
OpenAI Chat: gpt-4-32k (aliases: 4-32k)
PaLM 2: chat-bison-001 (aliases: palm, palm2)

Add --options to also see documentation for the options supported by each model:

llm models --options

Output:

OpenAI Chat: gpt-4o (aliases: 4o)
  Options:
    temperature: float
      What sampling temperature to use, between 0 and 2. Higher values like
      0.8 will make the output more random, while lower values like 0.2 will
      make it more focused and deterministic.
    max_tokens: int
      Maximum number of tokens to generate.
    top_p: float
      An alternative to sampling with temperature, called nucleus sampling,
      where the model considers the results of the tokens with top_p
      probability mass. So 0.1 means only the tokens comprising the top 10%
      probability mass are considered. Recommended to use top_p or
      temperature but not both.
    frequency_penalty: float
      Number between -2.0 and 2.0. Positive values penalize new tokens based
      on their existing frequency in the text so far, decreasing the model's
      likelihood to repeat the same line verbatim.
    presence_penalty: float
      Number between -2.0 and 2.0. Positive values penalize new tokens based
      on whether they appear in the text so far, increasing the model's
      likelihood to talk about new topics.
    stop: str
      A string where the API will stop generating further tokens.
    logit_bias: dict, str
      Modify the likelihood of specified tokens appearing in the completion.
      Pass a JSON string like '{"1712":-100, "892":-100, "1489":-100}'
    seed: int
      Integer seed to attempt to sample deterministically
    json_object: boolean
      Output a valid JSON object {...}. Prompt must mention JSON.
  Attachment types:
    image/png, image/gif, image/webp, image/jpeg
OpenAI Chat: gpt-4o-mini (aliases: 4o-mini)
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
  Attachment types:
    image/png, image/gif, image/webp, image/jpeg
OpenAI Chat: gpt-4o-audio-preview
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
  Attachment types:
    audio/mpeg, audio/wave
OpenAI Chat: gpt-3.5-turbo (aliases: 3.5, chatgpt)
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
OpenAI Chat: gpt-3.5-turbo-16k (aliases: chatgpt-16k, 3.5-16k)
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
OpenAI Chat: gpt-4 (aliases: 4, gpt4)
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
OpenAI Chat: gpt-4-32k (aliases: 4-32k)
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
OpenAI Chat: gpt-4-1106-preview
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
OpenAI Chat: gpt-4-0125-preview
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
OpenAI Chat: gpt-4-turbo-2024-04-09
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
OpenAI Chat: gpt-4-turbo (aliases: gpt-4-turbo-preview, 4-turbo, 4t)
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
OpenAI Chat: o1-preview
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
OpenAI Chat: o1-mini
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
OpenAI Completion: gpt-3.5-turbo-instruct (aliases: 3.5-instruct, chatgpt-instruct)
  Options:
    temperature: float
      What sampling temperature to use, between 0 and 2. Higher values like
      0.8 will make the output more random, while lower values like 0.2 will
      make it more focused and deterministic.
    max_tokens: int
      Maximum number of tokens to generate.
    top_p: float
      An alternative to sampling with temperature, called nucleus sampling,
      where the model considers the results of the tokens with top_p
      probability mass. So 0.1 means only the tokens comprising the top 10%
      probability mass are considered. Recommended to use top_p or
      temperature but not both.
    frequency_penalty: float
      Number between -2.0 and 2.0. Positive values penalize new tokens based
      on their existing frequency in the text so far, decreasing the model's
      likelihood to repeat the same line verbatim.
    presence_penalty: float
      Number between -2.0 and 2.0. Positive values penalize new tokens based
      on whether they appear in the text so far, increasing the model's
      likelihood to talk about new topics.
    stop: str
      A string where the API will stop generating further tokens.
    logit_bias: dict, str
      Modify the likelihood of specified tokens appearing in the completion.
      Pass a JSON string like '{"1712":-100, "892":-100, "1489":-100}'
    seed: int
      Integer seed to attempt to sample deterministically
    logprobs: int
      Include the log probabilities of most likely N per token

When running a prompt you can pass the full model name or any of the aliases to the -m/--model option:

llm -m 4o \
  'As many names for cheesecakes as you can think of, with detailed descriptions'