llm/docs/usage.md

Usage

The default command for this is llm prompt - you can use llm instead if you prefer.

Executing a prompt

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-4 if you have access:

llm 'Ten names for cheesecakes' -m gpt4

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

Pass --model <model name> to use a different model.

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

echo 'Ten names for cheesecakes' | llm

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

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' --continue

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

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

llm 'More names' --chat 2

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

Note that this feature only works if you have been logging your previous conversations to a database, having run the llm init-db command described below.

Using with a shell

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

llm "Describe these changes: $(git diff)"

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

Listing available models

The llm models list command lists every model that can be used with LLM, along with any aliases:

llm models list

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 list --options

Output:

OpenAI Chat: gpt-3.5-turbo (aliases: 3.5, chatgpt)
  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}'
OpenAI Chat: gpt-3.5-turbo-16k (aliases: chatgpt-16k, 3.5-16k)
  temperature: float
  max_tokens: int
  top_p: float
  frequency_penalty: float
  presence_penalty: float
  stop: str
  logit_bias: dict, str
OpenAI Chat: gpt-4 (aliases: 4, gpt4)
  temperature: float
  max_tokens: int
  top_p: float
  frequency_penalty: float
  presence_penalty: float
  stop: str
  logit_bias: dict, str
OpenAI Chat: gpt-4-32k (aliases: 4-32k)
  temperature: float
  max_tokens: int
  top_p: float
  frequency_penalty: float
  presence_penalty: float
  stop: str
  logit_bias: dict, str

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

llm -m chatgpt-16k 'As many names for cheesecakes as you can think of, with detailed descriptions'

Models that have been installed using plugins will be shown here as well.

Setting a custom default model

The model used when calling llm without the -m/--model option defaults to gpt-3.5-turbo - the fastest and least expensive OpenAI model, and the same model family that powers ChatGPT.

You can use the llm models default command to set a different default model. For GPT-4 (slower and more expensive, but more capable) run this:

llm models default gpt-4

You can view the current model by running this:

llm models default

Any of the supported aliases for a model can be passed to this command.