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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)
- Parallel summarization reaching 80 tokens/second output 13B LLaMa2
- Upload and View documents via UI (control multiple collaborative or scratch collections)
- UI or CLI with streaming of all models
- Bake-off UI mode against many models at same time
- Variety of models supported (LLaMa2, Falcon, Vicuna, WizardLM including AutoGPTQ, 4-bit/8-bit, LORA)
- GPU support from HF and LLaMa.cpp GGML models, and CPU support using HF, LLaMa.cpp, and GPT4ALL models
- Linux, Docker, MAC, and Windows support
- Inference Servers support (HF TGI server, vLLM, Gradio, ExLLaMa, OpenAI)
- OpenAI-compliant Python client API for client-server control
- Evaluate performance using reward models
To quickly try out h2oGPT with LLaMa2 7B Chat, create a fresh Python 3.10 environment and run:
git clone https://github.com/h2oai/h2ogpt.git
cd h2ogpt
pip install -r requirements.txt
pip install -r reqs_optional/requirements_optional_langchain.txt
pip install -r reqs_optional/requirements_optional_gpt4all.txt
# if don't have wget, copy the below link to browser and download and place file into h2ogpt folder
wget https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/resolve/main/llama-2-7b-chat.ggmlv3.q8_0.bin
python generate.py --base_model='llama' --prompt_type=llama2then go to your browser by visiting http://127.0.0.1:7860 or http://localhost:7860.
For full instructions visit:
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-Source, Fully Hosted, Chat With Your Docs
- Technical Paper: https://arxiv.org/pdf/2306.08161.pdf
- Live Leaderboard for GPT-4 Elo Evaluation of Instruct/Chat models with h2o-LLM-eval.
- Advanced fine-tuning with H2O LLM Studio
demo2.mp4
YouTube 4K version: https://www.youtube.com/watch?v=_iktbj4obAI
- Supported OS and Hardware
- Getting Started
- LangChain Document Support
- Compare to PrivateGPT et al.
- Roadmap
- 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
- 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:
wget https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/resolve/main/llama-2-7b-chat.ggmlv3.q8_0.bin pip install requirements-parser pytest -s -v tests client/tests
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Flash attention support, see Flash Attention
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Docker for inference
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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.
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.