llm-meta-ai 0.1

LLM plugin for the Meta AI API

llm-meta-ai

LLM plugin for models hosted by the Meta AI API

Installation

Install this plugin in the same environment as LLM:

llm install llm-meta-ai

Usage

First, obtain an API key for the Meta AI API and set it as a key called meta-ai:

llm keys set meta-ai
# Paste key here

You can also set the key using the META_AI_TOKEN environment variable.

Run llm models to get a full list of available models. Models are prefixed with meta-ai/, for example:

llm -m meta-ai/muse-spark-1.1 "What is the capital of France?"

You can also list just the Meta AI models like this:

llm meta-ai models

Add --json for the full JSON model definitions.

The list of models is fetched from the API and cached for an hour. Run this command to refresh it:

llm meta-ai refresh

Reasoning

These are reasoning models: they think before they answer, and the reasoning tokens count towards your output token budget - they are reported in the completion_tokens_details.reasoning_tokens key in llm logs --json. Control how much the model reasons with the reasoning_effort option, one of none, minimal, low, medium, high or xhigh (not every model supports every value):

llm -m meta-ai/muse-spark-1.1 'What is the capital of France?' -o reasoning_effort low

Rate limits and max_tokens

Requests count against an output token rate limit before they run. Setting a token limit on your prompts helps avoid exhausting your quota - leave generous room for reasoning tokens:

llm -m meta-ai/muse-spark-1.1 'A short poem about a pelican' -o max_tokens 2000

Rate limits are applied per model - a model that returns 429 errors even after a long wait may not be enabled for your team, even if it shows up in the models list.

Attachments

Models accept images (PNG, JPEG, WebP, GIF, ICO) and PDFs:

llm -m meta-ai/muse-spark-1.1 'Describe this image' \
  -a https://static.simonwillison.net/static/2024/pelicans.jpg

The API also supports MP4 video via its Files API, which this plugin does not yet use.

Tools

Meta AI models support tools:

llm -m meta-ai/muse-spark-1.1 -T llm_time 'What time is it?' --td

Schemas

They support schemas as well:

llm -m meta-ai/muse-spark-1.1 'Invent a dog' --schema 'name, age int, breed'

Development

To contribute to this library, first checkout the code. Then create a new virtual environment:

cd llm-meta-ai
python -m venv venv
source venv/bin/activate

Now install the dependencies and test dependencies:

python -m pip install -e . --group dev

To run the tests:

python -m pytest