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Query and summarize your documents or just chat with local private GPT LLMs using h2oGPT, an Apache V2 open-source project.
- Private offline database of any documents (PDFs, Excel, Word, Images, Code, Text, MarkDown, etc.)
- Persistent database (Chroma, Weaviate, or in-memory FAISS) using accurate embeddings (instructor-large, all-MiniLM-L6-v2, etc.)
- Efficient use of context using instruct-tuned LLMs (no need for LangChain's few-shot approach)
- Upload and View documents via UI (collaborative space or scratch space)
- UI or CLI with streaming of all models
- Bake-off UI mode against many models at same time
- Variety of models supported (Falcon, Vicuna, WizardLM including AutoGPTQ, 4-bit/8-bit, LORA)
- GPU support from HF models, and CPU support using LLaMa cpp and GPT4ALL
- Linux, Docker, MAC, and Windows support
- Inference Servers support (HF TGI server, vLLM, Gradio, OpenAI)
- OpenAI-compliant Python client API for client-server control
- Evaluate performance using reward models
Live h2oGPT Document Q/A Demo
- 🤗 Live h2oGPT Chat Demo 1
- 🤗 Live h2oGPT Chat Demo 2
h2oGPT CPU
h2oGPT CPU- Discord
- Apache V2 models (Falcon 40, etc.) at 🤗
- YouTube: 100% Offline ChatGPT Alternative?
- YouTube: Ultimate Open-Source LLM Showdown (6 Models Tested) - Surprising Results!
- YouTube: Blazing Fast Falcon 40b 🚀 Uncensored, Open- 8000 Source, Fully Hosted, Chat With Your Docs
- Technical Paper: https://arxiv.org/pdf/2306.08161.pdf
demo2.mp4
YouTube 4K version: https://www.youtube.com/watch?v=_iktbj4obAI
- Supported OS and Hardware
- Compare to PrivateGPT et al.
- Roadmap
- Getting Started
- Development
- Help
- Acknowledgements
- Why H2O.ai?
- Disclaimer
GPU mode requires CUDA support via torch and transformers. A 6.9B (or 12GB) model in 8-bit uses 8GB (or 13GB) of GPU memory. 8-bit precision, 4-bit precision, and AutoGPTQ can further reduce memory requirements down no more than about 6.5GB when asking a question about your documents (see low-memory mode).
CPU mode uses GPT4ALL and LLaMa.cpp, e.g. gpt4all-j, requiring about 14GB of system RAM in typical use.
GPU and CPU mode tested on variety of NVIDIA GPUs in Ubuntu 18-22, but any modern Linux variant should work. MACOS support tested on Macbook Pro running Monterey v12.3.1 using CPU mode, as well as MAC M1 using MPS.
- Integration of code and resulting LLMs with downstream applications and low/no-code platforms
- Complement h2oGPT chatbot with search and other APIs
- High-performance distributed training of larger models on trillion tokens
- Enhance the model's code completion, reasoning, and mathematical capabilities, ensure factual correctness, minimize hallucinations, and avoid repetitive output
- Add other tools like search
- Add agents for SQL and CSV question/answer
First one needs a Python 3.10 environment. For help installing a Python 3.10 environment, see Install Python 3.10 Environment. On newer Ubuntu systems and environment may be installed by just doing:
sudo apt-get install -y build-essential gcc python3.10-dev
virtualenv -p python3 h2ogpt
source h2ogpt/bin/activateor use conda:
conda create -n h2ogpt -y
conda activate h2ogpt
conda install python=3.10 -c conda-forge -yCheck your installation by doing:
python --version # should say 3.10.xx
pip --version # should say pip 23.x.y ... (python 3.10)On some systems, pip still refers back to the system one, then one can use python -m pip or pip3 instead of pip or try python3 instead of python.
For MACOS and Windows 10/11 please follow their instructions.
