Gai/Gen is the Universal Multi-Modal Wrapper Library for LLM. The library is designed to provide a simplified and unified interface for seamless switching between multi-modal open source language models on a local machine and OpenAI APIs.
Project description
Gai/Gen: Universal LLM Wrapper
This is a Universal Multi-Modal Wrapper Library for LLM inferencing.
The library provides a simplified and unified interface for seamless switching between multi-modal open source language models on a local machine and OpenAI APIs.
This is intended for Developers who are targetting the use of multi-modal LLMs for both OpenAI API and local machine models.
As the main focus is on 7 billion parameters and below open source models, running on commodity hardware, the library is designed to be a singleton wrapper. Only one model is loaded and cached into memory at any one time.
The wrappers are organised under the gen
folder according to 4 categories:
- ttt: Text-to-Text
- tts: Text-to-Speech
- stt: Speech-to-Text
- itt: Image-to-Text
Setting Up
It is highly recommended that you install the following variants in separate virtual environments.
# Install library for text-to-text generation
pip install gai-gen[TTT]
# Install library for text-to-speech generation
pip install gai-gen[TTS]
# Install library for speech-to-text generation
pip install gai-gen[STT]
# Install library for image-to-text generation
pip install gai-gen[ITT]
or
git clone https://www.github.com/kakkoii1337/gai-gen
Configuration
-
Create the default application directory
~/gai
and the default models directory~/gai/models
. If you want to change this defaults, change~/.gairc
-
Copy
gai.json
from this repository into~/gai
. This file contains the configurations for models and their respective loaders.
API Key
-
All API keys should be stored in a
.env
file in the root directory of the project. For example,OPENAI_API_KEY=<--replace-with-your-api-key--> ANTHROPIC_API_KEY=<--replace-with-your-api-key-->
Requirements
- The instructions are tested mainly on:
- Windows 11 with WSL
- Ubuntu 20.04.2 LTS
- NVIDIA RTX 2060 GPU with 8GB VRAM
- CUDA Toolkit is required for the GPU accelerated models. Run
nvidia-smi
to check if CUDA is installed. If you need help, refer to this https://gist.github.com/kakkoii1337/8a8d4d0bc71fa9c099a683d1601f219e
Credits
This library is made possible by the generosity and hardwork of the following open source projects. You are highly encouraged to check out the original source and documentations.
TTT
- TheBloke for all the quantized models in the demo
- turboderp for ExLlama
- Meta Team for the LLaMa2 Model
- HuggingFace team for the Transformers library and open source models
- [Mistral AI Team for Mistral7B Model
- Georgi Gerganov for LLaMaCpp
ITT
- Liu HaoTian for the LLaVa Model and Library
TTS
- Coqui-AI for the xTTS Model
STT
- OpenAI for Open Sourcing Whisper v3
Quick Start
1. Setup OpenAI API Key.
Save your OpenAI API key in a .env file in the root directory of your project.
OPENAI_API_KEY=<--replace-with-your-api-key-->
2. Run GPT4.
Run Text-to-Text generation using OpenAI by loading gpt-4
wrapper.
from gai.gen import Gaigen
gen = Gaigen.GetInstance().load('gpt-4')
response = gen.create(messages=[{'role':'USER','content':'Tell me a one paragraph short story.'},{'role':'ASSISTANT','content':''}])
print(response)
3. Install Mistral7B.
Open ~/gai/gai.json
and refer to model_path
:
- under
mistral-8k
, refer tomodel-path
for the repo name. - Go to huggingface
- Download the repo
Mistral-7B-Instruct-v0.1-GPTQ
into the~/gai/models
folder.
4. Run Mistral7B.
Run Text-to-Text generation using Mistral7B by replacing gpt-4
with mistral-8k
.
from gai.gen import Gaigen
gen = Gaigen.GetInstance().load('mistral-8k')
response = gen.create(messages=[{'role':'USER','content':'Tell me a one paragraph short story.'},{'role':'ASSISTANT','content':''}])
print(response)
Examples
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