semvec/pypi-packages huggingface dataset:
llm-mpt30b 0.1
Plugin for LLM adding support for the MPT-30B language model
License: Apache License, Version 2.0
Requires Python: >=3.9

# llm-mpt30b

[![PyPI](https://img.shields.io/pypi/v/llm-mpt30b.svg)](https://pypi.org/project/llm-mpt30b/)
[![Changelog](https://img.shields.io/github/v/release/simonw/llm-mpt30b?include_prereleases&label=changelog)](https://github.com/simonw/llm-mpt30b/releases)
[![Tests](https://github.com/simonw/llm-mpt30b/workflows/Test/badge.svg)](https://github.com/simonw/llm-mpt30b/actions?query=workflow%3ATest)
[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/llm-mpt30b/blob/main/LICENSE)

Plugin for [LLM](https://llm.datasette.io/) adding support for the [MPT-30B language model](https://huggingface.co/mosaicml/mpt-30b).

This plugin uses [TheBloke/mpt-30B-GGML](https://huggingface.co/TheBloke/mpt-30B-GGML). The code was inspired by [abacaj/mpt-30B-inference](https://github.com/abacaj/mpt-30B-inference).

## Installation

Install this plugin in the same environment as LLM.
```bash
llm install llm-mpt30b
```
After installing the plugin you will need to download the ~19GB model file. You can do this by running:

```bash
llm mpt30b download
```

## Usage

This plugin adds a model called `mpt30b`. You can execute it like this:

```bash
llm -m mpt30b "Three great names for a pet goat"
```
The alias `-m mpt` works as well.

You can pass the option `-o verbose 1` to see more verbose output - currently a progress bar showing any additional downloads that are made during execution.

## Development

To set up this plugin locally, first checkout the code. Then create a new virtual environment:

    cd llm-mpt30b
    python3 -m venv venv
    source venv/bin/activate

Now install the dependencies and test dependencies:

    pip install -e '.[test]'

To run the tests:

    pytest
llm-markov 0.1
Plugin for LLM adding a Markov chain generating model
License: Apache License, Version 2.0
Requires Python: >=3.7

# llm-markov

[![PyPI](https://img.shields.io/pypi/v/llm-markov.svg)](https://pypi.org/project/llm-markov/)
[![Changelog](https://img.shields.io/github/v/release/simonw/llm-markov?include_prereleases&label=changelog)](https://github.com/simonw/llm-markov/releases)
[![Tests](https://github.com/simonw/llm-markov/workflows/Test/badge.svg)](https://github.com/simonw/llm-markov/actions?query=workflow%3ATest)
[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/llm-markov/blob/main/LICENSE)

Plugin for [LLM](https://llm.datasette.io/) adding a [Markov chain](https://en.wikipedia.org/wiki/Markov_chain) generating model

## Installation

Install this plugin in the same environment as LLM.
```bash
llm install llm-markov
```
## Usage

This plugin adds a model called `markov`. You can execute it like this:

```bash
llm -m markov "The quick brown fox jumps over the lazy dog"
```

My default it will produce 100 words. You can control the number of words with the `-o number` option:

```bash
llm -m markov -o 20 "The quick brown fox jumps over the lazy dog"
```
A delay of 0.02s is simulated between each token. You can modify this using the `-o delay` option - to `0` to disable it, or some other floating point number of seconds to customize it:

```bash
llm -m markov "The quick brown fox jumps over the lazy dog" -o delay 0
llm -m markov "The quick brown fox jumps over the lazy dog" -o delay 0.1 -o length 20
```

## Development

To set up this plugin locally, first checkout the code. Then create a new virtual environment:

    cd llm-markov
    python3 -m venv venv
    source venv/bin/activate

Now install the dependencies and test dependencies:

    pip install -e '.[test]'

To run the tests:

    pytest
llm-palm 0.1
Plugin for LLM adding support for Google's PaLM 2 model
License: Apache License, Version 2.0
Requires Python: >=3.9

# llm-palm

[![PyPI](https://img.shields.io/pypi/v/llm-palm.svg)](https://pypi.org/project/llm-palm/)
[![Changelog](https://img.shields.io/github/v/release/simonw/llm-palm?include_prereleases&label=changelog)](https://github.com/simonw/llm-palm/releases)
[![Tests](https://github.com/simonw/llm-palm/workflows/Test/badge.svg)](https://github.com/simonw/llm-palm/actions?query=workflow%3ATest)
[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/llm-palm/blob/main/LICENSE)

Plugin for [LLM](https://llm.datasette.io/) adding support for Google's PaLM 2 model.

## Installation

Install this plugin in the same environment as LLM.
```bash
llm install llm-palm
```
## Configuration

You will need an API key from Google. Instructions for obtaining one: https://developers.generativeai.google/tutorials/setup

You can set that as an environment variable called `PALM_API_KEY`, or add it to the `llm` set of saved keys using:

```bash
llm keys set palm
```
```
Enter key: <paste key here>
```

## Usage

This plugin adds a model called `palm`. You can execute it like this:

```bash
llm -m palm "Ten great names for a pet pelican"
```
PaLM also supports system prompts:
```bash
echo "I like pelicans a lot" | llm -m palm --system "Translate to french"
```

## Development

To set up this plugin locally, first checkout the code. Then create a new virtual environment:

    cd llm-palm
    python3 -m venv venv
    source venv/bin/activate

Now install the dependencies and test dependencies:

    pip install -e '.[test]'

To run the tests:

    pytest