On Ubuntu, after Python 3.10 environment installed do:
git clone https://github.com/h2oai/h2ogpt.git
cd h2ogpt
# fix any bad env
pip uninstall -y pandoc pypandoc pypandoc-binary
# broad support, but no training-time or data creation dependencies
# CPU only:
pip install -r requirements.txt --extra-index https://download.pytorch.org/whl/cpu
# GPU only:
pip install -r requirements.txt --extra-index https://download.pytorch.org/whl/cu118Then run:
# Required for Doc Q/A: LangChain:
pip install -r reqs_optional/requirements_optional_langchain.txt
# Required for CPU: LLaMa/GPT4All:
pip install -r reqs_optional/requirements_optional_gpt4all.txt
# Optional: PyMuPDF/ArXiv:
pip install -r reqs_optional/requirements_optional_langchain.gpllike.txt
# Optional: Selenium/PlayWright:
pip install -r reqs_optional/requirements_optional_langchain.urls.txt
# Optional: support docx, pptx, ArXiv, etc. required by some python packages
sudo apt-get install -y libmagic-dev poppler-utils tesseract-ocr libreoffice
# Optional: for supporting unstructured package
python -m nltk.downloader all
# Optional: For AutoGPTQ support on x86_64 linux
pip uninstall -y auto-gptq ; CUDA_HOME=/usr/local/cuda-11.8 GITHUB_ACTIONS=true pip install auto-gptq --no-cache-dirSee AutoGPTQ for more details for AutoGPTQ and other GPU installation aspects.
Place all documents in user_path or upload in UI (Help with UI).
UI using GPU with at least 24GB with streaming:
python generate.py --base_model=h2oai/h2ogpt-oasst1-512-12b --load_8bit=True --score_model=None --langchain_mode='UserData' --user_path=user_pathUI using LLaMa.cpp model:
wget https://huggingface.co/TheBloke/WizardLM-7B-uncensored-GGML/resolve/main/WizardLM-7B-uncensored.ggmlv3.q8_0.bin
python generate.py --base_model='llama' --prompt_type=wizard2 --score_model=None --langchain_mode='UserData' --user_path=user_pathwhich works on CPU or GPU (assuming llama cpp python package compiled against CUDA or Metal).
If using OpenAI for the LLM is ok, but you want documents to be parsed and embedded locally, then do:
python generate.py --inference_server=openai_chat --base_model=gpt-3.5-turbo --score_model=Noneand perhaps you want better image caption performance and focus local GPU on that, then do:
python generate.py --inference_server=openai_chat --base_model=gpt-3.5-turbo --score_model=None --captions_model=Salesforce/blip2-flan-t5-xlAdd --share=True to make gradio server visible via sharable URL. If you see an error about protobuf, try:
pip install protobuf==3.20.0Once all files are downloaded, the CLI and UI can be run in offline mode, see offline mode.
- To create a development environment for training and generation, follow the installation instructions.
- To fine-tune any LLM models on your data, follow the fine-tuning instructions.
- To create a container for deployment, follow the Docker instructions.
- To run h2oGPT tests, run
pip install requirements-parser ; pytest -s -v tests client/tests
-
For advanced fine-tuning, also check out H2O LLM Studio
-
Flash attention support, see Flash Attention
-
Docker for inference
-
More Links, context, competitors, models, datasets
- Some training code was based upon March 24 version of Alpaca-LoRA.
- Used high-quality created data by OpenAssistant.
- Used base models by EleutherAI.
- Used OIG data created by LAION.
Our Makers at H2O.ai have built several world-class Machine Learning, Deep Learning and AI platforms:
- #1 open-source machine learning platform for the enterprise H2O-3
- The world's best AutoML (Automatic Machine Learning) with H2O Driverless AI
- No-Code Deep Learning with H2O Hydrogen Torch
- Document Processing with Deep Learning in Document AI
We also built platforms for deployment and monitoring, and for data wrangling and governance:
- H2O MLOps to deploy and monitor models at scale
- H2O Feature Store in collaboration with AT&T
- Open-source Low-Code AI App Development Frameworks Wave and Nitro
- Open-source Python datatable (the engine for H2O Driverless AI feature engineering)
Many of our customers are creating models and deploying them enterprise-wide and at scale in the H2O AI Cloud:
- Multi-Cloud or on Premises
- Managed Cloud (SaaS)
- Hybrid Cloud
- AI Appstore
We are proud to have over 25 (of the world's 280) Kaggle Grandmasters call H2O home, including three Kaggle Grandmasters who have made it to world #1.
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