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Description:

grazier 0.1.1

Grazier: Easily call Large Language Models from a unified API
Grazier is a Python library for easily calling large language models from a unified API.
Supported Large Models
From OpenAI:

GPT-4 (Base, 32K) (Chat and Completion Engines)
GPT-3.5 (ChatGPT) (Chat and Completion Engines)
GPT-3 (Davinci (v2,v3), Ada, Babbage, Curie) (Completion Engine)

From Anthropic:

Claude 2 (Base) (Chat and Completion Engines)
Claude (Base, 100K) (Chat and Completion Engines)
Claude Instant (Base, 100K) (Chat and Completion Engines)

From Google/GCP:

PaLM (Chat and Completion Engines)

From Huggingface

GPT-2 (Base, Medium, Large, XL) (Completion Engine)
GPT-Neo (125M, 1.3B, 2.7B) (Completion Engine)
GPT-J (6B) (Completion Engine)
Falcon (7B, 40B, rw-1B, rw-7B) (Completion Engine)
Dolly (v1 - 6B, v2 - 3B, 7B, 12B) (Chat and Completion Engines)
MPT (Instruct - 7B, 30B) (Chat and Completion Engines)

From Facebook (via Huggingface)

Llama (7B, 13B, 30B, 65B) (Completion Engine)
Llama 2 (7B, 13B, 70B) (Completion Engine)
OPT (125M, 350M, 1.3B, 2.7B, 6.7B, 13B, 30B, 66B) (Completion Engine)

From Stanford (via Huggingface)

Alpaca (7B) (Chat and Completion Engines)

From Berkeley (via Huggingface)

Koala (7B, 13B_v1, 13B_v2) (Chat and Completion Engines)
Vicuna (7B, 13B) (Chat and Completion Engines)

From StabilityAI (via Huggingface)

StableLM (7B, 13B) (Chat and Completion Engines)

From AllenAI (via Huggingface)

Tulu (7B, 13B, 30B, 65B) (Chat and Completion Engines)
Open Instruct (ShareGPT) (7B, 13B, 30B, 65B) (Chat and Completion Engines)

From AI21

Jurassic 2 (Light, Mid, Ultra) (Completion Engines)

Image/Language Models:

Blip2 (2.7B, 6.7B) (Image + Text Engine)
Open Flamingo (3B, 4B, 9B) (Image + Text Engine)

Installation
Grazier can easily be installed using pip:
pip install grazier

Each of the LLMs may need additional setup, which you can find in the engine setup section below.
Usage
For completion engines, it's as simple as:
import grazier

grazier.list_models()
['gptj-6B', 'gpt2', 'gpt2-med', 'gpt2-lg', 'gpt2-xl', 'distilgpt2', 'gptneo-125M', 'gptneo-1.3B', 'gptneo-2.7B', 'stablelm-3B', 'stablelm-7B', 'opt-125M', 'opt-350M', 'opt-1.3b', 'opt-2.7b', 'opt-6.7b', 'opt-13b', 'opt-30b', 'opt-66b', 'llama-7B', 'llama-13B', 'llama-30B', 'llama-65B', 'gpt3-davinci3', 'gpt3-davinci2', 'gpt3-curie', 'gpt3-babbage', 'gpt3-ada', 'palm']
gpt2 = grazier.get("gpt2")
completion = gpt2("I enjoy walking with my cute dog, but sometimes he gets scared and")
print(completion)

For chat engines, all you need to do is add the type="chat" parameter:
from grazier import Conversation, Speaker, get, list_models

conversation = Conversation()
conversation.add_turn("You are a funny person.", speaker=Speaker.SYSTEM)
conversation.add_turn("Hi, how are you?", speaker=Speaker.USER)
conversation.add_turn("I am doing well, how about you?", speaker=Speaker.AI)
conversation.add_turn("What are you planning to do today?", speaker=Speaker.USER)

list_models(type="chat")
['claude', 'claude-100k', 'claude-instant', 'claude-instant-100k', 'bard', 'koala-7b', 'koala-13b-v1', 'koala-13b-v2', 'vicuna-7b', 'vicuna-13b', 'alpaca-13b', 'chat-gpt', 'gpt4', 'gpt4-32k', 'stablelm-3b', 'stablelm-7b', 'palm']
gpt4 = get("gpt4", type="chat")
next_turn = gpt4(conversation)
print(next_turn)

