Skip to main content

llm-groq

LLM plugin providing access to Groqcloud models.

Installation

Install this plugin in the same environment as LLM:

llm install llm-groq

Usage

First, obtain an API key for Groqcloud.

Configure the key using the llm keys set groq command:

llm keys set groq
<paste key here>

You can now access the three Mistral hosted models: groq-llama2 and groq-mixtral.

To run a prompt through groq-mixtral:

llm -m groq-mixtral 'A sassy name for a pet sasquatch'

To start an interactive chat session with groq-mixtral:

llm chat -m groq-mixtral
llm chat -m groq-mixtral
Chatting with groq-mixtral
Type 'exit' or 'quit' to exit
Type '!multi' to enter multiple lines, then '!end' to finish
> three proud names for a pet walrus
Here are three whimsical and proud-sounding names for a pet walrus:

1. Regalus Maximus
2. Glacierus Royalty
3. Arctican Aristocat

These names evoke a sense of majesty and grandeur, fitting for a noble and intelligent creature like a walrus. I hope you find these names fitting and amusing! If you have any other requests or need assistance with something else, please don't hesitate to ask.

To use a system prompt with groq-mixtral to explain some code:

cat example.py | llm -m groq-mixtral -s 'explain this code'

Model options

TBD

Development

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

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

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

llm_groq-0.9.tar.gz (9.6 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

llm_groq-0.9-py3-none-any.whl (10.1 kB view details)

Uploaded Python 3

File details

Details for the file llm_groq-0.9.tar.gz.

File metadata

  • Download URL: llm_groq-0.9.tar.gz
  • Upload date:
  • Size: 9.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.13

File hashes

Hashes for llm_groq-0.9.tar.gz
Algorithm Hash digest
SHA256 600fef79d749a431a9ad0fc3f986b68629dd30ccdc61ba0d1ce77b33aa749d07
MD5 9830f4ddb5ef6e8eb86069adbb3907ac
BLAKE2b-256 2eaffdff701304eb46b4341f88b8ada16abe41f16f4f754f072ccbd7eb5b66e8

See more details on using hashes here.

File details

Details for the file llm_groq-0.9-py3-none-any.whl.

File metadata

  • Download URL: llm_groq-0.9-py3-none-any.whl
  • Upload date:
  • Size: 10.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.13

File hashes

Hashes for llm_groq-0.9-py3-none-any.whl
Algorithm Hash digest
SHA256 aaec60d121f29bba5612f5c5f2fe26895e42bc7e27f95c27548eed0bf4ee0be5
MD5 980d58e42b8ed3eee81391725e234ec0
BLAKE2b-256 8ec1457ae46e77f8a2d42692b92d128378074d4630c113825c238fd38bb3225e

See more details on using hashes here.

Release history Release notifications | RSS feed

This release

0.9 This release

2 files

0.8

2 files

0.7

2 files

0.6

2 files

0.5

2 files

0.4

2 files

0.3

2 files

0.2

2 files

0.1

2 files

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page