Open Source LLM toolkit to build trustworthy LLM applications. TigerArmor (AI safety), TigerRAG (embedding, RAG), TigerTune (fine-tuning)
π πFramework for Trustworthy LLM development: RAG + FineTune + AI Safety Measurementππ
Request Safety Evaluation for your LLMs & Chatbots at β TigerLab.ai β
Details can be found at: Metrics Defination
Find more demos at TigerLab.ai
https://github.com/tigerlab-ai/tiger/assets/4805931/e7c35117-269a-437d-99ab-10407a901cc5
https://github.com/tigerlab-ai/tiger/assets/148816206/4835b876-77e2-4483-9773-ea0b1d625f6c
BERT
for embedding, FAISS
for indexing, text-davinci-003
for generation.Llama2
and DistilBERT
.GPT2
. Top-k and Top-p Sampling
has been used for decoding. Perturbation-based augmenter coming soon!Before you begin setting up this project, please ensure you have completed the following tasks:
To get your OpenAI API token, follow these steps:
export OPENAI_API_KEY=<your API key>
Step 1. Clone the repo
git clone https://github.com/tigerlab-ai/tiger.git
Step 2. Install TigerRAG
cd tiger/TigerRAG
pip install .
Demo:
cd demos/movie_recs
python demo_ebr.py
python demo_rag.py
python demo_gar.py
Step 3. Install TigerTune
cd tiger/TigerTune
pip install --upgrade -e .
Demo:
python examples/classification_example.py
python examples/generation_example.py
CUDA GPU is needed to run generation_example.py. If you don't have a CUDA GPU connected, you can leverage our notebooks in notebooks/.
Step 4. Install TigerDA
cd tiger/TigerDA
pip install --upgrade -e .
Demo:
python examples/text_generation_augmenter_example.py
Please check out our Contribution Guide!
For bug fixes and feature requests, please file a Github issue.
In addition to the mentioned roadmap, we also maintain a backlog at https://github.com/tigerlab-ai/tiger/issues.
@misc{TigerLabAI_2023,
title={TigerLab AI Repository},
author={TigerLab AI},
howpublished={GitHub. Available online: https://github.com/tigerlab-ai/tiger},
year={2023}
}
A significant gap has arisen between general Large Language Models (LLMs) and the data stores that provide them with contextual information. Bridging this gap is a crucial step towards grounding AI systems in factual and safety domains, where their value lies not only in their generality but also in their specificity and uniqueness.
In pursuit of this goal, we are thrilled to introduce the Tiger toolkit (TigerRAG, TigerTune, TigerDA, TigerArmor) as an open-source resource for developers to create trustworthy AI models and language applications tailored to their specific needs.
We believe that our efforts will play a pivotal role in shaping the next phase of language modeling. This phase involves organizations customizing AI systems to align with their unique intellectual property and safety requirements, ushering in a new era of AI precision and safety.