For vision-augmented (image) engines, use type="image"
import grazier
from PIL import Image

grazier.list_models(type="image")
['blip2-opt-2.7b', 'blip2-opt-6.7b', 'blip2-opt-2.7b-coco', 'blip2-opt-6.7b-coco', 'blip2-flan-t5-xl', 'blip2-flan-t5-xxl', 'blip2-flan-t5-xl-coco', 'openflamingo-3b-vitl-mpt1b', 'openflamingo-3b-vitl-mpt1b-dolly', 'openflamingo-9b-vitl-mpt7b', 'openflamingo-4b-vitl-rpj3b']
blip2 = grazier.get("blip2-opt-2.7b", type="image")

image = Image.open('test_data/dog.jpg')
completion = blip2(image, "A photo of")

print(completion)

Individual Engine Setup
Each engine may require some specific details to be passed in. For example, OpenAI engines require an API key. These
details are generally set up with environment variables.
OpenAI Engines
For OpenAI engines, you will need to set the OPENAI_API_KEY and OPENAI_API_ORG environment variables. You can find
your API key and organization ID on the OpenAI dashboard. You can set these environment
variables in your shell or in a .env file in the root of your project. For example, in a .env file, you would have:
OPENAI_API_KEY=<your key>
OPENAI_API_ORG=<your org id>

or on the command line:
export OPENAI_API_KEY=<your key>
export OPENAI_API_ORG=<your org id>

Anthropic Engines
For Anthropic engines, you will need to set the ANTHROPIC_API_KEY environment variable. You can find your API key at
the Anthropic dashboard. You can set this environment variable in
your shell or in a .env file in the root of your project. For example, in a .env file, you would have:
ANTHROPIC_API_KEY=<your key>

or on the command line:
export ANTHROPIC_API_KEY=<your key>

Vertex Engines (PaLM)
For Google engines, we use the Vertex cloud API, which requires a Google Cloud Platform (GCP) project. You can create a
GCP project at the GCP console. You will also need to enable the Vertex AI API for
your project, set up a services account, and download the account JSON credentials. You can find instructions for that
following steps 1 to 6 of the tutorial here.
Finally, you will need to set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path of the JSON file.
You can set this environment variable in your shell or in a .env file in the root of your project. For example, in a
.env file, you would have:
GOOGLE_APPLICATION_CREDENTIALS=<path to your JSON file>

or on the command line:
export GOOGLE_APPLICATION_CREDENTIALS=<path to your JSON file>

Bard
For the Bard engine, you will need to get your Bard __Secure-1PSID and __Secure-1PSIDTS tokens. Get the value of this
variable by first going to https://bard.google.com/, then log in, press F12 for console, and go to the "Application" tab,
then "Cookies", then copy the value of the "__Secure-1PSID" and "__Secure-1PSIDTS" cookies. You can then set the
environment variables:
BARD__Secure_1PSID=<your session id>
BARD__Secure_1PSIDTS=<your session id timestamp>

Huggingface Engines
Most of the huggingface engines require no additional setup, however, some of the larger models require a GPU to run
with any kind of efficiency (and some require multiple GPUs with large amounts of memory). You can find more details
about the requirements for each model on the Huggingface model hub.
Llama, Alpaca, Koala, Vicuna and AllenAI Engines
For these engines, you will need to obtain and postprocess the weights yourself (due to Facebook's licensing). You can
find the instructions for doing so on each model page:

Llama: https://huggingface.co/docs/transformers/main/model_doc/llama
Alpaca: https://github.com/tatsu-lab/stanford_alpaca#recovering-alpaca-weights
Koala: https://github.com/young-geng/EasyLM/blob/main/docs/koala.md
Vicuna: https://github.com/lm-sys/FastChat#vicuna-weights
AllenAI: https://huggingface.co/allenai/tulu-65b

Once the weights have been downloaded and processed, you can set the following environment variables to the root
directory containing a folder for each variant (The format is, {root_dir}/{model-prefix}/weights.bin, the root directory would
be root_dir, and the model-prefix is the name of the model, e.g. tulu-65b):
LLAMA_WEIGHTS_ROOT=<path to the llama weights>
ALPACA_WEIGHTS_ROOT=<path to the alpaca weights>
KOALA_WEIGHTS_ROOT=<path to the koala weights>
VICUNA_WEIGHTS_ROOT=<path to the vicuna weights>
ALLENAI_WEIGHTS_ROOT=<path to the allenai weights>

AI21 Models (Jurassic)
For AI21 models, you will need to set the AI21_API_KEY environment variable. You can find your API key at
the AI21 Studio Dashboard. You can set this environment variable in
your shell or in a .env file in the root of your project. For example, in a .env file, you would have:
AI21_API_KEY=<your key>

or on the command line:
export AI21_API_KEY=<your key>

Citation
If you use grazier in your work, please cite:
@misc{grazier,
author = {David Chan},
title = {grazier: Easily call Large Language Models from a unified API},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{
https://github.com/DavidMChan/grazier
}}
}

License
grazier is licensed under the terms of the MIT license. See LICENSE for more information.